algorithmic trading

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pages: 224 words: 13,238

Electronic and Algorithmic Trading Technology: The Complete Guide by Kendall Kim

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algorithmic trading, automated trading system, backtesting, commoditize, computerized trading, corporate governance, Credit Default Swap, diversification, en.wikipedia.org, family office, financial innovation, fixed income, index arbitrage, index fund, interest rate swap, linked data, market fragmentation, money market fund, natural language processing, quantitative trading / quantitative finance, random walk, risk tolerance, risk-adjusted returns, short selling, statistical arbitrage, Steven Levy, transaction costs, yield curve

The Aite Group estimates that at the end of 2004, approximately 25% of total equities trading volume was driven by algorithmic trading (see Exhibit 3.9). Within this 25%, the sell side was composed of 13% followed by hedge fund volume, which stood at 10% of the total. Algorithmic trading volume initiated by traditional money managers was less than 3%. The popular use of algorithmic trading by hedge funds can also be attributable to the explosive growth in hedge funds within the last 15 years (see Exhibit 3.10). IT Spending in Algorithmic Trading Algorithmic trading services will continue to rise. IT spending will also rise. At the end of 2004, $200 million USD was spent on different IT components that make up algorithmic trading services, according to the Aite Group. Order Management Systems accounted for over 60% of that The Growth of Program and Algorithmic Trading 37 Percentage of Equities Trading Volume Driven by Algorithmic Trading 45% 40% 35% 30% Hedge Funds CAGR=7.2% 25% 20% Traditional Buy-side CAGR = 49.8% 15% 10% Sell-side CAGR = 4.2% 5% 0% 2004 2005 2006 Sell-side 2007 Traditional Buy-side 2008 Hedge Funds Exhibit 3.9 Percentage of equities trading volume driven by algorithmic trading.

These are used for statistical arbitrage, the practice of monitoring and comparing share prices to identify patterns that can be exploited to make a profit. Some exchanges now regulate the use of electronic and algorithmic trading, preventing their systems from being overloaded or to avoid repeating the crash of 1987. On July 7, 2005, the London Stock Exchange asked for algorithmic trading to be suspended after the London bombings. We are still in the infancy of algorithmic trading. Its impact on the corporate world is still uncertain. Algorithmic trading is now predominantly used to trade large capitalization companies, by making it easier to buy and sell large blocks of stock. It is a less well-suited means to trade small-cap or illiquid securities. The growing use of algorithmic trading could potentially 14 Electronic and Algorithmic Trading Technology lead brokers to further ignore the small-cap universe. This would result in an even further hit on smaller companies struggling to make markets to the public despite diminished stock research coverage and increased regulatory costs.

The last step is transaction cost analysis, which looks at the trading model and the execution to see how well the trading process worked.1 The basic building blocks of algorithmic trading are designed to capture real-time trading opportunities, 1 ‘‘Algorithmic Trading: 4 Perspectives,’’ Futures Industry, July–August 2005, http://www. futuresindustry.org/fimagazi-1929.asp?a¼1052&iss¼154. 163 164 Electronic and Algorithmic Trading Technology identifying tiny market inefficiencies relating to various factors such as price, volume, liquidity, benchmarks, and so on. Exhibit 15.1 illustates the elements of algorithmic trading. According to the Aite Group, the demand for algorithmic trading services continues to increase. At the end of 2004, over US $200 million was spent on different IT components that make up algorithmic trading services. IT spending on Order Management Systems (OMSs) accounted for over 60% of total spending in 2004.


pages: 354 words: 26,550

High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems by Irene Aldridge

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algorithmic trading, asset allocation, asset-backed security, automated trading system, backtesting, Black Swan, Brownian motion, business process, capital asset pricing model, centralized clearinghouse, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, computerized trading, diversification, equity premium, fault tolerance, financial intermediation, fixed income, high net worth, implied volatility, index arbitrage, information asymmetry, interest rate swap, inventory management, law of one price, Long Term Capital Management, Louis Bachelier, margin call, market friction, market microstructure, martingale, Myron Scholes, New Journalism, p-value, paper trading, performance metric, profit motive, purchasing power parity, quantitative trading / quantitative finance, random walk, Renaissance Technologies, risk tolerance, risk-adjusted returns, risk/return, Sharpe ratio, short selling, Small Order Execution System, statistical arbitrage, statistical model, stochastic process, stochastic volatility, systematic trading, trade route, transaction costs, value at risk, yield curve, zero-sum game

To ensure optimal execution of systematic trading, algorithms were designed to mimic established execution strategies of traditional traders. To this day, the term “algorithmic trading” usually refers to the systematic execution process—that is, the optimization of buy-and-sell decisions once these buy-and-sell decisions were made by another part of the systematic trading process or by a human portfolio manager. Algorithmic trading may determine how to process an order given current market conditions: whether to execute the order aggressively (on a price close to the market price) or passively (on a limit price far removed from the current market price), in one trade or split into several smaller “packets.” As mentioned previously, algorithmic trading does not usually make portfolio allocation decisions; the decisions about when to buy or sell which securities are assumed to be exogenous. 16 HIGH-FREQUENCY TRADING High-frequency trading became a trading methodology defined as quantitative analysis embedded in computer systems processing data and making trading decisions at high speeds and keeping no positions overnight.

., 207, 274, 278 Fill and kill (FAK) orders, 69 331 Index FINalternatives survey, 21 Financial Accounting Standard (FAS) 133, 263 Financial Information eXchange (FIX) protocol, 31, 239–242 Financial markets, suitable for high-frequency trading, 37–47 fixed-income markets, 40–43 foreign exchange markets, 43–46 liquidity requirements, 37–38 technological innovation and evolution of, 7–13 Finnerty, Joseph E., 183 Fisher, Lawrence, 174 Fixed-income markets, 40–43 algorithmic trading and, 19 event arbitrage, 181–183 FIX protocol, 31, 239–242 Flannery, M.J., 181 Fleming, Michael J., 182 Forecasting methodologies, event arbitrage, 168–173 Foreign currency exchange, 43–46 algorithmic trading and, 19 event arbitrage, 175–178 fundamental analysis and, 14 liquidity and, 38 statistical arbitrage, 189–191 transparent costs, 287 Foster, F., 158 Foucault, T., 66–67, 68, 122–123, 139, 142, 163, 274 Frankfurter, G.M., 209 Franklin, Benjamin, 288 Fransolet, L., 59 French, Kenneth R., 194–195 Frenkel, Jacob, 167 Froot, K., 87 Fuller, W.A., 98 Fundamental analysis, 14–15, 23 Fung, W., 57, 58 Futures: algorithmic trading, 19 commodity markets, 46–47 event arbitrage, 183 fixed-income markets, 40–42 foreign exchange markets, 43–46 liquidity, 38 statistical arbitrage, 197–198 Galai, D., 130 Gambler’s Ruin Problem, 135–137, 268 Garlappi, L., 210 Garman, M.B., 107, 135–137 Gatev, Evan, 188 Generalized autoregressive conditional heteroscedasticity (GARCH) process, 106–107, 123 George, T., 147 Getmansky, M., 59 Gini curve, 222, 228–229 Glantz, Morton, 284–285, 292–293, 298, 299 Globex, 9 Glosten, Lawrence R., 131, 147, 151, 156 Goal-setting, risk management and, 252–253 Goettler, R., 67, 163 Goetzmann, William N., 59, 188 Goldman Sachs, 25 Good for the day (GFD) orders, 68 Good for the extended day (GFE) orders, 68 Goodhart, Charles, 8, 89, 168 Good till canceled (GTC) orders, 68 Good till date (GTD) orders, 68 Good till time (GTT) orders, 68 Gorton, G., 184 Government regulation, 26 Graham, Benjamin, 14 Granger, C., 89, 101, 109 Granger causality specification, 197 Grauer, R.R., 209 Gravitational pull, of quotes, 130 Green, T.C., 182 Gregoriou, G.N., 56 Grilli, Vittorio, 167 Gueyie, J.

As computer technology develops further and drops in price, highfrequency systems are bound to take on an even more active role. Special care should be taken, however, to distinguish high-frequency trading from electronic trading, algorithmic trading, and systematic trading. Figure 2.5 illustrates a schematic difference between high-frequency, systematic, and traditional long-term investing styles. Electronic trading refers to the ability to transmit the orders electronically as opposed to telephone, mail, or in person. Since most orders in today’s financial markets are transmitted via computer networks, the term electronic trading is rapidly becoming obsolete. Algorithmic trading is more complex than electronic trading and can refer to a variety of algorithms spanning order-execution processes as well as high-frequency portfolio allocation decisions.

Quantitative Trading: How to Build Your Own Algorithmic Trading Business by Ernie Chan

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algorithmic trading, asset allocation, automated trading system, backtesting, Black Swan, Brownian motion, business continuity plan, compound rate of return, Edward Thorp, Elliott wave, endowment effect, fixed income, general-purpose programming language, index fund, John Markoff, Long Term Capital Management, loss aversion, p-value, paper trading, price discovery process, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Ray Kurzweil, Renaissance Technologies, risk-adjusted returns, Sharpe ratio, short selling, statistical arbitrage, statistical model, survivorship bias, systematic trading, transaction costs

Ernie successfully distills a large amount of detailed and difficult subject matter down to a very clear and comprehensive resource for novice and pro alike.” J AC K E T D ES I G N : PAU L M c C A RT H Y J AC K E T A RT: © D O N R E LY E A —STEVE HALPERN, founder, HCC Capital, LLC How to Build Your Own Algorithmic Trading Business trader and consultant who advises clients on how Quantitative Trading or sophisticated theories. Instead, he highlights the Wiley Trading B y some estimates, quantitative (or algorithmic) trading now accounts for over one-third of trading volume in the United States. While institutional traders continue to implement this highly effective approach, many independent traders—with Quantitative Trading limited resources and less computing power—have wondered if they can still challenge powerful industry professionals at their own game?

Ernie successfully distills a large amount of detailed and difficult subject matter down to a very clear and comprehensive resource for novice and pro alike.” J AC K E T D ES I G N : PAU L M c C A RT H Y J AC K E T A RT: © D O N R E LY E A —STEVE HALPERN, founder, HCC Capital, LLC How to Build Your Own Algorithmic Trading Business trader and consultant who advises clients on how Quantitative Trading or sophisticated theories. Instead, he highlights the Wiley Trading B y some estimates, quantitative (or algorithmic) trading now accounts for over one-third of trading volume in the United States. While institutional traders continue to implement this highly effective approach, many independent traders—with Quantitative Trading limited resources and less computing power—have wondered if they can still challenge powerful industry professionals at their own game?

latest news, ideas, and trends in quantitative —PETER BORISH, Chairman and CEO, Computer Trading Corporation trading, you’re welcome to visit Dr. Chan’s blog, epchan.blogspot.com, as well as his premium “Dr. Ernest Chan provides an optimal framework for strategy development, back-testing, risk management, content Web site, epchan.com/subscriptions, programming knowledge, and real-time system implementation to develop and run an algorithmic trading which you’ll have free access to with purchase of business step by step in Quantitative Trading.” this book. —YASER ANWAR, trader As an independent trader, you’re free from the con- “Quantitative systematic trading is a challenging field that has always been shrouded in mystery, straints found in today’s institutional environment— seemingly too difficult to master by all but an elite few.


pages: 257 words: 13,443

Statistical Arbitrage: Algorithmic Trading Insights and Techniques by Andrew Pole

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algorithmic trading, Benoit Mandelbrot, Chance favours the prepared mind, constrained optimization, Dava Sobel, George Santayana, Long Term Capital Management, Louis Pasteur, mandelbrot fractal, market clearing, market fundamentalism, merger arbitrage, pattern recognition, price discrimination, profit maximization, quantitative trading / quantitative finance, risk tolerance, Sharpe ratio, statistical arbitrage, statistical model, stochastic volatility, systematic trading, transaction costs

Chapter 11 describes the phoenix of statistical arbitrage, rising out of the ashes of the fire created and sustained by the technological developments in algorithmic trading. New, sustained patterns of stock price dynamics are emerging. The story of statistical arbitrage has returned to a new beginning. Will this fledgling fly? The renaissance predicted in Chapter 11, drafted in 2005, is already coming to pass. Since at least early 2006 there has been a resurgence of performance from those practitioners who persisted through the extremely challenging dynamic changes of 2003–2005. Interestingly, while there are new systematic patterns in the movements of relative equity prices, some old patterns have also regained potency. Adoption of algorithmic trading is accelerating, with tools now offered by more than 20 vendors. In another technology driven development, beginning with Goldman Sachs in late 2006, at least two offerings of general hedge fund replication by algorithmic means have been brought to market.

New York: Penguin Books, 1996. 223 Index Accuracy issues, structural models, 59–61 Adaptive model, 172 Adjusted prices, 13n1 Advanced Theory of Statistics, The (Kendall, Stuart, and Ord), 63 Algorithmic trading (Black Boxes), 1, 3, 183–190 dynamic updating, 188 market deflation and, 189–190 modeling transaction volume and market impact, 185–188 Alliance Capital, 165 Altvest, 161 American Airlines (AMR)–Continental Airlines (CAL) spread, 2, 10–16, 37–39, 40–45 Antilochus, 163 Applied Multivariate Analysis (Press), 65 ‘‘Arbed away’’ claim, 159–160 ARCH (autoregressive conditional heteroscedastic) models, 75–76 ARFIMA (autoregressive fractionally integrated moving average), 49 ARIMA (autoregressive integrated moving average), 48–49 Arnold, V. I., 205–206n3 Asian bird flu, 175 Autocorrelation, 129–130 Automatic trading, see Algorithmic trading (Black Boxes) Autoregression and cointegration, 47–49 Autoregressive conditional heteroscedastic (ARCH) models, 75–76 Autoregressive fractionally integrated moving average (ARFIMA), 49 Autoregressive integrated moving average (ARIMA), 48–49 Avarice, catastrophe process and, 205–209 Ball bearing analogy, 211–212 Bamberger, Gerry, 1n1 Bank of America, 165, 185, 189 Barra model, 21 Barron’s, 161 Bid-ask spread, declining, 156–159 Binomial distribution, 88–89 Black Boxes, 1, 3, 183–190 dynamic updating, 188 market deflation and, 189–190 modeling transaction volume and market impact, 185–188 Block trading activity, 173 Bollinger bands, 17, 26 Bond futures, 85–87 Box, G.E.P., 1, 9, 48, 191 Brahe, Tyco, 6n3 British Petroleum (BP)–Royal Dutch Shell (RD) spread, 46–47 Calibration, 12–16, 13n1, 32–36 Carroll, Lewis, 67 Catastrophe process, 191–221, 205n3 contrasted to popcorn process, 194–198 Cuscore statistics and, 200–205, 211–221 cusp, 206, 208 forecasts with, 198–200 move, 194–200 normal factor, 206, 207 risk management and, 209–211 splitting factor, 206, 207 surface, 205–206, 207 theoretical interpretation of, 205–209 trend change identification and, 200–205 Catastrophe Theory (Arnold), 205–206n3 Cauchy distribution, 74, 126 Change point identification, 200 Chi-square distribution, 96 Classical time series models, 47–52 autoregression and cointegration, 47–49 dynamic linear model, 49–50 fractal analysis, 52 pattern finding techniques, 51–52 volatility modeling, 50–51 225 226 Cointegration and autoregression, 47–49 Competition, return decline and, 160–162 Conditional distribution, 118–119, 121–122 Conditional probability, 69 Consumer surplus, 159, 162 Continental Airlines (CAL)–American Airlines (AMR) spread, 2, 10–16, 37–39, 40–45 Continuity, 114–117 Correlation: first-order serial, 77–82 during loss episodes, 151–154 Correlation filters, 21–22 Correlation searches, with software, 26 Covariance, 103 Credit crisis of 1998, risk and, 145–148 Credit Suisse First Boston, 26, 38–39, 185, 189 Cuscore statistics, 200–205, 211–221 Daimler Chrysler, 24 Debt rating, risk and, 145–148 Decimalization, 156–159 Defactored returns, 55–57, 65–66 D.E.

Statistical Arbitrage Algorithmic Trading Insights and Techniques ANDREW POLE John Wiley & Sons, Inc. Statistical Arbitrage Founded in 1807, John Wiley & Sons is the oldest independent publishing company in the United States. With offices in North America, Europe, Australia, and Asia. Wiley is globally committed to developing and marketing print and electronic products and services for our customers’ professional and personal knowledge and understanding. The Wiley Finance series contains books written specifically for finance and investment professionals as well as sophisticated individual investors and their financial advisors. Book topics range from portfolio management to e-commerce, risk management, financial engineering, valuation, and financial instrument analysis, as well as much more.


pages: 317 words: 84,400

Automate This: How Algorithms Came to Rule Our World by Christopher Steiner

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23andMe, Ada Lovelace, airport security, Al Roth, algorithmic trading, backtesting, big-box store, Black-Scholes formula, call centre, cloud computing, collateralized debt obligation, commoditize, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, Donald Trump, Douglas Hofstadter, dumpster diving, Flash crash, Gödel, Escher, Bach, High speed trading, Howard Rheingold, index fund, Isaac Newton, John Markoff, John Maynard Keynes: technological unemployment, knowledge economy, late fees, Marc Andreessen, Mark Zuckerberg, market bubble, medical residency, money market fund, Myron Scholes, Narrative Science, PageRank, pattern recognition, Paul Graham, Pierre-Simon Laplace, prediction markets, quantitative hedge fund, Renaissance Technologies, ride hailing / ride sharing, risk tolerance, Sergey Aleynikov, side project, Silicon Valley, Skype, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, transaction costs, upwardly mobile, Watson beat the top human players on Jeopardy!, Y Combinator

TO TRADE IS TO DIG The unlikely tale begins in 2008, when a New York hedge fund asked Daniel Spivey to develop an algorithmic trading strategy that searched out tiny price discrepancies between index futures in Chicago and their underlying stocks and securities in New York. If the future cost more in Chicago than did all of its stocks in New York, the strategy would sell futures in Chicago and buy stocks in New York. When prices in Chicago and New York realigned—and they always realigned—Spivey’s program would dump its positions and book its profit. Spivey’s strategy was no different than the arbitrage Thomas Peterffy did with similar stock indexes in disparate markets—except Spivey had to do it a lot faster. With the rise of algorithmic trading, any kind of low-risk arbitrage bet draws a crowd, which is why such strategies demand speed.

“Where are your traders?” “This is it, it’s all right here,” Peterffy said, pointing at an IBM computer squatting next to the sole Nasdaq terminal in the room. “We do it all from this.” A tangle of wires ran between the Nasdaq machine and the IBM, which hosted code that dictated what, when, and how much to trade. The Nasdaq employee didn’t realize it, but he had walked in on the first fully automated algorithmic trading system in the world. Peterffy’s setup didn’t merely suggest what to trade, as other systems had in the past. It didn’t simply pump out trades that humans would later carry out. The computer, by way of a surreptitious hack into the trading terminal, made all of the decisions and executed all of the trades. No humans necessary. Its trading partners, though, were 100 percent human—and they were getting drubbed.

Few, if any, traders were taking advantage of technology the way Peterffy did. Some trading houses kept open phone lines between New York and Chicago so that clerks could bark prices back and forth and pounce on large pricing discrepancies. Peterffy’s automated system allowed his traders to harvest not only large mispricings but also smaller ones—and they almost always got to them before others. Peterffy had created the first algorithmic trading operation working from coast to coast. All trading activity from the handhelds was radioed to waiting terminals Peterffy had installed at each exchange. The computers there would then wire the data across the leased phone lines straight to Timber Hill’s offices in the World Trade Center, where it would be received by a large master algorithm called simply the Correlator, which ran phalanxes of code to dissect markets and pinpoint their weaknesses, while dispatching Timber Hill traders in each city to hammer them.


pages: 318 words: 87,570

Broken Markets: How High Frequency Trading and Predatory Practices on Wall Street Are Destroying Investor Confidence and Your Portfolio by Sal Arnuk, Joseph Saluzzi

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algorithmic trading, automated trading system, Bernie Madoff, buttonwood tree, commoditize, computerized trading, corporate governance, cuban missile crisis, financial innovation, Flash crash, Gordon Gekko, High speed trading, latency arbitrage, locking in a profit, Mark Zuckerberg, market fragmentation, Ponzi scheme, price discovery process, price mechanism, price stability, Sergey Aleynikov, Sharpe ratio, short selling, Small Order Execution System, statistical arbitrage, transaction costs, two-sided market, zero-sum game

Prior to founding Themis in 2002, he was with Instinet Corporation, where he headed the team responsible for equity sales and trading for institutional money managers, for more than 10 years. His opinions are sought by leaders, regulators, market participants, and the media and are presented via white papers and Themis’ widely read blog. He is a frequent speaker at industry conferences, such as Trader Forum, Waters, National Organization of Investment Professionals (NOIP), and Fusion IQ’s Big Picture, on issues involving market access, algorithmic trading, and other sell- and buy-side concerns. He also provides expert commentary for media outlets such as the Associated Press, BBC Radio, Bloomberg TV and Radio, BNN, CNBC, Fox Business, NPR, Barron’s, The New York Times, The Wall Street Journal, USA Today, Time, Los Angeles Times, Bloomberg News, Pensions & Investments, and Advanced Trading. Arnuk earned an MBA in finance from New York University’s Stern School of Business and a Bachelor’s degree in finance from SUNY Binghamton University.

The primary purpose of the stock exchanges has devolved to catering to a class of highly profitable market participants called high frequency traders, or HFTs, who are interested only in hyper-short term trading, investors be damned. The stock exchanges give these HFTs perks and advantages to help them be as profitable as possible, even if doing so adversely affects you, the investors, because HFT firms are the exchanges’ biggest customers. These HFTs use high-powered computers to automatically and algorithmically trade in and out of securities in speeds measured in microseconds (millionths of a second). Although there are few HFTs relative to the number of investors in the marketplace, the following is generally estimated in the industry: • HFTs account for 50–75% of the volume traded on the exchanges each day and a substantial portion of the stock exchanges’ profits. • While smaller HFTs churn hundreds of millions of shares per day, a few of the larger HFTs each account for more than 10% of any given day’s trading volume

Because their algorithmic models price securities with such an emphasis on nearby prices and robust uninterrupted pricing data flow, when that data displays discrepancies, they withdraw their “liquidity provision” and shut down. The Joint CFTC-SEC Advisory Committee, set up to study and report findings on the events of May 6, 2010, summed it nicely: “In the present environment, where high frequency and algorithmic trading predominate and where exchange competition has essentially eliminated rule-based market maker obligations...even in the absence of extraordinary market events, limit order books can quickly empty and prices can crash.”1 Another concern is the market’s instrument makeup. In 2010, Exchange Traded Products (ETP), including its biggest category, exchange traded funds, or ETFs, reached an asset under management (AUM) level of $1.3 trillion.2 Only ten years prior, ETP AUM totaled a mere $66 billion.3 This represents nearly a 19-fold increase.


pages: 402 words: 110,972

Nerds on Wall Street: Math, Machines and Wired Markets by David J. Leinweber

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AI winter, algorithmic trading, asset allocation, banking crisis, barriers to entry, Big bang: deregulation of the City of London, butterfly effect, buttonwood tree, buy low sell high, capital asset pricing model, citizen journalism, collateralized debt obligation, corporate governance, Craig Reynolds: boids flock, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Danny Hillis, demand response, disintermediation, distributed generation, diversification, diversified portfolio, Emanuel Derman, en.wikipedia.org, experimental economics, financial innovation, fixed income, Gordon Gekko, implied volatility, index arbitrage, index fund, information retrieval, intangible asset, Internet Archive, John Nash: game theory, Kenneth Arrow, Khan Academy, load shedding, Long Term Capital Management, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, market fragmentation, market microstructure, Mars Rover, Metcalfe’s law, moral hazard, mutually assured destruction, Myron Scholes, natural language processing, negative equity, Network effects, optical character recognition, paper trading, passive investing, pez dispenser, phenotype, prediction markets, quantitative hedge fund, quantitative trading / quantitative finance, QWERTY keyboard, RAND corporation, random walk, Ray Kurzweil, Renaissance Technologies, Richard Stallman, risk tolerance, risk-adjusted returns, risk/return, Robert Metcalfe, Ronald Reagan, Rubik’s Cube, semantic web, Sharpe ratio, short selling, Silicon Valley, Small Order Execution System, smart grid, smart meter, social web, South Sea Bubble, statistical arbitrage, statistical model, Steve Jobs, Steven Levy, Tacoma Narrows Bridge, the scientific method, The Wisdom of Crowds, time value of money, too big to fail, transaction costs, Turing machine, Upton Sinclair, value at risk, Vernor Vinge, yield curve, Yogi Berra, your tax dollars at work

It and the automated NASDAQ systems accommodated ever larger orders. Orders exceeding the size limits for automation were routed to specialists and market makers. Algorithm Wars 67 This was algorithmic trading without algorithms, an early form of direct market access. The first user interfaces were for one stock at a time, electronic versions of simple, single paper buy and sell slips. This became tedious, and soon execution capabilities for a list of names followed. Everyone was happy to be able to produce and screen these lists using their new Lotus 1-2-3 spreadsheets, which totaled everything up nicely to avoid costly errors. We were only a step away from algorithmic trading. Programmers at the order origination end grew more capable and confident in their abilities to generate and monitor an ever larger number of small orders.

From Order Pad to Algos The first direct-access tools from the sell side were single-stock electronic order pads, followed shortly by lists. The next step was breaking those orders down into pieces small enough to execute electronically, and spreading them out in time. Innovative systems like ITG’s QuantEx, discussed in Chapter 7, allowed traders without large software staffs to use and define analytics and rules to control electronic trading. This began to look like what we consider to be algorithmic trading today. The big news in algorithmic trading in the late 1980s was that you could do it at all. The first algo strategies were based on simple rules, like “send this order out in 10 equal waves, spaced equally in time from open to close.” But these were predictable and easy to game by manipulating the price on a thin name with a limit order placed just before the arrival of the next wave, getting bagged in classic “Spy vs.

Its first investment was in Integrated Analytics Corporation (IAC), which Dale Prouty and I founded to deliver the specialized and less filling expert system environment needed for financial applications. Years later we published a paper, “A Little Artificial Intelligence Goes a Long Way on Wall Street” on the details; an updated version appears in Chapter 7. We called all this “electronic order working” back then, since we didn’t know it was algorithmic trading. How Do You Keep the Rats from Eating the Wires? Shortly after we started the company, a colleague from the AI group at Arthur D. Little, the venerable Cambridge consulting firm, asked me to fill Intr oduction xxxi in for him at the last minute at a technology session at a finance conference being held in Los Angeles; his dog was sick. The topic was a generic “AI on Wall Street,” the last one in a catchall session.


pages: 327 words: 91,351

Traders at Work: How the World's Most Successful Traders Make Their Living in the Markets by Tim Bourquin, Nicholas Mango

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algorithmic trading, automated trading system, backtesting, commodity trading advisor, Credit Default Swap, Elliott wave, fixed income, Long Term Capital Management, paper trading, pattern recognition, prediction markets, risk tolerance, Small Order Execution System, statistical arbitrage, The Wisdom of Crowds, transaction costs, zero-sum game

Markets have a way of taking some niches away, but good traders find a way to modify their niche and morph into something different. For example, those who traded successfully in the 1970s and 1980s but continued on with exactly the same strategy couldn’t stay at the top of the game and inevitably encountered frustration. Markets change, and traders have to identify that and be flexible whenever it happens. That has been especially true in recent years, with the increase in high-frequency and algorithmic trading, plus twenty-four-hour markets, electronic exchanges, and the quant world. Even though the nature of the markets has changed, however, I would say for the most part that I’m still in the same niche now that I was in 1980. But there are subtleties within that niche—money management, most notably—that I have changed over time in response to changes in the markets. Bourquin: Let’s talk about that niche.

Is there validity to that, or no? Lund: That’s a really good question. I think you are right. I think a lot of people are using it as an excuse for why they’re not profitable. There’s no doubt that high-frequency trading is out there. You see it all the time. You just have to learn to adapt to it. If you are going to trade patterns, you have to understand that high-frequency trading and algorithmic trading are going to distort those patterns to a point where you traditionally would have thought they were broken. Maybe you will see a cup with a handle, or maybe you will see a double-bottom, or maybe you will see a semi-triangle, and price will drop out of that pattern. In the past, you would say, “Okay, that’s it. The pattern is broken.” But then, miraculously, price will come back into that pattern and continue the way that you wanted.

How do you determine those levels? Toma: Well, my risk manager mentality means I’m really focusing on the risk, so I’m not too concerned about my profit target at first. I want to focus on how much I can afford to lose and, more importantly, how I’ll know if I’m wrong about the trade. In years past, I would set arbitrary stops, like six ticks or two points in the ES. However, because high volatility and algorithmic trading are so prevalent, that really didn’t tell me that I was wrong. It only indicated that my maximum risk had been hit, so I needed to get out. Here’s one thing that may be contrary to public opinion: A lot of traders want to have two-to-one, three-to-one, or five-to-one risk/reward ratios. Well, that’s great, but if I have a setup that gives me 70 percent, I don’t mind taking one-to-one on that.


pages: 348 words: 39,850

Data Scientists at Work by Sebastian Gutierrez

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Albert Einstein, algorithmic trading, Bayesian statistics, bioinformatics, bitcoin, business intelligence, chief data officer, clean water, cloud computing, commoditize, computer vision, continuous integration, correlation does not imply causation, creative destruction, crowdsourcing, data is the new oil, DevOps, domain-specific language, Donald Knuth, follow your passion, full text search, informal economy, information retrieval, Infrastructure as a Service, Intergovernmental Panel on Climate Change (IPCC), inventory management, iterative process, lifelogging, linked data, Mark Zuckerberg, microbiome, Moneyball by Michael Lewis explains big data, move fast and break things, move fast and break things, natural language processing, Network effects, nuclear winter, optical character recognition, pattern recognition, Paul Graham, personalized medicine, Peter Thiel, pre–internet, quantitative hedge fund, quantitative trading / quantitative finance, recommendation engine, Renaissance Technologies, Richard Feynman, Richard Feynman, self-driving car, side project, Silicon Valley, Skype, software as a service, speech recognition, statistical model, Steve Jobs, stochastic process, technology bubble, text mining, the scientific method, web application

www.it-ebooks.info 180 Chapter 9 | Jonathan Lenaghan, PlaceIQ Sebastian Gutierrez: Tell me about your journey to becoming a data scientist at PlaceIQ. Jonathan Lenaghan: Prior to joining PlaceIQ as a data scientist in March of 2012, I worked in the financial services industry doing algorithmic trading. Before that, I worked for eight years in academic physics. So I’ve always worked with a great deal of data—although, compared to my algorithmic trading work, my physics work was a bit more weighted toward the analytical than the computational. Gutierrez: Algorithmic trading sounds like an interesting job with interesting data sets. What drove the transition to PlaceIQ? Lenaghan: I liked the style of work I was doing in the financial services industry, solving quantitative problems, but it began to feel like I was solving the same problem year after year with slight variations.

There are mathematical frameworks around these complicated instruments, which have theoretical, analytical, and computational aspects. The second field is high-frequency algorithmic trading, in which you have very simple financial instruments, such as equities, which are traded in very complicated ways. This field is much more computational and algorithmdriven. There’s no analytical structure around this type of trading; it’s essentially driven by experimental data. I originally wanted to go into quantitative finance to work in the first field dominated by analysis of complicated derivative products, but I ended up in the second field dominated by algorithmic trading of simple financial instruments. Gutierrez: What made you change your plan from doing purely analytical work to data-driven work? Lenaghan: I quickly learned that the empirical style of work in algorithmic trading suited me very well. I liked the experimental design—figuring out what works, what doesn’t work, and how not to trick yourself with data.

Another challenge is that people under pressure to find patterns are prone to fall into the common human fallacies of overfitting models with insufficient data and overreading correlation as causation. Gutierrez: How do the data challenges you faced in the algorithmic trading world compare to the data challenges you face at PlaceIQ? Lenaghan: The initial data challenge when I came to PlaceIQ was that geospatial data was a data type that I had never worked with. The second challenge was that the data volume was scaled up by a couple of orders of magnitude. The volume of data in the algorithmic trading I was doing was quite large—say, a terabyte a year. But the PlaceIQ environment generates hundreds and hundreds of terabytes a year. Making these adjustments were exciting challenges www.it-ebooks.info 183 184 Chapter 9 | Jonathan Lenaghan, PlaceIQ for me.


pages: 443 words: 51,804

Handbook of Modeling High-Frequency Data in Finance by Frederi G. Viens, Maria C. Mariani, Ionut Florescu

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algorithmic trading, asset allocation, automated trading system, backtesting, Black-Scholes formula, Brownian motion, business process, continuous integration, corporate governance, discrete time, distributed generation, fixed income, Flash crash, housing crisis, implied volatility, incomplete markets, linear programming, mandelbrot fractal, market friction, market microstructure, martingale, Menlo Park, p-value, pattern recognition, performance metric, principal–agent problem, random walk, risk tolerance, risk/return, short selling, statistical model, stochastic process, stochastic volatility, transaction costs, value at risk, volatility smile, Wiener process

Lancette, Kiseop Lee, and Yanhui Mi 1.1 1.2 1.3 1.4 1.5 1.6 Introduction, 3 The Statistical Models, 6 Parametric Estimation Methods, 9 Finite-Sample Performance via Simulations, 14 Empirical Results, 18 Conclusion, 22 References, 24 2 A Study of Persistence of Price Movement using High Frequency Financial Data 27 Dragos Bozdog, Ionuţ Florescu, Khaldoun Khashanah, and Jim Wang 2.1 Introduction, 27 2.2 Methodology, 29 2.3 Results, 35 v vi Contents 2.4 Rare Events Distribution, 41 2.5 Conclusions, 44 References, 45 3 Using Boosting for Financial Analysis and Trading 47 Germán Creamer 3.1 3.2 3.3 3.4 3.5 Introduction, 47 Methods, 48 Performance Evaluation, 53 Earnings Prediction and Algorithmic Trading, 60 Final Comments and Conclusions, 66 References, 69 4 Impact of Correlation Fluctuations on Securitized structures 75 Eric Hillebrand, Ambar N. Sengupta, and Junyue Xu 4.1 Introduction, 75 4.2 Description of the Products and Models, 77 4.3 Impact of Dynamics of Default Correlation on Low-Frequency Tranches, 79 4.4 Impact of Dynamics of Default Correlation on High-Frequency Tranches, 87 4.5 Conclusion, 92 References, 94 5 Construction of Volatility Indices Using A Multinomial Tree Approximation Method Dragos Bozdog, Ionuţ Florescu, Khaldoun Khashanah, and Hongwei Qiu 5.1 5.2 5.3 5.4 Introduction, 97 New Methodology, 99 Results and Discussions, 101 Summary and Conclusion, 110 References, 115 97 vii Contents part Two Long Range Dependence Models 117 6 Long Correlations Applied to the Study of Memory Effects in High Frequency (TICK) Data, the Dow Jones Index, and International Indices 119 Ernest Barany and Maria Pia Beccar Varela 6.1 6.2 6.3 6.4 6.5 Introduction, 119 Methods Used for Data Analysis, 122 Data, 128 Results and Discussions, 132 Conclusion, 150 References, 160 7 Risk Forecasting with GARCH, Skewed t Distributions, and Multiple Timescales 163 Alec N.

This does correspond to the previously observed peak in trading activity (Fig. 2.9) at about the same time. We hypothesize that the peak in rare events may be caused by the activation of various trading strategies after the stabilization of the market following the opening. Recall that the histogram presents the rare events detection for ALL equity within a class. This may be evidence of algorithmic trading starting at about the same time, reaching about the same conclusion, placing similar limit orders, and therefore pulling the market in the same direction with relatively little volume. We do underline, however, that this does not destabilize the market. This much is evident from the ensuing pattern of rare events which follows the same trend as before the spike. Finally, we notice the presence of a significant number of rare events concentrated around noon for small- and mid-vol equity.

In this chapter, we review several applications of algorithmic modeling to planning and corporate performance evaluation and to forecast stock prices, cumulative abnormal return, and earnings surprises. The rest of the chapter is organized as follows: Section 3.2 introduces the main methods used in this chapter; Section 3.3 presents the application of boosting to performance evaluation and the generation of balanced scorecards (BSCs); Section 3.4 shows how boosting can be applied to forecast earnings surprise and to algorithmic trading; and Section 3.5 presents the conclusions and recommendations. 3.2 Methods In this section we introduce boosting and how it can be used to support the generation of BSCs. 3.2.1 BOOSTING Adaboost is a general discriminative learning algorithm invented by Freund and Schapire (1997). The basic idea of Adaboost is to repeatedly apply a simple learning algorithm, called the weak or base learner,1 to different weightings of the same training set.


pages: 368 words: 32,950

How the City Really Works: The Definitive Guide to Money and Investing in London's Square Mile by Alexander Davidson

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accounting loophole / creative accounting, algorithmic trading, asset allocation, asset-backed security, bank run, banking crisis, barriers to entry, Big bang: deregulation of the City of London, capital asset pricing model, central bank independence, corporate governance, Credit Default Swap, dematerialisation, discounted cash flows, diversified portfolio, double entry bookkeeping, Edward Lloyd's coffeehouse, Elliott wave, Exxon Valdez, forensic accounting, global reserve currency, high net worth, index fund, inflation targeting, intangible asset, interest rate derivative, interest rate swap, John Meriwether, London Interbank Offered Rate, Long Term Capital Management, margin call, market fundamentalism, Nick Leeson, North Sea oil, Northern Rock, pension reform, Piper Alpha, price stability, purchasing power parity, Real Time Gross Settlement, reserve currency, Right to Buy, shareholder value, short selling, The Wealth of Nations by Adam Smith, transaction costs, value at risk, yield curve, zero-coupon bond

This will affect the share price because the shares are held as security against the CFD trades, he says. For more about how CFDs work, see Chapter 9. Hedge funds in particular, but also investment banks and pension funds use algorithmic trading, which is computer-based trading applying mathematical models known as algorithms. It requires fewer staff to administer than traditional trading. The models used will generate both the size and timing of the trade based on a volume-weighted or time-weighted average price. Algorithmic trading can split a large trade into smaller ones to reduce market impact and cut trading costs. Concerns are sometimes raised that algorithmic trading can power a market rise or fall as programmed traders follow momentum triggered by buyers or sellers. Some firms run programmes known as sniffers that discover momentum in stocks, giving them a cue to jump onto the bandwagon.

Some firms run programmes known as sniffers that discover momentum in stocks, giving them a cue to jump onto the bandwagon. Algorithms follow many different trading strategies, which can even serve to counteract each other, as fans of algorithmic trading point out. 16 Share trading venues and exchanges Introduction In this chapter, we will look at the exchange trading facilities for UK equities in the competitive environment encouraged by the Markets in Financial Instruments Directive. Read this chapter with Chapter 15, which covers the London Stock Exchange. Overview As the second edition of this book went to press, there are eight recognised investment exchanges (RIEs) in London, some concerned with derivatives. We will focus here on exchanges that trade UK equities, where only the London Stock Exchange (LSE) (covered in Chapter 15) and PLUS Markets Group (see below) are currently authorised to operate primary as well as secondary markets. virt-x, a cross-border exchange for pan-European blue chip stocks, is authorised only to operate secondary markets.

The PLUS trading platform, based on a quote-driven system, claims to have substantial benefits in promoting liquidity in small and mid-cap stock trading, partly in terms of smoothing out price shifts caused by sudden volume surges and also in terms of the ability of market markers to price-improve in relation to specific orders. There is feedback from market participants that there has been a ‘drying up’ of liquidity in small-caps when unsuitable stocks were moved to SETSmm, according to PLUS. The LSE has denied that liquidity is affected. Order books such as SETS (provided by the LSE, and explained in Chapter 15) suit those market participants who prefer alternative methods of trading, such as algorithmic trades, and those who require direct market access to execute their business, according to Wynn-Evans. However, the PLUS platform suits those participants who prefer quote-driven trading, such as retail brokers and market makers, both of which play a valuable role in providing price formation and liquidity in those smaller stocks that trade less frequently. Market makers often quote one price on PLUS and another on SETS.


pages: 280 words: 73,420

Crapshoot Investing: How Tech-Savvy Traders and Clueless Regulators Turned the Stock Market Into a Casino by Jim McTague

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algorithmic trading, automated trading system, Bernie Madoff, Bernie Sanders, Bretton Woods, buttonwood tree, computerized trading, corporate raider, creative destruction, credit crunch, Credit Default Swap, financial innovation, fixed income, Flash crash, High speed trading, housing crisis, index arbitrage, locking in a profit, Long Term Capital Management, margin call, market bubble, market fragmentation, market fundamentalism, Myron Scholes, naked short selling, pattern recognition, Ponzi scheme, quantitative trading / quantitative finance, Renaissance Technologies, Ronald Reagan, Sergey Aleynikov, short selling, Small Order Execution System, statistical arbitrage, technology bubble, transaction costs, Vanguard fund, Y2K

I see nothing sinister or unfair about the advantages that come out of their investments and efforts.” A few exchanges went even further for HFT subscribers to their data feed and froze some customer orders for a few milliseconds to give them a first shot at taking them and earning a rebate for making the transaction. These were called flash orders. Dan Mathisson, managing director of advanced electronic systems at Credit Suisse, and a pioneer in algorithmic trading, said in his view, flash orders violated the spirit of regulation NMS and weakened the notion of a national market system.3 Levitt also was in favor of banning that practice. In effect, with the assistance of the exchanges, the high-frequency traders were driving around the markets in the equivalent of 1200-horsepower Bugatti Veyron Super Sport roadsters while retail investors whose brokers did not employ similarly sophisticated and expensive systems on their behalf were puttering around in the equivalent of the family sedan.

The new division housed virtually every economist at the SEC and placed them on equal footing with the lawyers. Hu from time to time brought in outside lecturers to keep his division up to speed. He had been the one to invite Arnuk and Saluzzi to talk to the staff about high-frequency trading (HFT) in November 2009. The day of the Flash Crash, Clara Vega, an economist with Federal Reserve Board, was at the SEC at the behest of Hu presenting a seminar on her paper, “Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market.” The paper found that high-frequency traders reduce market volatility and provide liquidity to the marker in times of stress. Back in the 1990s, Gensler, a former Goldman Sachs trader, had been one of a cadre of good old boys that included Alan Greenspan and former Treasury Secretary Robert Rubin that had kept the CFTC under Brooksley Born from regulating derivatives despite her repeated warnings that these instruments posed a systemic risk to the financial markets.

He told the High Frequency Trading Review in June 2010 that if one multiplied the penny-per-share profit margin of a typical high-frequency trade by the amount of daily HFT volume, which he estimated at 10 billion shares, the sum would be $2 billion in annual profits. Narang complained, “Some firms, like TABB, have grossly overstated the amount of HFT revenues by expanding the definition to include algorithmic trading in general, regardless of the actual holding period of the trader. Large stat-arb firms,5 which hold positions for multiple days, are not engaging in HFT. By definition, if you are able to hold positions for that long, you are not part of the HFT arms race, and [you] don’t need superfast technology to access opportunities before they disappear!” Narang had a narrow definition of HFT. Some high-frequency traders were large firms such as Getco and Tradebot Systems, both of which were market-making firms that provided the same services once delivered by specialists.


pages: 356 words: 105,533

Dark Pools: The Rise of the Machine Traders and the Rigging of the U.S. Stock Market by Scott Patterson

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algorithmic trading, automated trading system, banking crisis, bash_history, Bernie Madoff, butterfly effect, buttonwood tree, Chuck Templeton: OpenTable, cloud computing, collapse of Lehman Brothers, computerized trading, creative destruction, Donald Trump, fixed income, Flash crash, Francisco Pizarro, Gordon Gekko, Hibernia Atlantic: Project Express, High speed trading, Joseph Schumpeter, latency arbitrage, Long Term Capital Management, Mark Zuckerberg, market design, market microstructure, pattern recognition, pets.com, Ponzi scheme, popular electronics, prediction markets, quantitative hedge fund, Ray Kurzweil, Renaissance Technologies, Sergey Aleynikov, Small Order Execution System, South China Sea, Spread Networks laid a new fibre optics cable between New York and Chicago, stealth mode startup, stochastic process, transaction costs, Watson beat the top human players on Jeopardy!, zero-sum game

The trouble was that when a large number of algorithms sold and shut down, the market became more volatile, triggering more selling. In other words, a vicious self-reinforcing feedback loop. The Flash Crash had proven this wasn’t merely a fanciful nightmare scenario bandied about by apocalyptic market Luddites. The question tormenting experts was how far the loop would go next time. Progress Software, a firm that tracks algorithmic trading, predicted that a financial institution would lose one billion dollars or more in 2012 when a rogue algorithm went “into an infinite loop … which cannot be shut down.” And since the computer programs were now linked across markets—stock trades were synced to currencies and commodities and futures and bonds—and since many of the programs were very similar and were massively leveraged, the fear haunting the minds of the Plumbers was that the entire system could snap like a brittle twig in a matter of minutes.

It was fully automated (though traders could jump in and manually trade under certain circumstances) and extremely aggressive. Calculating that most firms wouldn’t have the ability to make a profit by rapidly trading options in the decimal era, Bodek believed he’d have a golden opportunity to become a major player by taking the risk and, with TW’s help, deploying models sophisticated enough to manage the risk. Trading began in August 2008. The strategy Bodek designed was the culmination of a complex algorithmic trading tradition that had started at Hull and that he’d carried on at Goldman and UBS. He’d started with the premise that he could model the theoretical value of all options as implied by the price of the underlying stock. Throughout the trading day, there were small swings in the prices of the options that signaled to the Machine that they had swung away from their true theoretical value. That meant an opportunity.

At first, they were electronic networks that allowed large investors to swap big blocks of stock away from the prying eyes of the lit market. Among the first was a pool called Liquidnet, launched in 2000. In 2004, Dan Mathisson at Credit Suisse built a dark pool called Crossfinder. Pipeline Trading, run by a nuclear physicist and a former president of Nasdaq, rolled out a dark pool for big block trading the same year. Goldman Sachs would build a dark pool called Sigma X. Even Getco would eventually launch a dark pool. As algorithmic trading grew, large investors were finding it harder to trade large chunks of stock. More and more trades were sliced and diced into small, round-numbered pieces—two hundred, three hundred shares—that algos could more easily juggle. The algos deployed complex methods to hunt out the large whale orders the big firms traded, such as “pinging” dark pools with orders that they canceled seconds later.


pages: 320 words: 87,853

The Black Box Society: The Secret Algorithms That Control Money and Information by Frank Pasquale

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Affordable Care Act / Obamacare, algorithmic trading, Amazon Mechanical Turk, American Legislative Exchange Council, asset-backed security, Atul Gawande, bank run, barriers to entry, basic income, Berlin Wall, Bernie Madoff, Black Swan, bonus culture, Brian Krebs, call centre, Capital in the Twenty-First Century by Thomas Piketty, Chelsea Manning, Chuck Templeton: OpenTable, cloud computing, collateralized debt obligation, computerized markets, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, crowdsourcing, cryptocurrency, Debian, don't be evil, drone strike, Edward Snowden, en.wikipedia.org, Fall of the Berlin Wall, Filter Bubble, financial innovation, financial thriller, fixed income, Flash crash, full employment, Goldman Sachs: Vampire Squid, Google Earth, Hernando de Soto, High speed trading, hiring and firing, housing crisis, informal economy, information asymmetry, information retrieval, interest rate swap, Internet of things, invisible hand, Jaron Lanier, Jeff Bezos, job automation, Julian Assange, Kevin Kelly, knowledge worker, Kodak vs Instagram, kremlinology, late fees, London Interbank Offered Rate, London Whale, Marc Andreessen, Mark Zuckerberg, mobile money, moral hazard, new economy, Nicholas Carr, offshore financial centre, PageRank, pattern recognition, Philip Mirowski, precariat, profit maximization, profit motive, quantitative easing, race to the bottom, recommendation engine, regulatory arbitrage, risk-adjusted returns, Satyajit Das, search engine result page, shareholder value, Silicon Valley, Snapchat, Spread Networks laid a new fibre optics cable between New York and Chicago, statistical arbitrage, statistical model, Steven Levy, the scientific method, too big to fail, transaction costs, two-sided market, universal basic income, Upton Sinclair, value at risk, WikiLeaks, zero-sum game

Financial intermediaries also spare small-time investors the trouble of actually understanding the business model and future prospects of what they invest in. “No need to worry if it’s a bit of a black box,” a broker may counsel about a hot tip. “It’s our job to understand the details.” Sadly, many workers who earnestly contribute to 401(k) plans mistake the unglamorous realities of fixed-income arbitrage, algorithmic trading, and mind-numbing derivative contracts for the glitter of venture capital jackpots. Investors like to think of their money supporting brave innovators and entrepreneurs. But how many really 128 THE BLACK BOX SOCIETY know the ultimate destinations of their dollars? As Doug Henwood has shown, nearly all of the activity in the current stock market is transfers of existing shares.113 The trading simply reallocates claims to the future productivity of existing firms.

Finance’s pervasive short-termism crowds out visionaries.114 Even for those used to the American solicitude for moneyed interests, finance debates are extraordinarily skewed toward the sector’s insiders and away from broader social concern about what useful ser vices they actually provide to the rest of the society. The trend toward self-reference reaches a reductio ad absurdum in an avantgarde form of black box finance: high-speed algorithmic trading. The Low Social Value of High-Frequency Trading Modern equity markets are very complex.115 For example, consider what happens when an investor logs into an account at a brokerage to place an order (all within a second, given automation). The broker will sometimes send the trade to wholesalers. As of 2012, these wholesalers could “internalize” about a fifth of trades, matching them with their own internal orders.

HFT’ers merely anticipate and mimic what others are doing, without exploring the underlying value of the company whose shares are being traded. This bare signaling is another version of the black box problems illuminated in credit ratings or credit default swaps. The mere existence of an AAA rating, or insurance from AIG, led to a FINANCE’S ALGORITHMS 131 false sense of security for many investors. Here, buy and sell signals can take on a life of their own, leading to momentum trading and herding.124 Algorithmic trading can create extraordinary instability and frozen markets when split-second trading strategies interact in unexpected ways.125 Consider, for instance, the flash crash of May 6, 2010, when the stock market lost hundreds of points in a matter of minutes.126 In a report on the crash, the CFTC and SEC observed that “as liquidity completely evaporated,” trades were “executed at irrational prices as low as one penny or as high as $100,000.”127 Traders had programmed split-second algorithmic strategies to gain a competitive edge, but soon found themselves in the position of a sorcerer’s apprentice, unable to control the technology they had developed.128 Though prices returned to normal the same day, there is no guarantee future markets will be so lucky.


pages: 523 words: 143,139

Algorithms to Live By: The Computer Science of Human Decisions by Brian Christian, Tom Griffiths

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4chan, Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, algorithmic trading, anthropic principle, asset allocation, autonomous vehicles, Bayesian statistics, Berlin Wall, Bill Duvall, bitcoin, Community Supported Agriculture, complexity theory, constrained optimization, cosmological principle, cryptocurrency, Danny Hillis, David Heinemeier Hansson, delayed gratification, dematerialisation, diversification, Donald Knuth, double helix, Elon Musk, fault tolerance, Fellow of the Royal Society, Firefox, first-price auction, Flash crash, Frederick Winslow Taylor, George Akerlof, global supply chain, Google Chrome, Henri Poincaré, information retrieval, Internet Archive, Jeff Bezos, John Nash: game theory, John von Neumann, knapsack problem, Lao Tzu, Leonard Kleinrock, linear programming, martingale, Nash equilibrium, natural language processing, NP-complete, P = NP, packet switching, Pierre-Simon Laplace, prediction markets, race to the bottom, RAND corporation, RFC: Request For Comment, Robert X Cringely, sealed-bid auction, second-price auction, self-driving car, Silicon Valley, Skype, sorting algorithm, spectrum auction, Steve Jobs, stochastic process, Thomas Bayes, Thomas Malthus, traveling salesman, Turing machine, urban planning, Vickrey auction, Vilfredo Pareto, Walter Mischel, Y Combinator, zero-sum game

Seale, Darryl A., and Amnon Rapoport. “Sequential Decision Making with Relative Ranks: An Experimental Investigation of the ‘Secretary Problem.’” Organizational Behavior and Human Decision Processes 69 (1997): 221–236. Sen, Amartya. “Goals, Commitment, and Identity.” Journal of Law, Economics, and Organization 1 (1985): 341–355. Sethi, Rajiv. “Algorithmic Trading and Price Volatility.” Rajiv Sethi (blog), May 7, 2010, http://rajivsethi.blogspot.com/2010/05/algorithmic-trading-and-price.html. Sevcik, Kenneth C. “Scheduling for Minimum Total Loss Using Service Time Distributions.” Journal of the ACM 21, no. 1 (1974): 66–75. Shallit, Jeffrey. “What This Country Needs Is an 18¢ Piece.” Mathematical Intelligencer 25, no. 2 (2003): 20–23. Shasha, Dennis, and Cathy Lazere. Out of Their Minds: The Lives and Discoveries of 15 Great Computer Scientists.

He’s seemingly the only person in the world willing to pay, in this case, $49 for a stock that the market is apparently valuing at under $40, but he doesn’t care; he’s seen the quarterly reports, he’s certain in what he knows. Investors are said to fall into two broad camps: “fundamental” investors, who trade on what they perceive as the underlying value of a company, and “technical” investors, who trade on the fluctuations of the market. The rise of high-speed algorithmic trading has upset the balance between these two strategies, and it’s frequently complained that computers, unanchored to the real-world value of goods—unbothered at pricing a texbook at tens of millions of dollars and blue-chip stocks at a penny—worsen the irrationality of the market. But while this critique is typically leveled at computers, people do the same kind of thing too, as any number of investment bubbles can testify.

a sale price of more than $23 million: The pricing on this particular Amazon title was noticed and reported on by UC Berkeley biologist Michael Eisen; see “Amazon’s $23,698,655.93 book about flies,” April 23, 2011 on Eisen’s blog it is NOT junk, http://www.michaeleisen.org/blog/?p=358. worsen the irrationality of the market: See, for instance, the reactions of Columbia University economist Rajiv Sethi in the immediate wake of the flash crash. Sethi, “Algorithmic Trading and Price Volatility.” save the entire herd from disaster: This can also be thought of in terms of mechanism design and evolution. It is better on average for any particular individual to be a somewhat cautious herd follower, yet everyone benefits from the presence of some group members who are headstrong mavericks. In this way, overconfidence can be thought of as a form of altruism. For more on the “socially optimal proportion” of such group members, see Bernardo and Welch, “On the Evolution of Overconfidence and Entrepreneurs.”

How I Became a Quant: Insights From 25 of Wall Street's Elite by Richard R. Lindsey, Barry Schachter

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Albert Einstein, algorithmic trading, Andrew Wiles, Antoine Gombaud: Chevalier de Méré, asset allocation, asset-backed security, backtesting, bank run, banking crisis, Black-Scholes formula, Bonfire of the Vanities, Bretton Woods, Brownian motion, business process, buy low sell high, capital asset pricing model, centre right, collateralized debt obligation, commoditize, computerized markets, corporate governance, correlation coefficient, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, discounted cash flows, disintermediation, diversification, Donald Knuth, Edward Thorp, Emanuel Derman, en.wikipedia.org, Eugene Fama: efficient market hypothesis, financial innovation, fixed income, full employment, George Akerlof, Gordon Gekko, hiring and firing, implied volatility, index fund, interest rate derivative, interest rate swap, John von Neumann, linear programming, Loma Prieta earthquake, Long Term Capital Management, margin call, market friction, market microstructure, martingale, merger arbitrage, Myron Scholes, Nick Leeson, P = NP, pattern recognition, Paul Samuelson, pensions crisis, performance metric, prediction markets, profit maximization, purchasing power parity, quantitative trading / quantitative finance, QWERTY keyboard, RAND corporation, random walk, Ray Kurzweil, Richard Feynman, Richard Feynman, Richard Stallman, risk-adjusted returns, risk/return, shareholder value, Sharpe ratio, short selling, Silicon Valley, six sigma, sorting algorithm, statistical arbitrage, statistical model, stem cell, Steven Levy, stochastic process, systematic trading, technology bubble, The Great Moderation, the scientific method, too big to fail, trade route, transaction costs, transfer pricing, value at risk, volatility smile, Wiener process, yield curve, young professional

But, shortly after I joined, my boss, the head of program trading, and his boss resigned en masse to join another firm. On the bright side, I had learned enough and had produced a first model for the program trading. I was disappointed at the turn of events. I had enjoyed working with the program trading team. But the silver lining for me was that I ended up publishing the work and the model itself has become so widely used as a basis for algorithmic trading.21 At the time, I had JWPR007-Lindsey 128 May 7, 2007 16:55 h ow i b e cam e a quant hoped to develop a model and implement it at Morgan Stanley. That is not how it worked out, but in the end it was a great introduction to quant research. For a first job on Wall Street, things went pretty well. I learned a great deal and I worked at a truly first rate firm. I also met a number of people who have had a tremendous influence on my career.

First in Goldman Sachs Quantitative Strategies Research Notes, February 1996, “Implied Trinomial Trees of the Volatility Smile,” Derman, Kani, and Chriss, and then in a paper by the same name published in the Summer 1996 in the Journal of Derivatives. 19. I published this as “Transatlantic Trees,” Risk 9, no. 7 (1996). 20. The book was published in late 1996 by Irwin Publications under the title Black-Scholes and Beyond. Irwin was later acquired by McGrawHill publications. 21. I ended up publishing the work in the paper Optimal Liquidation of Portfolio Transactions with Robert Algren. Institutional Investor published an article about Algorithmic Trading in its November 2004 issue titled “The Orders Battle” that noted this article “helped lay the groundwork for arrival-price algorithms being developed on Wall Street.” 22. Technically speaking, this is a variational problem. In particular the set of all possible paths is infinite dimensional and the optimal path for a given level of risk aversion is the solution to the Euler-Lagrange equation. 23.

He has held positions in the mathematics departments at Harvard University and the Institute for Advanced Study. He is a founding member of the board of Math for America, a nonprofit dedicated to improving the quality of mathematics teaching in the United States. He is also a member of the board the Mathematical Finance program at University of Chicago. Dr. Chriss has published extensively in quantitative finance – including “Optimal Execution of Portfolio Transactions” a seminal paper on algorithmic trading, “Optimal Portfolios from Ordering Information,” and the book Black-Scholes and Beyond: Modern Option Pricing. Dr. Chriss holds an BS and PhD in mathematics from University of Chicago and an MS in mathematics from California Institute of Technology. Andrew Davidson is president and founder of Andrew Davidson & Co., Inc., a consulting firm specializing in the application of analytical tools to investment management.


pages: 323 words: 95,939

Present Shock: When Everything Happens Now by Douglas Rushkoff

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algorithmic trading, Andrew Keen, bank run, Benoit Mandelbrot, big-box store, Black Swan, British Empire, Buckminster Fuller, cashless society, citizen journalism, clockwork universe, cognitive dissonance, Credit Default Swap, crowdsourcing, Danny Hillis, disintermediation, Donald Trump, double helix, East Village, Elliott wave, European colonialism, Extropian, facts on the ground, Flash crash, game design, global supply chain, global village, Howard Rheingold, hypertext link, Inbox Zero, invention of agriculture, invention of hypertext, invisible hand, iterative process, John Nash: game theory, Kevin Kelly, laissez-faire capitalism, Law of Accelerating Returns, loss aversion, mandelbrot fractal, Marshall McLuhan, Merlin Mann, Milgram experiment, mutually assured destruction, negative equity, Network effects, New Urbanism, Nicholas Carr, Norbert Wiener, Occupy movement, passive investing, pattern recognition, peak oil, price mechanism, prisoner's dilemma, Ralph Nelson Elliott, RAND corporation, Ray Kurzweil, recommendation engine, selective serotonin reuptake inhibitor (SSRI), Silicon Valley, Skype, social graph, South Sea Bubble, Steve Jobs, Steve Wozniak, Steven Pinker, Stewart Brand, supply-chain management, the medium is the message, The Wisdom of Crowds, theory of mind, Turing test, upwardly mobile, Whole Earth Catalog, WikiLeaks, Y2K, zero-sum game

Their contribution to the tragedy from which they hoped to benefit notwithstanding, they were making a prediction and making a bet based on their analysis of where the future was heading. Temporally compressed though it may be, it is still based on making conclusions. Value is created over time. It is a product of the cause-and-effect, temporal universe—however much it may be abstracted. A majority of equity trading today is designed to circumvent that universe of time-generated value altogether. Computer-driven or algorithmic trading, as it is now called, has its origins in the arms race. Mathematicians spent decades trying to figure out a way to evade radar. They finally developed stealth technology, which really just works by using electric fields to make a big thing—like a plane—appear to be many little things. Then, in 1999, an F-117 using stealth was shot down over Serbia. It seems some Hungarian mathematicians had figured out that instead of looking for objects in the sky, the antiaircraft detection systems needed to look only for the electrical fields.28 Those same mathematicians and their successors are now being employed by Wall Street firms to hide from and predict one another’s movements.

The algorithms actually shoot out little trades, much like radar, in order to measure the response of the market and then infer if there are any big movements going on. The original algorithms are, in turn, on the lookout for these little probes and attempt to run additional countermoves and fakes. This algorithmic dance—what is known as black box trading—accounts for over 70 percent of Wall Street trading activity today. In high-frequency, algorithmic trading, speed is everything. Algorithms need to know what is happening and make their moves before their enemy algorithms can react and adjust. No matter how well they write their programs, and no matter how powerful the computers they use, the most important factor in bringing algorithms up to speed is a better physical location on the network. The physical distance of a brokerage house’s computers to the computers executing the trades makes a difference in how fast the algorithm can read and respond to market activity.

He dedicated the book to Benoit Mandelbrot. While fractal geometry can certainly help us find strong, repeating patterns within the market activity of the 1930s Depression, it did not predict the crash of 2007. Nor did the economists using fractals manage to protect their banks and brokerages from the systemic effects of bad mortgage packages, overleveraged European banks, or the impact of algorithmic trading on moment-to-moment volatility. More recently, in early 2010, the world’s leading forecaster applying fractals to markets, Robert Prechter, called for the market to enter a decline of such staggering proportions that it would dwarf anything that has happened in the past three hundred years.16 Prechter bases his methodology on the insights of a 1930s economist, Ralph Nelson Elliott, who isolated a number of the patterns that seem to recur in market price data.


pages: 366 words: 94,209

Throwing Rocks at the Google Bus: How Growth Became the Enemy of Prosperity by Douglas Rushkoff

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3D printing, activist fund / activist shareholder / activist investor, Airbnb, algorithmic trading, Amazon Mechanical Turk, Andrew Keen, bank run, banking crisis, barriers to entry, bitcoin, blockchain, Burning Man, business process, buy low sell high, California gold rush, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, centralized clearinghouse, citizen journalism, clean water, cloud computing, collaborative economy, collective bargaining, colonial exploitation, Community Supported Agriculture, corporate personhood, corporate raider, creative destruction, crowdsourcing, cryptocurrency, disintermediation, diversified portfolio, Elon Musk, Erik Brynjolfsson, ethereum blockchain, fiat currency, Firefox, Flash crash, full employment, future of work, gig economy, Gini coefficient, global supply chain, global village, Google bus, Howard Rheingold, IBM and the Holocaust, impulse control, income inequality, index fund, iterative process, Jaron Lanier, Jeff Bezos, jimmy wales, job automation, Joseph Schumpeter, Kickstarter, loss aversion, Lyft, Marc Andreessen, Mark Zuckerberg, market bubble, market fundamentalism, Marshall McLuhan, means of production, medical bankruptcy, minimum viable product, Naomi Klein, Network effects, new economy, Norbert Wiener, Oculus Rift, passive investing, payday loans, peer-to-peer lending, Peter Thiel, post-industrial society, profit motive, quantitative easing, race to the bottom, recommendation engine, reserve currency, RFID, Richard Stallman, ride hailing / ride sharing, Ronald Reagan, Satoshi Nakamoto, Second Machine Age, shareholder value, sharing economy, Silicon Valley, Snapchat, social graph, software patent, Steve Jobs, TaskRabbit, The Future of Employment, trade route, transportation-network company, Turing test, Uber and Lyft, Uber for X, unpaid internship, Y Combinator, young professional, zero-sum game, Zipcar

Although it may cost the trader only a few pennies extra, those pennies add up when algorithms perform these routines millions of times a day, extracting real value from the market. To be clear, the algorithms are providing no service. Any liquidity they might create is more than compensated for by the liquidity they take away when they’re seeking to generate volatility or panic selling. They disadvantage not only human brokers but also the individual investors that a digital stock market was supposed to empower. Algorithmic trading doesn’t happen on a laptop connected to the net by Wi-Fi. It requires the kind of hardware, connectivity, and real-estate location that only the wealthiest, most established firms can afford. It may be disruptive to trading, but it only enhances the advantages of the traditional players—or at least the firms they worked for before they were replaced by machines. New exchanges are emerging to counteract some of these trends.

You may need to scroll forward from that location to find the corresponding reference on your e-reader. acquisitions, 78 Acxiom, 32, 40–41 advertising, 20–21. See also marketing big data and, 42 branding and, 20, 35–37 “likes” economy and, 35–37 AFL-CIO Housing Investment Trust, 210 Agency.com, 196 Airbnb, 98–99, 213, 219, 222 destructive destruction and, 100 peer-to-peer commerce enabled by, 45, 46 algorithmic trading, 179–84 Alphabet, 78 Amazon, 50, 76, 83, 93, 219, 229 destructive destruction and, 100 highly centralized sales platform of, 29 as platform monopoly, 87–90 Amazon Associates, 89 Amazon Mechanical Turk, 49n, 50, 90, 214, 222 Amazon Prime, 90 American Revolution, 71–72, 75 Ameritrade, 177, 178 amplification, 70, 73 Amsted Industries, 117 Anderson, Chris, 26, 33 Andreessen, Marc, 191–92 angel investors, 187, 188 AngelList, 201 Aoki, Steve, 36 Apple, 37, 76, 80, 83, 141, 218 Ariel, 209–10 aristocracy, 17–18, 22, 70, 128–29, 133, 230 Aristotle, 69 artificial intelligence, 90–91 artisanal economy, 16–18, 21, 22, 226, 233–34 arts, funding of, 236 Atkinson, Anthony B., 65 austerity, 136–37 auto attendants, 14 Bandcamp, 29–30 Barber, Brad, 177 Barnes & Noble, 83, 87 barter, 127 barter exchanges, 159 Basecamp, 59–60 BASF, 107 Battle-Bro, 121 Bauwens, Michael, 221 bazaars, 16–18 money and, 127 obsolescence of, caused by corporations, 70–71 Bell, Daniel, 53 Belloc, Hilaire, 229 benefit corporations, 119 Ben & Jerry’s, 80, 205 Benna, Ted, 171 BerkShare, 154–55 Best Buy, 90 Bezos, Jeff, 90, 92–93 Biewald, Lukas, 49–50 big data, 39–44 data point collection and comparisons of, 41–42 game changing product invention reduced by reliance on, 43 predicting future choices, as means of, 41, 42–43 reduction in spontaneity of customers and, 43 social graphs and, 40 suspicion of, as increasing value of data already being sold, 43–44 traditional market research, distinguished, 41 Big Shift, 76 biopiracy, 218 biotech crash of 1987, 6 Bitcoin, 143–49, 150–51, 152, 219, 222 BitTorrent, 142–43, 219 Blackboard, 95–96 Blackstone Group, 115 black swans, 183 blockchain, 144–51, 222 Bitcoin, 144, 145, 146, 147, 149, 222 decentralized autonomous corporations (DACs) and, 149–50 Blogger, 8, 31 Bloomberg, 182 Bodie, Zvie, 174 Borders, 83, 87 bot programs, 37 bounded investing, 210–15 Bovino, Beth Ann, 81–82 Brand, Russell, 36 branding, 20 social, and “likes” economy, 35–37 Branson, Richard, 121 Brin, Sergey, 92–93 Bristol Pounds, 156 British East India Company, 71–72 Brixton Pounds, 156 brokered barter system, 127 Brynjolfsson, Erik, 23, 53 Buffett, Warren, 168, 209 burn rate, 190 Bush, Jeb, 227–28 Calacanis, Jason, 201 Calvert, 209–10 Campbell Soup Company, 119 capital.

., 229 Circuit City, 90 Citizens United case, 72 Claritas, 32 click workers, 50 climate change, 135, 227–28, 237 coin of the realm, 128–29 collaboration as corporate strategy, 106–7 colonialism, 71–72 commons, 215–23 co-owned networks and, 220–23 history of, 215–16 projects inspired by, 217–18 successful, elements of, 216–17 tragedy of, 215–16 worker-owned collectives and, 219–20 competencies, of corporations, 79–80 Connect+Develop, 107 Consumer Electronics Show, 19 Consumer Reports,33 contracting with small and medium-sized enterprises, 112 cooperative currencies, 160–65 favor banks, 161 LETS (Local Exchange Trading System), 163–65 time dollar systems, 161–63 co-owned networks, 220–23 corporations, 68–82 acquisition of startups, growth through, 78 amplifying effect of, 70, 73 Big Shift and, 76 cash holdings of, 76, 77–78 competency of, 79–80 cost reduction, growth through, 79–80 decentralized autonomous corporations (DACs), 149–50 Deloitte’s study of return on assets (ROA) of, 76–77 distributive alternative to platform monopolies, 93–97 evaluation of, 69–74 extractive nature of, 71–72, 73, 74, 75, 80–82 growth targets, meeting, 68–69 income inequality and, 81–82 limits to corporate model, 75–76, 80–82 managerial and financial methods to deliver growth by, 77–79 monopolies (See monopolies) obsolescence created by, 70–71, 73 offshoring and, 78–79 personhood of, 72, 73–74, 90, 91 recoding of, 93–97, 125–26 repatriation and, 80 retrieval of values of empire and, 71–72, 73 as steady-state enterprises, 97–123 Costco, 74 cost reduction, and corporate growth, 79–80 Couchsurfing.com, 46 crashes of 1929, 99 of 2007, 133–34 biotech crash, of 1987, 6 flash crash, 180 Creative Commons, 215 creative destruction, 83–87 credit, 132–33 credit-card companies, 143–44 crowdfunding, 38–39, 198–201 crowdsharing apps, 45–49 crowdsourcing platforms, 49–50 Crusades, 16 Cumbrian Pounds, 156 Curitiba, Brazil modified LETS program, 164–65 Daly, Herman, 184 data big, 39–44 getting paid for our own, 44–45 “likes” economy and, 32, 34–36 in pre-digital era, 40 Datalogix, 32 da Vinci, Leonardo, 236 debt, 152–54 decentralized autonomous corporations (DACs), 149–50 deflation, 169 Dell, 115–16 Dell, Michael, 115–16 Deloitte Center for the Edge, 76–77 destructive destruction, 100 Detroit Dollars, 156 digital distributism, 224–39 artisanal era mechanisms and values retrieved by, 233–34 developing distributive businesses, 237–38 digital industrialism compared, 226 digital technology and, 230–31 historical ideals of distributism, 228–30 leftism, distinguished, 231 Pope Francis’s encyclical espousing distributed approach to land, labor and capital, 227–28 Renaissance era values, rebirth of, 235–37 subsidiarity and, 231–32 sustainable prosperity as goal of, 226–27 digital economy, 7–11 big data and, 39–44 destabilizing form of digitally accelerated capitalism, creation of, 9–10 digital marketplace, development of, 24–30 digital transaction networks and, 140–51 disproportionate relationship between capital and value in, 9 distributism and, 224–39 externalizing cost of replacing employees in, 14–15 industrialism and, 13–16, 23–24, 44, 53–54, 93, 101–2, 201, 214, 226 industrial society, distinguished, 11 “likes” and similar metrics, economy of, 30–39 platform monopolies and, 82–93, 101 digital industrialism, 13–16, 23–24, 101–2, 201 digital distributism compared, 226 diminishing returns of, 93 externalizing costs and, 14–15 growth agenda and, 14–15, 23–24 human data as commodity under, 44 income disparity and, 53–54 labor and land pushed to unbound extremes by, 214 “likes” economy and, 33 reducing bottom line as means of creating illusion of growth and, 14 digital marketplace, 24–30 early stages of e-commerce, 25–26 highly centralized sales platforms of, 29 initial treatment of Internet as commons, 25 “long tail” of widespread digital access and, 26 positive reinforcement feedback loop and, 28 power-law dynamics and, 26–29 removal of humans from selection process in, 28 digital transaction networks, 140–51 Bitcoin, 143–49, 150–51, 152 blockchains and, 144–51 central authorities, dependence on, 142 decentralized autonomous corporations (DACs) and, 149–50 PayPal, 140–41 theft and, 142 direct public offerings (DPOs), 205–6 discount brokerages, 176–78 diversification, 208, 211 dividends, 113–14, 208–10 dividend traps, 113 Dorsey, Jack, 191–92 Draw Something, 192, 193 Drexler, Mickey, 116 dual transformation, 108–9 dumbwaiter effect, 19 Dutch East India Company, 71, 89, 131 eBay, 16, 26, 29, 45, 140 education industry, 95–97 Eisenhower administration, 52–53, 63, 75 Elberse, Anita, 28 employee-owned companies, 116–18 Enron, 133, 171n Eroski, 220 eSignal, 178 EthicalBay, 221 E*Trade, 176, 177 Etsy, 16, 26, 30 expense reduction, and corporate growth, 78–79 Facebook, 4, 31, 83, 93, 96, 201 data gathering and sales by, 41, 44 innovation by acquisition of startups, 78 IPO of, 192–93, 195 psychological experiments conducted on users by, 32–33 factors of production, 212–14 Fairmondo, 221 Family Assistance Plan, 63 family businesses, 103–4, 231–32 FarmVille, 192 favor banks, 161 Febreze Set & Refresh, 108 Federal Reserve, 137–38 feedback loop, and positive reinforcement, 28 Ferriss, Tim, 201 feudalism, 17 financial services industry, 131–33, 171–73, 175 Fisher, Irving, 158 flash crash, 180 flexible purpose corporations, 119–20 flow, investing in, 208–10 Forbes,88, 173, 174 40-hour workweek, reduction of, 58–60 401(k) plans, 171–74 Francis, Pope, 227, 228, 234 Free, Libre, Open Knowledge (FLOK) program, 217–18 Free (Anderson), 33 free money theory, local currencies based on, 156–59 barter exchanges, 159 during Great Depression, 158–59 self-help cooperatives, 159 stamp scrip, 158–59 tax anticipation scrip, 159 Wörgls, 157–58 frenzy, 98–99 Fried, Jason, 59 Friedman, Milton, 64 Friendster, 31 Frito-Lay, 80 front running, 180–81 Fulfillment by Amazon, 89 Fureai Kippu (Caring Relationship Tickets), 162 Future of Work initiative, 56n Gallo, Riso, 103–4 Gap, 116 Gates, Bill, 186 General Electric, 132 General Public License (GPL) for software, 216 Gesell, Silvio, 157 GI Bill, 99 Gimein, Mark, 147 Gini coefficient of income inequality, 81–82, 92 global warming, 135, 227–28, 237 GM, 80 Goldman Sachs, 133, 195 gold standard, 139 Google, 8, 48, 78, 83, 90–91, 93, 141, 218 acquisitions by, 191 business model of, 37 data sales by, 37, 44 innovation by acquisition of startups, 78 IPO of, 194–95 protests against, 1–3, 5, 98–99 grain receipts, 128 great decoupling, 53 Great Depression, 137, 158–59 Great Exhibition, 1851, 19 Greenspan, Alan, 132–33 growth, 1–11 bazaars, and economic expansion in late Middle Ages, 16–18 central currency and, 126, 129–31, 133–36 digital industrialism, growth agenda of, 14–15, 23–24 highly centralized e-commerce platforms and, 29 startups, hypergrowth expected of, 187–91 as trap (See growth trap) growth trap, 4–5, 68–123 central currency as core mechanism of, 133–34 corporations as program and, 68–82 platform monopolies and, 82–93, 101 recoding corporate model and, 93–97 steady-state enterprises and, 98–123 guaranteed minimum income programs, 62–65 guaranteed minimum wage public jobs, 65–66 guilds, 17 Hagel, John, 76–77 Hardin, Garrett, 215–16 Harvard Business Review,108–9 Heiferman, Scott, 196–97 Henry VIII, King, 215, 229 Hewlett-Packard UK, 112 high-frequency trading (HFT), 179–80 Hilton, 115 Hobby Lobby case, 72 Hoffman, Reid, 61 Holland, Addie Rose, 205–6 holograms, 235 Homeport New Orleans, 121 housing industry, 135 Huffington, Arianna, 34, 35, 201 Huffington Post, 34, 201 human role in economy, 13–67 aristocracy’s efforts to control peasant economy, 17–18 bazaars and, 16–18 big data and, 39–44 chartered monopolies and, 18 decreasing employment and, 30–39 digital marketplace, impact of, 24–30 industrialism and, 13–16, 18–24, 44 “likes” economy and, 30–39 reevaluation of employment and adopting policies to decrease it and, 54–67 sharing economy and, 44–54 Hurwitz, Charles, 117 IBM, 90–91, 112 inclusive capitalism, 111–12 income disparity corporate model and, 81–82 digital technology as accelerating, 53–54 Gini coefficient of, 81–82, 92 growth trap and, 4 power-law dynamics and, 27–28, 30 public service options for reducing, 65–66 IndieGogo, 30, 199 individual retirement accounts (IRAs), 171 industrial farming, 134–35 industrialism, 18–24 branding and, 20 digital, 13–16, 23–24, 44, 53–54, 93, 101–2, 201, 214, 226 disempowerment of workers and, 18–19 human connection between producer and consumer, loss of, 19–20 isolation of human consumers from one another and, 20–21 mass marketing and, 19–20 mass media and, 20–21 purpose of, 18–19, 22 value system of, 18–19 inflation, 169 Instagram, 31 Intercontinental Exchange, 182 interest, 129–31 investors/investing, 70, 72, 168–223 algorithmic trading and, 179–84 bounded, 210–15 commons model for running businesses and, 215–23 crowdfunding and, 198–201 derivative finance, volume of, 182 digital technology and, 169–70, 175–84 direct public offerings (DPOs) and, 205–6 discount brokerages and, 176–78 diversification and, 208, 211 dividends and, 208–10 flow, investing in, 208–10 high-frequency trading (HFT) and, 179–80 in low-interest rate environment, 169–70 microfinancing platforms and, 202–4 platform cooperatives and, 220–23 poor performance of do-it-yourself traders and, 177–78 retirement savings and, 170–75 startups and, 184–205 ventureless capital and, 196–205 irruption, 98 i-traffic, 196 iTunes, 27, 29, 34, 89 J.


Adaptive Markets: Financial Evolution at the Speed of Thought by Andrew W. Lo

Albert Einstein, Alfred Russel Wallace, algorithmic trading, Andrei Shleifer, Arthur Eddington, Asian financial crisis, asset allocation, asset-backed security, backtesting, bank run, barriers to entry, Berlin Wall, Bernie Madoff, bitcoin, Bonfire of the Vanities, bonus culture, break the buck, Brownian motion, business process, butterfly effect, capital asset pricing model, Captain Sullenberger Hudson, Carmen Reinhart, Chance favours the prepared mind, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computerized trading, corporate governance, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, Daniel Kahneman / Amos Tversky, delayed gratification, Diane Coyle, diversification, diversified portfolio, double helix, easy for humans, difficult for computers, Ernest Rutherford, Eugene Fama: efficient market hypothesis, experimental economics, experimental subject, Fall of the Berlin Wall, financial deregulation, financial innovation, financial intermediation, fixed income, Flash crash, Fractional reserve banking, framing effect, Gordon Gekko, greed is good, Hans Rosling, Henri Poincaré, high net worth, housing crisis, incomplete markets, index fund, interest rate derivative, invention of the telegraph, Isaac Newton, James Watt: steam engine, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Meriwether, Joseph Schumpeter, Kenneth Rogoff, London Interbank Offered Rate, Long Term Capital Management, loss aversion, Louis Pasteur, mandelbrot fractal, margin call, Mark Zuckerberg, market fundamentalism, martingale, merger arbitrage, meta analysis, meta-analysis, Milgram experiment, money market fund, moral hazard, Myron Scholes, Nick Leeson, old-boy network, out of africa, p-value, paper trading, passive investing, Paul Lévy, Paul Samuelson, Ponzi scheme, predatory finance, prediction markets, price discovery process, profit maximization, profit motive, quantitative hedge fund, quantitative trading / quantitative finance, RAND corporation, random walk, randomized controlled trial, Renaissance Technologies, Richard Feynman, Richard Feynman, Richard Feynman: Challenger O-ring, risk tolerance, Robert Shiller, Robert Shiller, short selling, sovereign wealth fund, statistical arbitrage, Steven Pinker, stochastic process, survivorship bias, The Great Moderation, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Malthus, Thorstein Veblen, Tobin tax, too big to fail, transaction costs, Triangle Shirtwaist Factory, ultimatum game, Upton Sinclair, US Airways Flight 1549, Walter Mischel, Watson beat the top human players on Jeopardy!, WikiLeaks, Yogi Berra, zero-sum game

However, much of this monitoring can be automated with preset rules that alert the investor—text messaging, smartphone notification, and social media make this simple—to pay attention and make a decision when the need arises. This is particularly straightforward for passive strategies that are dedicated to achieving the returns of an index. Existing technology can easily integrate active risk management with passive investing through algorithmic trading, derivatives, securities exchange design, telecommunications, and back-office infrastructure. Thanks to these new technologies, the existing link between active risk management and active investing, and passive risk management and passive investing, can now be severed. Adaptive Markets in Action • 271 DISBANDING THE ALPHA BETA SIGMA FRATERNITY Here’s one concrete example of how to sever this link.

He first introduced me to several models in theoretical biology that were particularly relevant to economics, and in 1999 he and I published a paper outlining the possibility of applying evolutionary arguments to the Efficient Markets Hypothesis.22 He has since published many more papers applying ideas in physics and biology to finance. In addition to his academic pursuits, Doyne cofounded a successful quantitative hedge fund called Prediction Company with his fellow physicist, Norman Packard. This fund uses multiple algorithmic trading strategies to make money in stock markets around the world. Based on his experience, Doyne published a fascinating article in 2002 titled “Market Force, Ecology, and Evolution,” where he developed a precise analogy between financial markets and biological ecologies. His theory builds on Grossman and Stiglitz’s insight that if markets were perfectly efficient there would be no motive for financial trading, so markets can never be perfectly efficient.

If we add to these events the technology failures associated with the initial public offerings of BATS and Facebook (March 23 and May 18, 2012), Knight Capital Group’s $458 million loss from accidental electronic trades, and the two-and-a-half hour Bloomberg terminal outage (April 17, 2015) that postponed a multi-billion-dollar government debt issue, a pattern emerges. Evolution at the speed of thought hasn’t completely adapted yet to trading at the speed of light. Finance Behaving Badly • 361 Markets can’t give up financial technology cold turkey—the advantages of algorithmic trading and electronic markets are simply too great. Rather, we have to demand better, more robust technology that is so advanced it becomes foolproof and invisible to the human operator. Every successful technology has gone through such a process of maturation: the rotary telephone versus the iPhone, the scalpel versus the laser, the incandescent light bulb versus LEDs, and paper road maps versus Google Maps and GPS.


pages: 268 words: 75,850

The Formula: How Algorithms Solve All Our Problems-And Create More by Luke Dormehl

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3D printing, algorithmic trading, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, big data - Walmart - Pop Tarts, call centre, Cass Sunstein, Clayton Christensen, commoditize, computer age, death of newspapers, deferred acceptance, Edward Lorenz: Chaos theory, Erik Brynjolfsson, Filter Bubble, Flash crash, Florence Nightingale: pie chart, Frank Levy and Richard Murnane: The New Division of Labor, Google Earth, Google Glasses, High speed trading, Internet Archive, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, John Markoff, Kevin Kelly, Kodak vs Instagram, lifelogging, Marshall McLuhan, means of production, Nate Silver, natural language processing, Netflix Prize, pattern recognition, price discrimination, recommendation engine, Richard Thaler, Rosa Parks, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, Slavoj Žižek, social graph, speech recognition, Steve Jobs, Steven Levy, Steven Pinker, Stewart Brand, the scientific method, The Signal and the Noise by Nate Silver, upwardly mobile, Wall-E, Watson beat the top human players on Jeopardy!, Y Combinator

On social networking sites, algorithms highlight news that is “relevant” to us, and on dating sites like eHarmony they match us up with potential life partners. It is not “cyberbole,” then, to suggest that algorithms represent a crucial force in our participation in public life. They go further than the four main areas I have chosen to look at in this book, too. For instance, algorithmic trading now represents a whopping 70 percent of the U.S. equity market, running on supercomputers that are able to buy and sell millions of shares at practically the speed of light. Algorithmic trading has become a race measured in milliseconds, with billions of dollars dependent on the laying of new fiber-optic cables that will shave just five milliseconds off the communication time between financial markets in London and New York. (To put this in perspective, it takes a human 300 milliseconds to blink.)3 Medicine, too, has taken an algorithmic turn, as doctors working in hospitals are often asked to rely on algorithms rather than their own clinical judgment.


pages: 574 words: 164,509

Superintelligence: Paths, Dangers, Strategies by Nick Bostrom

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agricultural Revolution, AI winter, Albert Einstein, algorithmic trading, anthropic principle, anti-communist, artificial general intelligence, autonomous vehicles, barriers to entry, Bayesian statistics, bioinformatics, brain emulation, cloud computing, combinatorial explosion, computer vision, cosmological constant, dark matter, DARPA: Urban Challenge, data acquisition, delayed gratification, demographic transition, Donald Knuth, Douglas Hofstadter, Drosophila, Elon Musk, en.wikipedia.org, endogenous growth, epigenetics, fear of failure, Flash crash, Flynn Effect, friendly AI, Gödel, Escher, Bach, income inequality, industrial robot, informal economy, information retrieval, interchangeable parts, iterative process, job automation, John Markoff, John von Neumann, knowledge worker, Menlo Park, meta analysis, meta-analysis, mutually assured destruction, Nash equilibrium, Netflix Prize, new economy, Norbert Wiener, NP-complete, nuclear winter, optical character recognition, pattern recognition, performance metric, phenotype, prediction markets, price stability, principal–agent problem, race to the bottom, random walk, Ray Kurzweil, recommendation engine, reversible computing, social graph, speech recognition, Stanislav Petrov, statistical model, stem cell, Stephen Hawking, strong AI, superintelligent machines, supervolcano, technological singularity, technoutopianism, The Coming Technological Singularity, The Nature of the Firm, Thomas Kuhn: the structure of scientific revolutions, transaction costs, Turing machine, Vernor Vinge, Watson beat the top human players on Jeopardy!, World Values Survey, zero-sum game

Other systems specialize in finding arbitrage opportunities within or between markets, or in high-frequency trading that seeks to profit from minute price movements that occur over the course of milliseconds (a timescale at which communication latencies even for speed-of-light signals in optical fiber cable become significant, making it advantageous to locate computers near the exchange). Algorithmic high-frequency traders account for more than half of equity shares traded on US markets.69 Algorithmic trading has been implicated in the 2010 Flash Crash (see Box 2). * * * Box 2 The 2010 Flash Crash By the afternoon of May, 6, 2010, US equity markets were already down 4% on worries about the European debt crisis. At 2:32 p.m., a large seller (a mutual fund complex) initiated a sell algorithm to dispose of a large number of the E-Mini S&P 500 futures contracts to be sold off at a sell rate linked to a measure of minute-to-minute liquidity on the exchange.

After the market closed for the day, representatives of the exchanges met with regulators and decided to break all trades that had been executed at prices 60% or more away from their pre-crisis levels (deeming such transactions “clearly erroneous” and thus subject to post facto cancellation under existing trade rules).70 The retelling here of this episode is a digression because the computer programs involved in the Flash Crash were not particularly intelligent or sophisticated, and the kind of threat they created is fundamentally different from the concerns we shall raise later in this book in relation to the prospect of machine superintelligence. Nevertheless, these events illustrate several useful lessons. One is the reminder that interactions between individually simple components (such as the sell algorithm and the high-frequency algorithmic trading programs) can produce complicated and unexpected effects. Systemic risk can build up in a system as new elements are introduced, risks that are not obvious until after something goes wrong (and sometimes not even then).71 Another lesson is that smart professionals might give an instruction to a program based on a sensible-seeming and normally sound assumption (e.g. that trading volume is a good measure of market liquidity), and that this can produce catastrophic results when the program continues to act on the instruction with iron-clad logical consistency even in the unanticipated situation where the assumption turns out to be invalid.

Journal of Economic Growth 16 (2): 135–56. Zuleta, Hernando. 2008. “An Empirical Note on Factor Shares.” Journal of International Trade and Economic Development 17 (3): 379–90. INDEX A Afghan Taliban 215 Agricultural Revolution 2, 80, 261 AI-complete problem 14, 47, 71, 93, 145, 186 AI-OUM, see optimality notions AI-RL, see optimality notions AI-VL, see optimality notions algorithmic soup 172 algorithmic trading 16–17 anthropics 27–28, 126, 134–135, 174, 222–225 definition 225 Arendt, Hannah 105 Armstrong, Stuart 280, 291, 294, 302 artificial agent 10, 88, 105–109, 172–176, 185–206; see also Bayesian agent artificial intelligence arms race 64, 88, 247 future of 19, 292 greater-than-human, see superintelligence history of 5–18 overprediction of 4 pioneers 4–5, 18 Asimov, Isaac 139 augmentation 142–143, 201–203 autism 57 automata theory 5 automatic circuit breaker 17 automation 17, 98, 117, 160–176 B backgammon 12 backpropagation algorithm 8 bargaining costs 182 Bayesian agent 9–11, 123, 130; see also artificial agent and optimality notions Bayesian networks 9 Berliner, Hans 12 biological cognition 22, 36–48, 50–51, 232 biological enhancement 36–48, 50–51, 142–143, 232; see also cognitive enhancement boxing 129–131, 143, 156–157 informational 130 physical 129–130 brain implant, see cyborg brain plasticity 48 brain–computer interfaces 44–48, 51, 83, 142–143; see also cyborg Brown, Louise 43 C C. elegans34–35, 266, 267 capability control 129–144, 156–157 capital 39, 48, 68, 84–88, 99, 113–114, 159–184, 251, 287, 288, 289 causal validity semantics 197 CEV, see coherent extrapolated volition Chalmers, David 24, 265, 283, 295, 302 character recognition 15 checkers 12 chess 11–22, 52, 93, 134, 263, 264 child machine 23, 29; see also seed AI CHINOOK 12 Christiano, Paul 198, 207 civilization baseline 63 cloning 42 cognitive enhancement 42–51, 67, 94, 111–112, 193, 204, 232–238, 244, 259 coherent extrapolated volition (CEV) 198, 211–227, 296, 298, 303 definition 211 collaboration (benefits of) 249 collective intelligence 48–51, 52–57, 67, 72, 142, 163, 203, 259, 271, 273, 279 collective superintelligence 39, 48–49, 52–59, 83, 93, 99, 285 definition 54 combinatorial explosion 6, 9, 10, 47, 155 Common Good Principle 254–259 common sense 14 computer vision 9 computing power 7–9, 24, 25–35, 47, 53–60, 68–77, 101, 134, 155, 198, 240–244, 251, 286, 288; see also computronium and hardware overhang computronium 101, 123–124, 140, 193, 219; see also computing power connectionism 8 consciousness 22, 106, 126, 139, 173–176, 216, 226, 271, 282, 288, 292, 303; see also mind crime control methods 127–144, 145–158, 202, 236–238, 286; see also capability control and motivation selection Copernicus, Nicolaus 14 cosmic endowment 101–104, 115, 134, 209, 214–217, 227, 250, 260, 283, 296 crosswords (solving) 12 cryptographic reward tokens 134, 276 cryptography 80 cyborg 44–48, 67, 270 D DARPA, see Defense Advanced Research Projects Agency DART (tool) 15 Dartmouth Summer Project 5 data mining 15–16, 232, 301 decision support systems 15, 98; see also tool-AI decision theory 10–11, 88, 185–186, 221–227, 280, 298; see also optimality notions decisive strategic advantage 78–89, 95, 104–112, 115–126, 129–138, 148–149, 156–159, 177, 190, 209–214, 225, 252 Deep Blue 12 Deep Fritz 22 Defense Advanced Research Projects Agency (DARPA) 15 design effort, see optimization power Dewey, Daniel 291 Differential Technological Development (Principle of) 230–237 Diffie–Hellman key exchange protocol 80 diminishing returns 37–38, 66, 88, 114, 273, 303 direct reach 58 direct specification 139–143 DNA synthesis 39, 98 Do What I Mean (DWIM) 220–221 domesticity 140–143, 146–156, 187, 191, 207, 222 Drexler, Eric 239, 270, 276, 278, 300 drones 15, 98 Dutch book 111 Dyson, Freeman 101, 278 E economic growth 3, 160–166, 179, 261, 274, 299 Einstein, Albert 56, 70, 85 ELIZA (program) 6 embryo selection 36–44, 67, 268 emulation modulation 207 Enigma code 87 environment of evolutionary adaptedness 164, 171 epistemology 222–224 equation solvers 15 eugenics 36–44, 268, 279 Eurisko 12 evolution 8–9, 23–27, 44, 154, 173–176, 187, 198, 207, 265, 266, 267, 273 evolutionary selection 187, 207, 290 evolvable hardware 154 exhaustive search 6 existential risk 4, 21, 55, 100–104, 115–126, 175, 183, 230–236, 239–254, 256–259, 286, 301–302 state risks 233–234 step risks 233 expert system 7 explicit representation 207 exponential growth, see growth external reference semantics 197 F face recognition 15 failure modes 117–120 Faraday cage 130 Fields Medal 255–256, 272 Fifth-Generation Computer Systems Project 7 fitness function 25; see also evolution Flash Crash (2010) 16–17 formal language 7, 145 FreeCell (game) 13 G game theory 87, 159 game-playing AI 12–14 General Problem Solver 6 genetic algorithms 7–13, 24–27, 237–240; see also evolution genetic selection 37–50, 61, 232–238; see also evolution genie AI 148–158, 285 definition 148 genotyping 37 germline interventions 37–44, 67, 273; see also embryo selection Ginsberg, Matt 12 Go (game) 13 goal-content 109–110, 146, 207, 222–227 Good Old-Fashioned Artificial Intelligence (GOFAI) 7–15, 23 Good, I.


pages: 312 words: 91,538

The Fear Index by Robert Harris

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algorithmic trading, backtesting, banking crisis, dark matter, family office, Fellow of the Royal Society, fixed income, Flash crash, God and Mammon, high net worth, implied volatility, mutually assured destruction, Neil Kinnock, Renaissance Technologies, speech recognition

He took his position next to Quarry, folded his hands on the table in front of him, and stared fixedly at his interlaced fingers. He felt Quarry’s hand grasp his shoulder, the weight increasing as the Englishman rose to his feet. ‘Right then, we can at last get started. So – welcome, friends, to Geneva. It’s almost eight years since Alex and I set up shop together, using his intelligence and my looks, to create a very special kind of investment fund, based exclusively on algorithmic trading. We started with just over a hundred million dollars in assets under management, a big chunk of it courtesy of my old friend over there, Bill Easterbrook, of AmCor – welcome, Bill. We made a profit that first year, and we’ve gone on making a profit every year, which is why we are now one hundred times larger than when we started, with AUM of ten billion dollars. ‘I’m not going to boast about our track record.

Or we can make a prediction, based on twenty years of data, that if tin is at this price and the yen at that, then it is more likely than not that the DAX will be here. Obviously we have vastly more pairs of averages than that to work with – several millions of them – but the principle can be simply stated: the most reliable guide to the future is the past. And we only have to be right about the markets fifty-five per cent of the time to make a profit. ‘When we started out, not many people could have guessed how important algorithmic trading would turn out to be. The pioneers in this business were frequently dismissed as quants, or geeks, or nerds – we were the guys who none of the girls would dance with at parties—’ ‘That’s still true,’ interjected Quarry. Hoffmann waved aside the interruption. ‘Maybe it is, but the successes we have achieved at this firm speak for themselves. Hugo pointed out that in a period when the Dow has declined by nearly twenty-five per cent, we’ve grown in value by eighty-three per cent.


pages: 338 words: 106,936

The Physics of Wall Street: A Brief History of Predicting the Unpredictable by James Owen Weatherall

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Albert Einstein, algorithmic trading, Antoine Gombaud: Chevalier de Méré, Asian financial crisis, bank run, beat the dealer, Benoit Mandelbrot, Black Swan, Black-Scholes formula, Bonfire of the Vanities, Bretton Woods, Brownian motion, butterfly effect, capital asset pricing model, Carmen Reinhart, Claude Shannon: information theory, collateralized debt obligation, collective bargaining, dark matter, Edward Lorenz: Chaos theory, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial innovation, fixed income, George Akerlof, Gerolamo Cardano, Henri Poincaré, invisible hand, Isaac Newton, iterative process, John Nash: game theory, Kenneth Rogoff, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, martingale, Myron Scholes, new economy, Paul Lévy, Paul Samuelson, prediction markets, probability theory / Blaise Pascal / Pierre de Fermat, quantitative trading / quantitative finance, random walk, Renaissance Technologies, risk-adjusted returns, Robert Gordon, Robert Shiller, Robert Shiller, Ronald Coase, Sharpe ratio, short selling, Silicon Valley, South Sea Bubble, statistical arbitrage, statistical model, stochastic process, The Chicago School, The Myth of the Rational Market, tulip mania, V2 rocket, Vilfredo Pareto, volatility smile

Packard stayed with the company for a few more years, serving as CEO until 2003, when he left to start a new company, called ProtoLife. By the time they left, they had made their point: a firm grasp of statistics and a little creative reappropriation of tools from physics were enough to beat the Man. It was time to tackle a new set of problems. Black box models, and more generally “algorithmic trading,” have taken much of the backlash against quantitative finance in the period since the 2007–2008 financial crisis. The negative press is not undeserved. Black box models often work, but by definition it is impossible to pinpoint why they work, or to fully predict when they are going to fail. This means that black box modelers don’t have the luxury of being able to guess when the assumptions that have gone into their models are going to turn bad.

.”: The article I have in mind is Niederhoffer and Osborne (1966); the collaborator was Victor Niederhoffer, the now-(in)famous hedge fund manager. For more on Niederhoffer, see his autobiography, Niederhoffer (1998), or the recent New Yorker profile (Cassidy 2007). “. . . Osborne proposed the first trading program . . .”: In other words, the first systematic, fully deterministic trading strategy that could be programmed into a computer — a system for what today would be called algorithmic trading. The proposal is made in Niederhoffer and Osborne (1966). 3. From Coastlines to Cotton Prices “Szolem Mandelbrojt was the very model . . .”: Information about Mandelbrojt comes from O’Connor and Robertson (2005), as well as from the biographical materials related to Mandelbrot cited below. “In 1950, Benoît Mandelbrot . . .”: Unfortunately, Mandelbrot passed away in 2010, before I had an opportunity to interview him in connection with this book.


pages: 484 words: 104,873

Rise of the Robots: Technology and the Threat of a Jobless Future by Martin Ford

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3D printing, additive manufacturing, Affordable Care Act / Obamacare, AI winter, algorithmic trading, Amazon Mechanical Turk, artificial general intelligence, assortative mating, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Bernie Madoff, Bill Joy: nanobots, call centre, Capital in the Twenty-First Century by Thomas Piketty, Chris Urmson, Clayton Christensen, clean water, cloud computing, collateralized debt obligation, commoditize, computer age, creative destruction, debt deflation, deskilling, diversified portfolio, Erik Brynjolfsson, factory automation, financial innovation, Flash crash, Fractional reserve banking, Freestyle chess, full employment, Goldman Sachs: Vampire Squid, Gunnar Myrdal, High speed trading, income inequality, indoor plumbing, industrial robot, informal economy, iterative process, Jaron Lanier, job automation, John Markoff, John Maynard Keynes: technological unemployment, John von Neumann, Kenneth Arrow, Khan Academy, knowledge worker, labor-force participation, labour mobility, liquidity trap, low skilled workers, low-wage service sector, Lyft, manufacturing employment, Marc Andreessen, McJob, moral hazard, Narrative Science, Network effects, new economy, Nicholas Carr, Norbert Wiener, obamacare, optical character recognition, passive income, Paul Samuelson, performance metric, Peter Thiel, Plutocrats, plutocrats, post scarcity, precision agriculture, price mechanism, Ray Kurzweil, rent control, rent-seeking, reshoring, RFID, Richard Feynman, Richard Feynman, Rodney Brooks, secular stagnation, self-driving car, Silicon Valley, Silicon Valley startup, single-payer health, software is eating the world, sovereign wealth fund, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, Steven Pinker, strong AI, Stuxnet, technological singularity, telepresence, telepresence robot, The Bell Curve by Richard Herrnstein and Charles Murray, The Coming Technological Singularity, The Future of Employment, Thomas L Friedman, too big to fail, Tyler Cowen: Great Stagnation, union organizing, Vernor Vinge, very high income, Watson beat the top human players on Jeopardy!, women in the workforce

Indeed, speed—in some cases measured in millionths or even billionths of a second—is so critical to algorithmic trading success that Wall Street firms have collectively invested billions of dollars to build computing facilities and communications paths designed to produce tiny speed advantages. In 2009, for example, a company called Spread Networks spent as much as $200 million to lay down a new fiber-optic cable link stretching 825 miles in a straight line from Chicago to New York. The company operated in stealth mode so as not to alert the competition even as it blasted its way through the Allegheny Mountains. When the new fiber-optic path came online, it offered a speed advantage of perhaps three or four thousandths of a second compared with existing communications routes. That was enough to allow any algorithmic trading systems employing the new route to effectively dominate their competition.


pages: 357 words: 95,986

Inventing the Future: Postcapitalism and a World Without Work by Nick Srnicek, Alex Williams

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3D printing, additive manufacturing, air freight, algorithmic trading, anti-work, back-to-the-land, banking crisis, basic income, battle of ideas, blockchain, Bretton Woods, call centre, capital controls, carbon footprint, Cass Sunstein, centre right, collective bargaining, crowdsourcing, cryptocurrency, David Graeber, decarbonisation, deindustrialization, deskilling, Doha Development Round, Elon Musk, Erik Brynjolfsson, Ferguson, Missouri, financial independence, food miles, Francis Fukuyama: the end of history, full employment, future of work, gender pay gap, housing crisis, income inequality, industrial robot, informal economy, intermodal, Internet Archive, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, late capitalism, liberation theology, Live Aid, low skilled workers, manufacturing employment, market design, Martin Wolf, mass immigration, mass incarceration, means of production, minimum wage unemployment, Mont Pelerin Society, neoliberal agenda, New Urbanism, Occupy movement, oil shale / tar sands, oil shock, patent troll, pattern recognition, Paul Samuelson, Philip Mirowski, post scarcity, postnationalism / post nation state, precariat, price stability, profit motive, quantitative easing, reshoring, Richard Florida, rising living standards, road to serfdom, Robert Gordon, Ronald Reagan, Second Machine Age, secular stagnation, self-driving car, Slavoj Žižek, social web, stakhanovite, Steve Jobs, surplus humans, the built environment, The Chicago School, The Future of Employment, Tyler Cowen: Great Stagnation, universal basic income, wages for housework, We are the 99%, women in the workforce, working poor, working-age population

These are tasks that computers are perfectly suited to accomplish once a programmer has created the appropriate software, leading to a drastic reduction in the numbers of routine manual and cognitive jobs over the past four decades.22 The result has been a polarisation of the labour market, since many middle-wage, mid-skilled jobs are routine, and therefore subject to automation.23 Across both North America and Western Europe, the labour market is now characterised by a predominance of workers in low-skilled, low-wage manual and service jobs (for example, fast-food, retail, transport, hospitality and warehouse workers), along with a smaller number of workers in high-skilled, high-wage, non-routine cognitive jobs.24 The most recent wave of automation is poised to change this distribution of the labour market drastically, as it comes to encompass every aspect of the economy: data collection (radio-frequency identification, big data); new kinds of production (the flexible production of robots,25 additive manufacturing,26 automated fast food); services (AI customer assistance, care for the elderly); decision-making (computational models, software agents); financial allocation (algorithmic trading); and especially distribution (the logistics revolution, self-driving cars,27 drone container ships and automated warehouses).28 In every single function of the economy – from production to distribution to management to retail – we see large-scale tendencies towards automation.29 This latest wave of automation is predicated upon algorithmic enhancements (particularly in machine learning and deep learning), rapid developments in robotics and exponential growth in computing power (the source of big data) that are coalescing into a ‘second machine age’ that is transforming the range of tasks that machines can fulfil.30 It is creating an era that is historically unique in a number of ways.

See Michael Albert, Parecon: Life After Capitalism (London: Verso, 2004), Part 3. 29.Nick Dyer-Witheford, ‘Red Plenty Platforms’, Culture Machine 14 (2013), p. 13. 30.Measured in terms of floating operations per second, the difference between 1969 and what is expected by 2019 is 107 versus 1018. Ibid., p. 8. Index 1968, 16–7, 63, 188n33 15M, 11, 22 abstraction, 10, 15, 36, 44, 81 additive manufacturing, 110, 143, 150, 182 affect, 7–8, 113–4, 140–1 afro-futurism, 139, 141 AI (artificial intelligence), 110, 143 alienation, 14–5, 82 algorithmic trading, 111 Allende, Salvador, 148, 149 alternativism, 194n95 Althusser, Louis, 81, 141–2 anti-globalisation, 3, 159, 162 anti-war, 3, 5, 22, 162 Apple, 146, Arab Spring, 131, 159 Argentina, 37–9, 173 authenticity, 10–1, 15, 27, 82, 180 automation, 1–2, 86, 88–9, 94–5, 97–8, 104–5, 109–17, 122, 127, 130, 143, 150–1, 167, 171–4, 181–2, 203n15, 212n121, 214n161, 215n9, 218n45 banking, 43–6, 61, 147 Beveridge Report, 118 big data, 110, 111 Bolshevik Revolution, 131 Bolsheviks, 137 Russian Revolution, 139 Brazil, 75, 119, 147, 157, 169 Bretton Woods, 61–2 Brown, Michael, 173 care labour, 113–4 Chicago School, 51, 59–60 Chile, 52, 62, 148, 149, 150 China, 87, 89, 97, 170 class, 14, 16–7, 20–1, 25, 53, 64–5, 87, 91, 96–102, 116, 120, 122–3, 126–7, 132–3, 155–62, 170, 173–4, 189n1, 206n44, 233n119, 233n4, 233n5, 234n18 Cleaver, Eldridge, 91–2 climate change, 13–4, 116 colonialism, 73, 75–6, 96–7, 225n3 common sense, 9–11, 21–2, 40, 54–5, 58–60, 63–7, 72, 131–7 communisation, 92, 225n5 competitive subjects, 63–5, 99, 124 complex systems, 13–4, conspiracy theories, 14–5 cosmism, 139 Critchley, Simon, 72 cryptocurrencies, 143, 182 Cybersyn, 149–150 debt, 9, 22, 35–6, 94 demands, 6–7, 30, 33, 107–8, 130, 159–62, 167 no demands, 7, 34–5, 107, 186n3 non-reformist demands, 108 transitional demands, 215n5 democracy, 31–3, 182 direct democracy, 27–9, 31–3, 164, 190n8 direct action, 6, 11, 27–9, 35–6 education, 64, 99, 104, 141–5, 165–6 Egypt, 32–4, 190n21 energy, 2, 16,19,41, 42–43, 116, 147, 148, 150–51, 164, 171, 178, 179, 182, 183 Engels, Friedrich, 79 Erhard, Ludwig, 57 ethics, 42 work ethic, 124–6 evictions, 8, 12, 36 feminism, 18–21, 122, 138, 161 Fisher, Antony, 58–9, 196n34 food miles, 42–3 fracking, 8 France, 17, 62, 149, 167 free time, 80, 115–6, 120–1, 167, 219n50 freedom, 63–5, 120–1, 126–7, 180–1 negative freedom, 79 synthetic freedom, 78–83 Friedman, Milton, 56, 59–61 full employment, 98–100 future, 1, 71–5, 175–8, 181–3 G20, 6, 94 gender, 21, 41, 90, 122 Germany, 45, 56–7 ghettos, 95–6 Gramsci, Antonio, 132, 165 Graeber, David, 33 grand narratives, 73–4 Great Depression, 46, 65, 99–101, 114–5 Harvey, David, 135 Hayek, Friedrich, 54–6 Holzer, Jenny, 175, 178 horizontalism, 18, 26–39 housing, 8, 28, 35, 48, 77, 80, 95, 96, 148, 159, 167, 168, humanism, 81–3, 180–1 hyperstition, 74–5, 138–9 Iceland, 34, 164 idleness, 85–6 immediacy, 10–1 immigration, 101–2, 161 India, 87, 97–8, 130 inequality, 22, 80, 93–4 informal economy, 95–8, 203n10, 206n44, 210n95 Institute of Economic Affairs, 58–9 Iranian Revolution, 131 Jameson, Fredric, 14, 92, 198n10 Japan, 147 Jimmy Reid Foundation, 117 jobless recovery, 94–5 Jobs, Steve, 179 Johnson, Boris, 172 Kalecki, Michał, 120 Krugman, Paul, 118 labour, 2, 3,9, 17, 20, 21, 33, 38, 48, 52, 58, 61–3, 74, 79, 81, 83, 85–143, 148, 150, 151, 156–8, 161, 163–181, 182 Laclau, Ernesto, 155, 159 Lafargue, Paul, 115, language, 81, 132, 160, 164–5 leisure, 85–6 Leninism, 17, 131, 188n33 Live Aid, 8 localism, 40–6 locavorism, 41–2 Lucas Aerospace, 147 Luxemburg, Rosa, 15 Lyotard, Francois, 73, 74 Manhattan Institute for Policy Research, 58, 59 marches, 6, 30, 49 Marikana massacre, 170 Marinaleda, 48 Marx, Karl, 73, 79, 85, 86, 92, 115, 119, 121, 122, 132, 142, 156, 158, 180 Mattick, Paul, 92, 118 media, 2, 7–8, 31, 36, 52, 58, 60, 63, 67, 88, 118, 125–6, 129, 133–5, 163–5, 176, 182 Mirowski, Philip, 66 modernity, 23, 63, 69–85, 86, 131, 176, 181 modernisation, 23, 60, 63, 137, 174 Mont Pelerin Society, 54, 86, 134, 164, 166 MPS, 55, 56, 58, 66, 67, 134 Move Your Money, 44 Murray, Charles, 59 Musk, Elon, 179 National Union of Rail, Maritime and Transport Workers, 172 negative solidarity, 20, 37 neoliberalism, 3, 12, 20–3, 47, 49, 51–67, 70, 72, 108, 116, 117, 119, 121, 124, 134, 141, 142, 148, 156, 176, 179, 183 neoliberal, 7, 9, 14–16, 20, 21, 37, 47, 49, 73, 93, 99, 118, 126, 127, 129, 131–2, 134, 135, 162, 169, 174, 176, 181 New Economics Foundation, 117, 144 new left, 18–22 New Zealand, 151 occupations, 5, 7, 10, 11, 29–31, 34, 49, 94, 172 Occupy Wall Street, 3, 6, 7, 11, 18, 22, 26, 29–38, 126, 133, 158, 159, 160, 162, 189n1 ordoliberals, 54, 57 organic intellectual, 165–6 Overton Window, 134, 139 Partido dos Trabalhadores, 169 parties, political, 2, 10, 16, 17, 18, 20, 21, 30, 34, 39, 46, 59, 105, 116, 118, 124, 129, 162, 164, 168, 169 personal savings, 94 Piketty, Thomas, 140 Plan C, 117 planning, 1, 15, 56, 141, 142, 149, 151, 182 Plant, Sadie, 82 Podemos, 159, 160, 169 police, 6, 30, 33, 36, 37, 102, 133, 161, 168, 171, 173 postcapitalism, 17, 38, 130, 143, 145, 150, 151, 158, 168, 178, 180 postcapitalist, 12, 15, 16, 32, 34, 83, 109, 115, 126, 136, 143, 145, 150, 152, 153, 157, 179, 180 Post-Crash Economic Society, 143 post-work, 23, 69, 83, 85, 86, 105, 107–127, 129, 130, 138, 140, 141, 153, 155, 156, 158, 161, 163, 164, 167, 174, 175, 176, 177, 178 Pou Chen Group, 170 power, 1, 2, 7, 9, 10, 14, 15, 18–21, 26, 28–30, 33, 36, 43, 46, 48, 49, 59, 61, 62, 65, 73, 78, 79, 80, 81, 87, 88, 93, 100, 108, 111, 116, 120, 123, 127, 130–5, 146, 148, 151, 153, 155–74, 175, 176, 179, 180, 182 precarity, 9, 86, 88, 93, 94, 95, 98, 104, 121, 123, 126, 130, 156, 157, 166, 167, 173, 174 precarious, 2, 64, 117, 129, 167 Precarious Workers Brigade, 117 premature deindustrialisation, 97, 98 primitive accumulation, 87, 89, 90, 96, 97 prison, 90, 102, 103, 119, 133 incarceration, 102, 103, 104, 105, 161 productivity, 74, 88, 97, 110–17, 125, 150, 167 progress, 21, 23, 46, 71–5, 77, 107, 114, 115, 120, 126, 131, 138, 179, 180 protests, 1, 7, 18, 22, 28, 31, 37, 49, 66, 153, 164 psychopathologies, 64 radio-frequency identification, 110 race, 14, 31, 90, 102, 103, 140, 156, 171, 172 Reagan, Ronald, 60, 62, 66, 70 Republican Party (US), 135 resistance, 2, 5, 12, 15, 30, 35, 46–8, 49, 69, 72, 74, 83, 114, 124, 134, 158, 173, 181 Rethinking Economics, 143 Robinson, Joan, 87 Roboticisation, 110, 209n69 mechanisation, 95, 101 Rolling Jubilee, 9 Samuelson, Paul, 142 second machine age, 111 secular stagnation, 143 self-driving cars, 110, 111, 113, 173 shadow work, 115 slavery, 74, 90, 95, 103 slow food, 41, 42 slum, 86, 96–8, 102, 104 social democracy, 3, 17, 46, 66, 70, 167, 176 social democratic, 10, 13, 16, 17, 19, 21, 22, 47, 57, 72, 80, 98, 100, 108, 123, 127, 168 social media, 1, 8, 182 South Africa, 119, 157, 170 Spain, 12, 22, 34, 35, 45, 159, 164 stagflation, 19, 27, 61, 65, 100 Stalinist, 17, 18, 137 strategy, 12, 20, 26, 49, 56, 67, 117, 127, 131–3, 136, 148, 153, 156, 163, 164 strategic, 8, 9, 11, 12, 14, 15, 17, 18, 25, 28, 29, 35, 49, 52, 55, 66, 70, 77, 108, 116, 131, 135, 157, 162, 163, 164, 170, 171, 173, 174 strikes, 9, 10, 28, 36, 37, 116, 120, 157, 167, 170–3 suicide, 94 surplus populations, 40, 86, 88–94, 96–97, 101–3, 104, 105, 120, 130, 166–7, 173, 203n10 Syriza, 159, 160 tactics, 6, 10, 11, 15, 18, 19, 26, 28, 39, 40, 49, 157, 164, 171–4 Tahrir Square, 32, 34 Taylorism, 152 technology, 1, 3, 72, 81, 88, 89, 98, 109, 110, 111, 129, 136, 137, 145–8, 150–3, 178, 179, 182 Thatcher, Margaret, 59, 60, 62, 66, 70, 72, 100 think tanks, 16, 55, 56, 58, 59, 60, 63, 67, 117, 134, 135, 165 trade unions, 10, 27, 47, 59, 61, 62, 71, 105, 116, 117, 124, 129, 148, 162, 166 labour unions, 16, 171 unions, 17, 18, 20, 27, 30, 44 UK Uncut, 126 unemployment, 20, 56, 60, 79, 86–98, 99, 100, 101, 101, 102, 115, 116, 118, 121, 123, 125, 127, 129, 147, 159, 161, 168, 170, 173, 207n44 United Automobile Workers, 170 United Kingdom UK, 8, 20, 40, 42, 45, 52, 54, 56, 58, 61, 62, 92, 93, 94, 117, 118, 126, 144, 147, 151, 172 United States, 8, 18, 29, 36, 44, 45, 59, 62, 78, 92, 95, 103, 114, 118, 123, 133, 135, 138, 167 America, 6, 16, 30, 38, 47, 56, 62, 76, 95, 97, 98, 100, 101, 102, 103, 110, 164 universal basic income, 108, 118, 123, 127, 140, 143 basic income, 80, 108, 118, 119, 120, 121, 122, 123, 124, 127, 129, 130, 140, 143, 164, 165, 167 universalism, 69, 70, 75–8, 83, 119, 132, 175, 197n1, 199n40 USSR, 62, 63, 79, 139 Soviet Union, 57, 70, 74, 139 utopia, 3, 28, 32, 35, 48, 54, 58, 60, 66, 69, 70, 72, 108, 113, 114, 132, 136, 137, 138, 139, 140, 141, 143, 145, 146, 150, 153, 177, 179, 181, 182 vanguard functions, 163 Venezuela, 169 wages, 2, 71, 87, 90, 91, 93, 94, 97, 98, 101, 111, 120, 122, 125, 156, 166, 167 welfare, 14, 38, 57, 59, 61, 62, 63, 64, 71, 73, 90, 100, 101, 103, 105, 118, 119, 122, 124 Wilde, Oscar, 182 withdrawal, 11, 47, 48, 69, 131, 182 exit, 47, 48, 181 escape, 3, 9, 11, 38, 69, 107, 114, 139, 165, 178 work, 1, 2, 16, 17, 23, 32, 36, 41, 44, 47, 64, 71, 85, 86, 90–6, 98, 100, 101, 103–5, 108, 109, 110–7, 120–7, 130, 131, 132, 133, 134, 136, 140, 141, 142, 143, 147, 150, 151, 152, 157, 163, 165, 166, 170, 173, 174, 176, 177, 178, 181 wage labour, 74, 85, 86, 87, 89, 90, 92, 103, 104, 105, 120, 136, 141, 180 job, 2, 38, 41, 47, 48, 63, 64, 79, 85, 86, 88, 89, 90, 93, 94, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 110, 111, 113, 114–23, 124, 125, 126, 129, 147, 148, 161, 166, 167, 171 worker-controlled factories, 38, 39 workfare, 59, 100, 104 World Trade Organisation, 6 World War II, 46, 54, 56, 57, 115, 156 Zapatistas, 11, 22, 26, 35 zero-hours contracts, 93 Žižek, Slavoj, 140 Zuccotti Park, 31, 32


pages: 447 words: 104,258

Mathematics of the Financial Markets: Financial Instruments and Derivatives Modelling, Valuation and Risk Issues by Alain Ruttiens

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algorithmic trading, asset allocation, asset-backed security, backtesting, banking crisis, Black Swan, Black-Scholes formula, Brownian motion, capital asset pricing model, collateralized debt obligation, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, discounted cash flows, discrete time, diversification, fixed income, implied volatility, interest rate derivative, interest rate swap, margin call, market microstructure, martingale, p-value, passive investing, quantitative trading / quantitative finance, random walk, risk/return, Satyajit Das, Sharpe ratio, short selling, statistical model, stochastic process, stochastic volatility, time value of money, transaction costs, value at risk, volatility smile, Wiener process, yield curve, zero-coupon bond

For more details on these models, see the Further Reading section at the end of the chapter. 15.1.6 Neural networks The use of so-called “neural networks”, essentially as a non-parametric forecasting tool, was popular in the 1990s but did not produce convincing results. As such, this section has no “raison d'être”. However, this technique seems to know a new lease of life, with some good reasons, in the successful area of high frequency (“algorithmic”) trading. It is, however, hard to appraise its effectiveness, since its users, in case of positive performance, will most probably not publish on it. In short (for more details, see, e.g., Further Reading), neural networks (hereafter called “NN”) may be defined as tools for non-linear forecasting. To start from the well-known multiple linear regression, considering a series of n, (1, …, j, …, n) data sets {(r1, x11, …, xk1, …, xm1), (r2, x12, …, xk2, …, xm2), …, (rj, x1j, …, xkj, …, xmj), …, (rn, x1n, …, xkn, …, xmn)}, where r is the dependent variable and x1, …, xk, …, xm are the m independent variables, the corresponding multiple linear regression is the straight line (15.2) where a is a constant, wk are the weights and the residue. â and the ŵk are the estimates of a and of the wk, such as they minimize the quadratic residuals With the kind of display used in the NN world, the multi-linear regression can be described as in Figure 15.5, where the transfer function Ψ here is the linear equation 15.2.

Robert TOMKINS, Options Explained, Macmillan Business, 1994, 597 p. Tim WEITHERS, Foreign Exchange, John Wiley & Sons, Inc., Hoboken, 2006, 336 p. Paul WILMOTT (ed.), The Best of Wilmott 2, John Wiley & Sons, Ltd, Chichester, 2005, 404 p. P. WILMOTT, H. RASMUSSEN (eds) New Directions in Mathematical Finance, John Wiley & Sons, Ltd, Chichester, 2002, 256 p. Index 4-moments CAPM actual (ACT) number of days AI see Alternative Investments “algorithmic” trading Alternative Investments (AI) American options bond options CRR pricing model option pricing rho amortizing swaps analytic method, VaR annual interest compounding annualized volatility autocorrelation corrective factor historical volatility risk measures APT see Arbitrage Pricing Theory AR see autoregressive process Arbitrage Pricing Theory (APT) ARCH see autoregressive conditional heteroskedastic process ARIMA see autoregressive integrated moving average process ARMA see autoregression moving average process ask price asset allocation attribution asset swaps ATM see at the money ATMF see at the money forward options at the money (ATM) convertible bonds options at the money forward (ATMF) options attribution asset allocation performance autoregression moving average (ARMA) process autoregressive (AR) process autoregressive conditional heteroskedastic (ARCH) process autoregressive integrated moving average (ARIMA) process backtesting backwardation basket CDSs basket credit derivatives basket options BDT see Black, Derman, Toy process benchmarks Bermudan options Bernardo Ledoit gain-loss ratio BGM model see LIBOR market model BHB model (Brinson’s) bid price binomial distribution binomial models binomial processes, credit derivatives binomial trees Black, Derman, Toy (BDT) process Black and Karasinski model Black–Scholes formula basket options beyond Black–Scholes call-put parity cap pricing currency options “exact” pricing exchange options exotic options floor pricing forward prices futures/forwards options gamma processes hypotheses underlying jump processes moneyness sensitivities example valuation troubles variations “The Black Swan” (Taleb) bond convexity bond duration between two coupon dates calculation assumptions calculation example callable bonds in continuous time duration D effective duration forwards FRNs futures mathematical approach modified duration options physical approach portfolio duration practical approach swaps uses of duration bond futures CFs CTD hedging theoretical price bond options callable bonds convertible bonds putable bonds bond pricing clean vs dirty price duration aspects floating rate bonds inflation-linked bonds risky bonds bonds binomial model CDSs convexity credit derivatives credit risk exotic options forwards futures government bonds options performance attribution portfolios pricing risky/risk-free spot instruments zero-coupon bonds see also bond duration book value method bootstrap method Brinson’s BHB model Brownian motion see also standard Wiener process bullet bonds Bund (German T-bond) 10-year benchmark futures callable bonds call options call-put parity jump processes see also options Calmar ratio Capital Asset Pricing Model (CAPM) 4-moments CAPM AI APT vs CAPM Sharpe capitalization-weighted indexes capital market line (CML) capital markets caplets CAPM see Capital Asset Pricing Model caps carry cash and carry operations cash flows cash settlement, CDSs CBs see convertible bonds CDOs see collateralized debt obligations CDSs see credit default swaps CFDs see contracts for difference CFs see conversion factors charm sensitivity cheapest to deliver (CTD) clean prices clearing houses “close” prices CML see capital market line CMSs see constant maturity swaps Coleman, T.


pages: 502 words: 107,657

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel

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Albert Einstein, algorithmic trading, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, backtesting, Black Swan, book scanning, bounce rate, business intelligence, business process, call centre, commoditize, computer age, conceptual framework, correlation does not imply causation, crowdsourcing, dark matter, data is the new oil, en.wikipedia.org, Erik Brynjolfsson, Everything should be made as simple as possible, experimental subject, Google Glasses, happiness index / gross national happiness, job satisfaction, Johann Wolfgang von Goethe, lifelogging, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, mass immigration, Moneyball by Michael Lewis explains big data, Nate Silver, natural language processing, Netflix Prize, Network effects, Norbert Wiener, personalized medicine, placebo effect, prediction markets, Ray Kurzweil, recommendation engine, risk-adjusted returns, Ronald Coase, Search for Extraterrestrial Intelligence, self-driving car, sentiment analysis, software as a service, speech recognition, statistical model, Steven Levy, text mining, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Turing test, Watson beat the top human players on Jeopardy!, X Prize, Yogi Berra, zero-sum game

Financial institutions: Don Davey, “Collect More for Less: Strategic Predictive Analytics Is Key to Growing Collections and Reducing Costs,” First Data White Paper, April 2009. www.firstdata.com/downloads/thought-leadership/fd_collectmoreforless_whitepaper.pdf. London Stock Exchange: “Black Box Traders Are on the March,” The Telegraph, August 27, 2006. www.telegraph.co.uk/finance/2946240/Black-box-traders-are-on-the-march.html#disqus_thread. Kendall Kim, Electronic and Algorithmic Trading Technology (Elsevier, 2007) www.elsevier.com/wps/find/bookdescription.cws_home/711644/description#description. John Elder: See Chapter 1 for this case study. For a short autobiographical essay by John Elder, see: Mohamed Medhat Gaber, Journeys to Data Mining: Experiences from 15 Renowned Researchers (Springer, 2012), 61–76. Various firms: Dave Andre, PhD, Cerebellum Capital, “Black Box Trading: Analytics in the Land of the Vampire Squid,” Predictive Analytics World San Francisco Conference, March 14, 2011, San Francisco, CA. www.predictiveanalyticsworld.com/sanfrancisco/2011/agenda.php#day1–8a.

The “n” after a page number refers to an entry that appears in a footnote on that page. A Abbott, Dean ABC AB testing Accident Fund Insurance actuarial approach advertisement targeting, predictive advertising. See marketing and advertising Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age (Harcourt) AI. See artificial intelligence (AI) airlines and aviation, predicting in Albee, Edward Albrecht, Katherine algorithmic trading. See black box trading Allen, Woody Allstate AlphaGenius Amazon.com employee security access needs machine learning and predictive models Mechanical Turk personalized recommendations sarcasm in reviews American Civil Liberties Union (ACLU) American Public University System Ansari X Prize Anxiety Index calculating as ensemble model measuring in blogs Apollo 11 Apple, Inc.


pages: 903 words: 235,753

The Stack: On Software and Sovereignty by Benjamin H. Bratton

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1960s counterculture, 3D printing, 4chan, Ada Lovelace, additive manufacturing, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, basic income, Benevolent Dictator For Life (BDFL), Berlin Wall, bioinformatics, bitcoin, blockchain, Buckminster Fuller, Burning Man, call centre, carbon footprint, carbon-based life, Cass Sunstein, Celebration, Florida, charter city, clean water, cloud computing, connected car, corporate governance, crowdsourcing, cryptocurrency, dark matter, David Graeber, deglobalization, dematerialisation, disintermediation, distributed generation, don't be evil, Douglas Engelbart, Douglas Engelbart, Edward Snowden, Elon Musk, en.wikipedia.org, Eratosthenes, ethereum blockchain, facts on the ground, Flash crash, Frank Gehry, Frederick Winslow Taylor, future of work, Georg Cantor, gig economy, global supply chain, Google Earth, Google Glasses, Guggenheim Bilbao, High speed trading, Hyperloop, illegal immigration, industrial robot, information retrieval, Intergovernmental Panel on Climate Change (IPCC), intermodal, Internet of things, invisible hand, Jacob Appelbaum, Jaron Lanier, John Markoff, Jony Ive, Julian Assange, Khan Academy, liberal capitalism, lifelogging, linked data, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, Masdar, McMansion, means of production, megacity, megastructure, Menlo Park, Minecraft, Monroe Doctrine, Network effects, new economy, offshore financial centre, oil shale / tar sands, packet switching, PageRank, pattern recognition, peak oil, peer-to-peer, performance metric, personalized medicine, Peter Eisenman, Peter Thiel, phenotype, Philip Mirowski, Pierre-Simon Laplace, place-making, planetary scale, RAND corporation, recommendation engine, reserve currency, RFID, Robert Bork, Sand Hill Road, self-driving car, semantic web, sharing economy, Silicon Valley, Silicon Valley ideology, Slavoj Žižek, smart cities, smart grid, smart meter, social graph, software studies, South China Sea, sovereign wealth fund, special economic zone, spectrum auction, Startup school, statistical arbitrage, Steve Jobs, Steven Levy, Stewart Brand, Stuxnet, Superbowl ad, supply-chain management, supply-chain management software, TaskRabbit, the built environment, The Chicago School, the scientific method, Torches of Freedom, transaction costs, Turing complete, Turing machine, Turing test, universal basic income, urban planning, Vernor Vinge, Washington Consensus, web application, Westphalian system, WikiLeaks, working poor, Y Combinator

Its infrastructural profile contains all of these qualities of the earth at once, each of them dependent on the others. It smooths space by striating it with heavy physical grids of cables and server farms, and striates space by smoothing it out with ubiquitous access, sensing, relay, and processing micropoints. For its chthonic Cloud, data centers are housed under mountains with reliable ice cores; suburban farmland between metropolitan trading centers is redug to lay private cable for algorithmic trading concerns near the old AT&T switches in New Jersey, realizing a new topographic expression of the transport layer of the TCP/IP stack; while the wireless frequency spectrum is subdivided, auctioned, allocated, and bundled into derivatives like any other prized commercial real estate. Whereas the Schmittian “grounded” way of thinking detests dedifferentiated space and the flattening superimposition of multiple maps, valorizing instead the perspectival spatial order of human establishment, the geographies of The Stack go a long way toward collapsing distinctions between the one and the other, as its interlacing of land, sea, and air through networks of recombinant flows realizes the simultaneous physicalization of the virtual and the virtualization of physical forces.

We've suggested that the recent financial crisis was also a crisis of addressability in that the kaleidoscopic nesting of asset debt inside collateralized futures inside options and so on not only allowed contagion to spread without quarantine, but that the absence of any reliable map of this haunted house of intertextual valuation made untangling the rot from the flesh all but impossible. The redesign of money—not just the currency vehicle of exchange, but of the valuation of things and events as such—may also require, or even entail, a more rigorous, flexible, and intricate mechanism for the identification of discrete assets as they twist and turn their way through financial wonderlands. What we now call “high-frequency trading” or algorithmic trading may continue to represent an increasingly larger percentage of all transactions, and as these techniques become more institutionalized, their methodologies and mechanisms become more normalized for even long-term investments. At the same time, the ability for deep address to engender not one but multiple address topologies describing the same set of events means that the potential for unprosecutable chaos is increased unless there are some workable standards for financial singularities, bifurcators, and resolvers that can police these data ontologies.

See also political agency amplifying, 274 collective versus individual, 175 decentering, 344 of the excluded, 173–175 human, 255 of inanimate objects, 131 of machines, 348 political, 250, 258 of the User, 164–165, 238, 252, 258, 260, 338, 347–348 utopian, 249 Agenda 21, 89, 306 agonistic geopolitics, 115 agricultural industry, 307–308 agricultural settlement, 22, 86 Ain, Gregory, 320 airports borders within, 155–156, 324 economy of, 281 envelopes of, 156–157 interfacial network of, 155–156 airport urbanism, 155–157, 162, 405n14 Alberti, Leon Battista, 154 alegal, 174, 176, 367 Aleppo, Syria, 321 Alexander, Christopher, 201 Algerian independence, 244–245 algorithmic automation, 134, 332, 341–342 algorithmic capitalism, 72, 80–81 algorithmic decision-making, 134, 332, 341–342 algorithmic geopolitics, 449n56 algorithmic governance, 134, 332–334, 337–338, 341–342, 348, 368 algorithmic hardware, 348–349 algorithmic intelligence, 81 algorithmic trading, 33, 335 Allende, Salvador, 58, 328 Allianz Arena, 187 Al Nasr, 321 Alphaville (Godard), 158 Althusser, Louis, 7–8 Amazon Cloud platform, 185–186, 330 Cloud Polis, 131–133, 331 corporate campus, 185 fulfillment centers, 111, 186, 443n19 future possibilities for, 141–142, 186 as geopolitical model, 125 mission, 186 platform wars, 110 profitability, 331, 449n52 workforce, 186, 307, 443n19 Amazon space, 443n19 Amazon Standard Identifier, 131 Amazon Web Services, 110, 123, 133 ambient emergency, 70–72 ambient interface, 296, 337–343, 368 anamnesis, 239–240, 297 Anders, William, 86 Anderson, Chris, 293 animal-human distinction, 275 animal-human interface, 276 animal User, 274–277 anonymity, 347, 360, 362, 405n16, 445n37 Anonymous, 110 Anthropocene architecture's response to, 182 challenge of, 78, 304, 353–354 life forms surviving, 107 planetary-scale computation surviving, 217, 302 post-Anthropocene, 364–365 anthropocentrism, 278 anthropometric design, 197 anthropometric space, 30 anthropomorphism, 277, 279 anti-cosmopolitanism, 248, 306 Anti-Fascist Protection Wall, 23 Antonov 225 airplane, 182 ants communications, 340 war machine, 352 zombie, 154 Aozaki, Nobutaka, 214–215 Apollo 8, 86–87, 144, 150, 251–252, 300.


pages: 483 words: 141,836

Red-Blooded Risk: The Secret History of Wall Street by Aaron Brown, Eric Kim

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activist fund / activist shareholder / activist investor, Albert Einstein, algorithmic trading, Asian financial crisis, Atul Gawande, backtesting, Basel III, Bayesian statistics, beat the dealer, Benoit Mandelbrot, Bernie Madoff, Black Swan, capital asset pricing model, central bank independence, Checklist Manifesto, corporate governance, creative destruction, credit crunch, Credit Default Swap, disintermediation, distributed generation, diversification, diversified portfolio, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, experimental subject, financial innovation, illegal immigration, implied volatility, index fund, Long Term Capital Management, loss aversion, margin call, market clearing, market fundamentalism, market microstructure, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, Myron Scholes, natural language processing, open economy, Pierre-Simon Laplace, pre–internet, quantitative trading / quantitative finance, random walk, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, road to serfdom, Robert Shiller, Robert Shiller, shareholder value, Sharpe ratio, special drawing rights, statistical arbitrage, stochastic volatility, The Myth of the Rational Market, Thomas Bayes, too big to fail, transaction costs, value at risk, yield curve

The standard introduction to quantitative finance is Paul Wilmott Introduces Quantitative Finance by, of course, Paul Wilmott. A few years ago Paul told me he had ceased being a person and had transformed into a brand. Antti Ilmanen wrote an excellent guide to the theory and practice of quant strategies, Expected Returns: An Investor’s Guide to Harvesting Market Rewards. More technical works on the subject are Algorithmic Trading and DMA: An Introduction to Direct Access Trading Strategies by Barry Johnson, Inside the Black Box: The Simple Truth about Quantitative Trading by Rishi K. Narang, and Multifractal Volatility: Theory, Forecasting, and Pricing by Laurent E. Calvet. The view of quantitative finance described in Red-Blooded Risk has a lot of overlap with two pathbreaking but eccentric works: The Handbook of Portfolio Mathematics: Formulas for Optimal Allocation & Leverage by Ralph Vince and Finding Alpha: The Search for Alpha When Risk and Return Break Down by Eric Falkenstein.

“It’s been a real privilege to work with Aaron Brown on this project. Comics has always been my passion, and utilizing the medium to help explain economic concepts is a contribution that I’m happy to make. I hope readers enjoy the works presented here, and hope we get a chance to collaborate again!” Index Accuracy ratio Adams, Brandon Aggregation process Ainsley, Craig Algorithmic Trading and DMA ( Johnson) Algren, Nelson All the Devils Are Here (McLean and Nocera) Allen, Franklin Anderson, Chris Anomalies Anthropology of Economy, The (Gudeman) Arbitrage Ariely, Dan Ars Conjectandi (Bernoulli) Augar, Philip Autocorrelation Bacharach, Michael Back office Backward-looking risk management Bad-boy publicity Bad Bet (O’Brien) Bankers Trust Bank for International Settlements Bank One Barter, Exchange and Value (Humphrey and Hugh-Jones) Basel capital rules Bayes, Thomas Bayesians/Bayesian concepts.


pages: 481 words: 120,693

Plutocrats: The Rise of the New Global Super-Rich and the Fall of Everyone Else by Chrystia Freeland

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activist fund / activist shareholder / activist investor, Albert Einstein, algorithmic trading, assortative mating, banking crisis, barriers to entry, Basel III, battle of ideas, Bernie Madoff, Big bang: deregulation of the City of London, Black Swan, Branko Milanovic, Bretton Woods, BRICs, business climate, call centre, carried interest, Cass Sunstein, Clayton Christensen, collapse of Lehman Brothers, commoditize, conceptual framework, corporate governance, creative destruction, credit crunch, Credit Default Swap, crony capitalism, Deng Xiaoping, don't be evil, double helix, energy security, estate planning, experimental subject, financial deregulation, financial innovation, Flash crash, Frank Gehry, Gini coefficient, global village, Goldman Sachs: Vampire Squid, Gordon Gekko, Guggenheim Bilbao, haute couture, high net worth, income inequality, invention of the steam engine, job automation, John Markoff, joint-stock company, Joseph Schumpeter, knowledge economy, knowledge worker, liberation theology, light touch regulation, linear programming, London Whale, low skilled workers, manufacturing employment, Mark Zuckerberg, Martin Wolf, Mikhail Gorbachev, Moneyball by Michael Lewis explains big data, NetJets, new economy, Occupy movement, open economy, Peter Thiel, place-making, Plutocrats, plutocrats, Plutonomy: Buying Luxury, Explaining Global Imbalances, postindustrial economy, Potemkin village, profit motive, purchasing power parity, race to the bottom, rent-seeking, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, self-driving car, short selling, Silicon Valley, Silicon Valley startup, Simon Kuznets, Solar eclipse in 1919, sovereign wealth fund, stem cell, Steve Jobs, the new new thing, The Spirit Level, The Wealth of Nations by Adam Smith, Tony Hsieh, too big to fail, trade route, trickle-down economics, Tyler Cowen: Great Stagnation, wage slave, Washington Consensus, winner-take-all economy, zero-sum game

Finally, these two big revolutions, together with a broader global trend toward more open markets in money, goods, and ideas, combine to reinforce each other and create a faster-paced, more volatile world. Twitter and Facebook are the offspring of the technology revolution, but they turn out to have made political revolutions easier to organize. Before the invention of the personal computer, the securitization of mortgages—which turned out to be part of the kindling for the financial crisis—would not have been possible. Nor would the algorithmic trading revolution, in which machines are replacing centuries-old stock exchanges and a couple of lines of corrupt code can trigger a multibillion-dollar loss of market value in moments, as occurred during the “flash crash” on May 6, 2010. — Revolution is the new global status quo, but not everyone is good at responding to it. My shorthand for the archetype best equipped to deal with it is “Harvard kids who went to provincial public schools.”

Note that not all terms may be searchable. Abramovich, Roman, 107 Abrams, Dan, 164 academics, 264, 267–69 Acemoglu, Daron, 21–22, 279–80 Ackermann, Josef, 254 Ackman, Bill, 51 active inertia, 145, 167–69 actors, 108 Ad Age, 50 Adderall, 52 Africa, 66, 76, 146 agency problem, 138 AIDS, 76, 246 AIG, 27, 101, 143 Akhmetov, Rinat, 103 Alger, Horatio, 45 algorithmic trading, 146 Allen, Herb, 68 Allen, Paul, 43 Allstate, 64 Alpha, 143 alpha geeks, 46–51, 92, 94 Amazon, 69, 234 Ambani, Mukesh, 199 Ambani family, 235 American Bar Association Journal, 107 American colonies, 11–12 Anderson, Chris, 68, 100 Anderson, Keith, 153–55 Andersson, Mats, 138 Animal Farm (Orwell), 90 Apollo, 142 Apple, 25, 54, 55, 104, 143 iPhone, 28 iPod, 24–26 Applied Materials, 64 Arcelor, 191 ArcelorMittal, 59 architects, 103–4 Ariely, Dan, xii, 273–74 ARK (Absolute Return for Kids), 74 Arkady, 110 Arthur, W.


pages: 481 words: 125,946

What to Think About Machines That Think: Today's Leading Thinkers on the Age of Machine Intelligence by John Brockman

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3D printing, agricultural Revolution, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, artificial general intelligence, augmented reality, autonomous vehicles, basic income, bitcoin, blockchain, clean water, cognitive dissonance, Colonization of Mars, complexity theory, computer age, computer vision, constrained optimization, corporate personhood, cosmological principle, cryptocurrency, cuban missile crisis, Danny Hillis, dark matter, discrete time, Douglas Engelbart, Elon Musk, Emanuel Derman, endowment effect, epigenetics, Ernest Rutherford, experimental economics, Flash crash, friendly AI, functional fixedness, Google Glasses, hive mind, income inequality, information trail, Internet of things, invention of writing, iterative process, Jaron Lanier, job automation, John Markoff, John von Neumann, Kevin Kelly, knowledge worker, loose coupling, microbiome, Moneyball by Michael Lewis explains big data, natural language processing, Network effects, Norbert Wiener, pattern recognition, Peter Singer: altruism, phenotype, planetary scale, Ray Kurzweil, recommendation engine, Republic of Letters, RFID, Richard Thaler, Rory Sutherland, Satyajit Das, Search for Extraterrestrial Intelligence, self-driving car, sharing economy, Silicon Valley, Skype, smart contracts, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, strong AI, Stuxnet, superintelligent machines, supervolcano, the scientific method, The Wisdom of Crowds, theory of mind, Thorstein Veblen, too big to fail, Turing machine, Turing test, Von Neumann architecture, Watson beat the top human players on Jeopardy!, Y2K

Therefore, the way our society deals right now with superhuman trading algorithms may offer a blueprint for future interactions with more general artificial intelligence. Among many other examples, today’s market circuit breakers may eventually generalize to future centralized abilities to cut off AIs from the outside world, and today’s large trader reporting rules may generalize to future requirements that advanced AIs be licensed and registered with the government. Through this lens, calls for stricter regulation of high-frequency algorithmic trading by slower human traders can be viewed as some of humanity’s earliest attempts to close a nascent “intelligence divide” with thinking machines. But how can we prevent a broader intelligence divide? Michael Faraday was apocryphally said to have been asked in 1850 by a skeptical British chancellor of the exchequer about the utility of electricity and to have responded, “Why, sir, there is every probability that you will soon be able to tax it.”

Similarly, if wealth is just a measure of freedom, and intelligence is just an engine of freedom maximization, intelligence divides could be addressed with progressive intelligence taxes. While taxing intelligence would be a novel method for mitigating the decoupling of human and machine economies, the decoupling problem will nonetheless require creative solutions. Already, in the high-frequency trading realm, there’s a sub-500-ms economy occupied by algorithms trading primarily among themselves and an above-500-ms economy occupied by everyone else. This example serves as a reminder that while spatial economic decoupling (e.g., between countries at different stages of development) has occurred for millennia, artificial intelligence is for the first time enabling temporal decoupling as well. Such decoupling arguably persists because the majority of the human economy still lives in a physical world not yet programmable with low latencies.


pages: 606 words: 157,120

To Save Everything, Click Here: The Folly of Technological Solutionism by Evgeny Morozov

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3D printing, algorithmic trading, Amazon Mechanical Turk, Andrew Keen, augmented reality, Automated Insights, Berlin Wall, big data - Walmart - Pop Tarts, Buckminster Fuller, call centre, carbon footprint, Cass Sunstein, choice architecture, citizen journalism, cloud computing, cognitive bias, creative destruction, crowdsourcing, data acquisition, Dava Sobel, disintermediation, East Village, en.wikipedia.org, Fall of the Berlin Wall, Filter Bubble, Firefox, Francis Fukuyama: the end of history, frictionless, future of journalism, game design, Gary Taubes, Google Glasses, illegal immigration, income inequality, invention of the printing press, Jane Jacobs, Jean Tirole, Jeff Bezos, jimmy wales, Julian Assange, Kevin Kelly, Kickstarter, license plate recognition, lifelogging, lone genius, Louis Pasteur, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, moral panic, Narrative Science, Nicholas Carr, packet switching, PageRank, Parag Khanna, Paul Graham, peer-to-peer, Peter Singer: altruism, Peter Thiel, pets.com, placebo effect, pre–internet, Ray Kurzweil, recommendation engine, Richard Thaler, Ronald Coase, Rosa Parks, self-driving car, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, Slavoj Žižek, smart meter, social graph, social web, stakhanovite, Steve Jobs, Steven Levy, Stuxnet, technoutopianism, the built environment, The Chicago School, The Death and Life of Great American Cities, the medium is the message, The Nature of the Firm, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas L Friedman, transaction costs, urban decay, urban planning, urban sprawl, Vannevar Bush, WikiLeaks

If only data about reported crimes are used to predict future crimes and guide police work, some types of crime might be left unstudied—and thus unpursued. What to do about the algorithms then? It is a rare thing to say these days but there is much to learn from the financial sector in this regard. For example, after a couple of disasters caused by algorithmic trading in August 2012, financial authorities in Hong Kong and Australia drafted proposals to establish regular independent audits of the design, development, and modification of the computer systems used for algorithmic trading. Thus, just as financial auditors could attest to a company’s balance sheet, algorithmic auditors could verify if its algorithms are in order. As algorithms are further incorporated into our daily lives—from Google’s Autocomplete to PredPol—it seems prudent to subject them to regular investigations by qualified and ideally public-spirited third parties.


pages: 515 words: 132,295

Makers and Takers: The Rise of Finance and the Fall of American Business by Rana Foroohar

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3D printing, accounting loophole / creative accounting, activist fund / activist shareholder / activist investor, additive manufacturing, Airbnb, algorithmic trading, Alvin Roth, Asian financial crisis, asset allocation, bank run, Basel III, bonus culture, Bretton Woods, British Empire, call centre, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, carried interest, centralized clearinghouse, clean water, collateralized debt obligation, commoditize, computerized trading, corporate governance, corporate raider, corporate social responsibility, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, crowdsourcing, David Graeber, deskilling, Detroit bankruptcy, diversification, Double Irish / Dutch Sandwich, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial deregulation, financial intermediation, Frederick Winslow Taylor, George Akerlof, gig economy, Goldman Sachs: Vampire Squid, Gordon Gekko, greed is good, High speed trading, Home mortgage interest deduction, housing crisis, Howard Rheingold, Hyman Minsky, income inequality, index fund, information asymmetry, interest rate derivative, interest rate swap, Internet of things, invisible hand, John Markoff, joint-stock company, joint-stock limited liability company, Kenneth Rogoff, knowledge economy, labor-force participation, labour mobility, London Whale, Long Term Capital Management, manufacturing employment, market design, Martin Wolf, money market fund, moral hazard, mortgage debt, mortgage tax deduction, new economy, non-tariff barriers, offshore financial centre, oil shock, passive investing, Paul Samuelson, pensions crisis, Ponzi scheme, principal–agent problem, quantitative easing, quantitative trading / quantitative finance, race to the bottom, Ralph Nader, Rana Plaza, RAND corporation, random walk, rent control, Robert Shiller, Robert Shiller, Ronald Reagan, Satyajit Das, Second Machine Age, shareholder value, sharing economy, Silicon Valley, Silicon Valley startup, Snapchat, sovereign wealth fund, Steve Jobs, technology bubble, The Chicago School, the new new thing, The Spirit Level, The Wealth of Nations by Adam Smith, Tim Cook: Apple, Tobin tax, too big to fail, trickle-down economics, Tyler Cowen: Great Stagnation, Vanguard fund, zero-sum game

“It used to be that people would come here after a physics or math degree, but now they are coming straight from economics-focused math programs [at the undergraduate level]. It’s become a trajectory.” Derman, who himself left a job at Bell Labs to make more money on the Street, understands the lure of a high-paid algorithmic trading job for students who will often end up with hundreds of thousands of dollars in college and graduate school loan debt—a Wall Street career is often the only quick way to financial solvency. But unlike many finance professors, he also tries to engender in his students a sense that algorithmic trading models are just one tool in the banker’s toolbox and shouldn’t be overrelied upon. Whether the students are listening is another question. It’s telling that about 80 percent of Derman’s students are now Asian, many of them Chinese, who are bringing the game of financial speculation to their own economies.


pages: 428 words: 121,717

Warnings by Richard A. Clarke

active measures, Albert Einstein, algorithmic trading, anti-communist, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, Bernie Madoff, cognitive bias, collateralized debt obligation, complexity theory, corporate governance, cuban missile crisis, data acquisition, discovery of penicillin, double helix, Elon Musk, failed state, financial thriller, fixed income, Flash crash, forensic accounting, friendly AI, Intergovernmental Panel on Climate Change (IPCC), Internet of things, James Watt: steam engine, Jeff Bezos, John Maynard Keynes: Economic Possibilities for our Grandchildren, knowledge worker, Maui Hawaii, megacity, Mikhail Gorbachev, money market fund, mouse model, Nate Silver, new economy, Nicholas Carr, nuclear winter, pattern recognition, personalized medicine, phenotype, Ponzi scheme, Ray Kurzweil, Richard Feynman, Richard Feynman, Richard Feynman: Challenger O-ring, risk tolerance, Ronald Reagan, Search for Extraterrestrial Intelligence, self-driving car, Silicon Valley, smart grid, statistical model, Stephen Hawking, Stuxnet, technological singularity, The Future of Employment, the scientific method, The Signal and the Noise by Nate Silver, Tunguska event, uranium enrichment, Vernor Vinge, Watson beat the top human players on Jeopardy!, women in the workforce, Y2K

Michael Sainato, “Steven Hawking, Elon Musk, and Bill Gates Warn About Artificial Intelligence,” The Observer (UK), Aug. 19, 2015, http://observer.com/2015/08/stephen-hawking-elon-musk-and-bill-gates-warn-about-artificial-intelligence (accessed Oct. 8, 2016); and Elon Musk interview with MIT students at the MIT Aeronautics and Astronautics Department Centennial Symposium, Oct. 2014, http://aeroastro.mit.edu/aeroastro100/centennial-symposium (accessed Oct. 8, 2016). 22. Bloomberg via Shobhit Seth, “The World of High Frequency Algorithmic Trading,” Investopedia, Sept. 16, 2015, www.investopedia.com/articles/investing/091615/world-high-frequency-algorithmic-trading.asp (accessed Oct. 8, 2016). 23. Andrew Ng, “Is A.I. an Existential Threat to Humanity?” Quora, https://www.quora.com/Is-AI-an-existential-threat-to-humanity/answer/Andrew-Ng (accessed Oct. 8, 2016). 24. The study looks at jobs at risk from weak AI and robotics. Carl Benedikt Frey and Michael A. Osbourne, “The Future of Employment: How Susceptible Are Jobs to Computerisation?”


pages: 184 words: 53,625

Future Perfect: The Case for Progress in a Networked Age by Steven Johnson

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Airbus A320, airport security, algorithmic trading, banking crisis, barriers to entry, Bernie Sanders, call centre, Captain Sullenberger Hudson, Cass Sunstein, cognitive dissonance, credit crunch, crowdsourcing, dark matter, Dava Sobel, David Brooks, Donald Davies, future of journalism, hive mind, Howard Rheingold, HyperCard, Jane Jacobs, John Gruber, John Harrison: Longitude, Kevin Kelly, Kickstarter, lone genius, Mark Zuckerberg, mega-rich, meta analysis, meta-analysis, Naomi Klein, Nate Silver, Occupy movement, packet switching, peer-to-peer, Peter Thiel, planetary scale, pre–internet, RAND corporation, risk tolerance, shareholder value, Silicon Valley, Silicon Valley startup, social graph, Steve Jobs, Steven Pinker, Stewart Brand, The Death and Life of Great American Cities, Tim Cook: Apple, urban planning, US Airways Flight 1549, WikiLeaks, William Langewiesche, working poor, X Prize, your tax dollars at work

If someone had been able to show me footage of the Occupy Wall Street protests back when I was writing those lines in Emergence, I would have nodded knowingly: Yep, that’s our future. But those same affordances for cheap and fast information triggered a vast array of outcomes that I failed to anticipate. And not just the Al Qaeda attacks. The Internet may well have made it easier for Occupy Wall Street to form, but it had an even more decisive role in the creation of high-frequency algorithmic trading, which has spawned both immense fortunes and immense instability on Wall Street. Global movements comparable to Occupy Wall Street formed many times before the age of networked computing, as Gladwell observed; they might have had a harder time reaching critical mass without the speed and efficiency of the Net, but they were at least within the realm of possibility. But high-frequency trades are literally impossible to execute in a world without networked computers.


pages: 133 words: 42,254

Big Data Analytics: Turning Big Data Into Big Money by Frank J. Ohlhorst

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algorithmic trading, bioinformatics, business intelligence, business process, call centre, cloud computing, create, read, update, delete, data acquisition, DevOps, fault tolerance, linked data, natural language processing, Network effects, pattern recognition, performance metric, personalized medicine, RFID, sentiment analysis, six sigma, smart meter, statistical model, supply-chain management, Watson beat the top human players on Jeopardy!, web application

The biomedical corporation Bioinformatics recently announced that it has reduced the time it takes to sequence a genome from years to days, and it has also reduced the cost, so it will be feasible to sequence an individual’s genome for $1,000, paving the way for improved diagnostics and personalized medicine. The financial sector has seen how Big Data and its associated analytics can have a disruptive impact on business. Financial services firms are seeing larger volumes through smaller trading sizes, increased market volatility, and technological improvements in automated and algorithmic trading. DATA AND DATA ANALYSIS ARE GETTING MORE COMPLEX One of the surprising outcomes of the Big Data paradigm is the shift of where the value can be found in the data. In the past, there was an inherent hypothesis that the bulk of value could be found in structured data, which usually constitute about 20 percent of the total data stored. The other 80 percent of data is unstructured in nature and was often viewed as having limited or little value.


pages: 222 words: 53,317

Overcomplicated: Technology at the Limits of Comprehension by Samuel Arbesman

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3D printing, algorithmic trading, Anton Chekhov, Apple II, Benoit Mandelbrot, citation needed, combinatorial explosion, Danny Hillis, David Brooks, digital map, discovery of the americas, en.wikipedia.org, Erik Brynjolfsson, Flash crash, friendly AI, game design, Google X / Alphabet X, Googley, HyperCard, Inbox Zero, Isaac Newton, iterative process, Kevin Kelly, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, mandelbrot fractal, Minecraft, Netflix Prize, Nicholas Carr, Parkinson's law, Ray Kurzweil, recommendation engine, Richard Feynman, Richard Feynman, Richard Feynman: Challenger O-ring, Second Machine Age, self-driving car, software studies, statistical model, Steve Jobs, Steve Wozniak, Steven Pinker, Stewart Brand, superintelligent machines, Therac-25, Tyler Cowen: Great Stagnation, urban planning, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, Y2K

We already see hints of the endpoint toward which we are hurtling: a world where nearly self-contained technological ecosystems operate outside of human knowledge and understanding. As a journal article in Scientific Reports in September 2013 put it, there is a complete “new machine ecology beyond human response time”—and this paper was talking only about the financial world. Stock market machines interact with one another in rich ways, essentially as algorithms trading among themselves, with humans on the sidelines. This book argues that there are certain trends and forces that overcomplicate our technologies and make them incomprehensible, no matter what we do. These forces mean that we will have more and more days like July 8, 2015, when the systems we think of as reliable come crashing down in inexplicable glitches. As a complexity scientist, I spend a lot of time being preoccupied with the rapidly increasing complexity of our world.


pages: 202 words: 66,742

The Payoff by Jeff Connaughton

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algorithmic trading, bank run, banking crisis, Bernie Madoff, collapse of Lehman Brothers, collateralized debt obligation, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, cuban missile crisis, desegregation, Flash crash, locking in a profit, London Interbank Offered Rate, London Whale, Long Term Capital Management, naked short selling, Neil Kinnock, Plutocrats, plutocrats, Ponzi scheme, risk tolerance, Robert Bork, short selling, Silicon Valley, too big to fail, two-sided market, young professional

And when you go swimming in this market, you’d better remember there’s nobody out there making sure the water is safe.” The flash crash taught at least three lessons, all of which Ted had identified long before May 6, 2010. First, stock prices don’t always reflect the market’s best estimation of the value of the underlying companies; in mini flash crashes, they can result from the breakdown of algorithmic trading strategies. Second, technology has far outpaced regulation. Regulators’ lack of understanding of HFT strategies and the volatility they create left the markets vulnerable to a nausea-inducing plunge. For example, the SEC took for granted that high-frequency traders were the new market makers without taking into account the ways in which they differed from traditional market makers. Not only did the speed of HFT algorithms cripple the markets in a matter of minutes, but the absence of true market makers to guarantee two-sided markets in times of high volatility created an enormous liquidity shortage.


pages: 282 words: 80,907

Who Gets What — and Why: The New Economics of Matchmaking and Market Design by Alvin E. Roth

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Affordable Care Act / Obamacare, Airbnb, algorithmic trading, barriers to entry, Berlin Wall, bitcoin, Build a better mousetrap, centralized clearinghouse, Chuck Templeton: OpenTable, commoditize, computer age, computerized markets, crowdsourcing, deferred acceptance, desegregation, experimental economics, first-price auction, Flash crash, High speed trading, income inequality, Internet of things, invention of agriculture, invisible hand, Jean Tirole, law of one price, Lyft, market clearing, market design, medical residency, obamacare, proxy bid, road to serfdom, school choice, sealed-bid auction, second-price auction, second-price sealed-bid, Silicon Valley, spectrum auction, Spread Networks laid a new fibre optics cable between New York and Chicago, Steve Jobs, The Wealth of Nations by Adam Smith, two-sided market

In just four minutes, the prices of futures and of the related SPY exchange-traded funds (as well as many of the stocks in the index) were driven down by several percentage points—a very big move, in the absence of earth-shattering news—and then recovered almost as fast. A subsequent investigation by the Securities and Exchange Commission and the Commodity Futures Trading Commission suggested that this brief distortion resulted from high-speed computer algorithms trading with one another, at a speed that eluded human supervision, and briefly spun out of control before anyone could react. In the aftermath of this flash crash, there was added confusion involving order backlogs and incorrect time stamps that made it difficult to determine which trades had actually gone through, since even some of the market computers had been left behind by the high-speed traders.


pages: 280 words: 79,029

Smart Money: How High-Stakes Financial Innovation Is Reshaping Our WorldÑFor the Better by Andrew Palmer

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Affordable Care Act / Obamacare, algorithmic trading, Andrei Shleifer, asset-backed security, availability heuristic, bank run, banking crisis, Black-Scholes formula, bonus culture, break the buck, Bretton Woods, call centre, Carmen Reinhart, cloud computing, collapse of Lehman Brothers, collateralized debt obligation, computerized trading, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, David Graeber, diversification, diversified portfolio, Edmond Halley, Edward Glaeser, endogenous growth, Eugene Fama: efficient market hypothesis, eurozone crisis, family office, financial deregulation, financial innovation, fixed income, Flash crash, Google Glasses, Gordon Gekko, high net worth, housing crisis, Hyman Minsky, implied volatility, income inequality, index fund, information asymmetry, Innovator's Dilemma, interest rate swap, Kenneth Rogoff, Kickstarter, late fees, London Interbank Offered Rate, Long Term Capital Management, loss aversion, margin call, Mark Zuckerberg, McMansion, money market fund, mortgage debt, mortgage tax deduction, Myron Scholes, negative equity, Network effects, Northern Rock, obamacare, payday loans, peer-to-peer lending, Peter Thiel, principal–agent problem, profit maximization, quantitative trading / quantitative finance, railway mania, randomized controlled trial, Richard Feynman, Richard Feynman, Richard Thaler, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, short selling, Silicon Valley, Silicon Valley startup, Skype, South Sea Bubble, sovereign wealth fund, statistical model, transaction costs, Tunguska event, unbanked and underbanked, underbanked, Vanguard fund, web application

The Chinese-born American was keen on the idea of starting his own company, and the most well-trodden route to that future from an East Coast university was first to head to Wall Street and make some money. The obvious choice for a math whiz was a career in quantitative finance: that seemed to offer the right mixture of autonomy, intellectual challenge, and high pay. In the summer of 2011, Gu interned with DE Shaw, an extremely successful algorithmic-­trading firm. But something was missing. As much as he might rationally accept that this firm and others like it were adding value to the capital markets, he did not feel an emotional connection to what he was doing. Gu is the kind of person who does not lack for options. He was one of the first batch of Thiel fellows, twenty people under twenty who were each given one hundred thousand dollars to skip college for two years and pursue their ambitions in a program funded by Peter Thiel, a guru of technology investing whose résumé includes founding PayPal and backing Facebook.


pages: 741 words: 179,454

Extreme Money: Masters of the Universe and the Cult of Risk by Satyajit Das

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affirmative action, Albert Einstein, algorithmic trading, Andy Kessler, Asian financial crisis, asset allocation, asset-backed security, bank run, banking crisis, banks create money, Basel III, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Black Swan, Bonfire of the Vanities, bonus culture, Bretton Woods, BRICs, British Empire, capital asset pricing model, Carmen Reinhart, carried interest, Celtic Tiger, clean water, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, corporate governance, corporate raider, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, debt deflation, Deng Xiaoping, deskilling, discrete time, diversification, diversified portfolio, Doomsday Clock, Edward Thorp, Emanuel Derman, en.wikipedia.org, Eugene Fama: efficient market hypothesis, eurozone crisis, Fall of the Berlin Wall, financial independence, financial innovation, financial thriller, fixed income, full employment, global reserve currency, Goldman Sachs: Vampire Squid, Gordon Gekko, greed is good, happiness index / gross national happiness, haute cuisine, high net worth, Hyman Minsky, index fund, information asymmetry, interest rate swap, invention of the wheel, invisible hand, Isaac Newton, job automation, Johann Wolfgang von Goethe, John Meriwether, joint-stock company, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, Kevin Kelly, labour market flexibility, laissez-faire capitalism, load shedding, locking in a profit, Long Term Capital Management, Louis Bachelier, margin call, market bubble, market fundamentalism, Marshall McLuhan, Martin Wolf, mega-rich, merger arbitrage, Mikhail Gorbachev, Milgram experiment, money market fund, Mont Pelerin Society, moral hazard, mortgage debt, mortgage tax deduction, mutually assured destruction, Myron Scholes, Naomi Klein, negative equity, Network effects, new economy, Nick Leeson, Nixon shock, Northern Rock, nuclear winter, oil shock, Own Your Own Home, Paul Samuelson, pets.com, Philip Mirowski, Plutocrats, plutocrats, Ponzi scheme, price anchoring, price stability, profit maximization, quantitative easing, quantitative trading / quantitative finance, Ralph Nader, RAND corporation, random walk, Ray Kurzweil, regulatory arbitrage, rent control, rent-seeking, reserve currency, Richard Feynman, Richard Feynman, Richard Thaler, Right to Buy, risk-adjusted returns, risk/return, road to serfdom, Robert Shiller, Robert Shiller, Rod Stewart played at Stephen Schwarzman birthday party, rolodex, Ronald Reagan, Ronald Reagan: Tear down this wall, Satyajit Das, savings glut, shareholder value, Sharpe ratio, short selling, Silicon Valley, six sigma, Slavoj Žižek, South Sea Bubble, special economic zone, statistical model, Stephen Hawking, Steve Jobs, survivorship bias, The Chicago School, The Great Moderation, the market place, the medium is the message, The Myth of the Rational Market, The Nature of the Firm, the new new thing, The Predators' Ball, The Wealth of Nations by Adam Smith, Thorstein Veblen, too big to fail, trickle-down economics, Turing test, Upton Sinclair, value at risk, Yogi Berra, zero-coupon bond, zero-sum game

Event-driven trading uses knowledge of regulations, legal documentation, and (sometimes critics suspect) inside information to make profits. Investment periods can be long and, as the name implies, there is “event risk” (a merger not proceeding). Quantitative funds use computer-driven trading strategies. Trend-following models identify the market’s mo (momentum) and ride the bucking bronco bareback. Sophisticated models identify undervalued and overvalued securities. Algorithmic trading, known as algo, black-box, or robo trading, uses computer programs to decide timing, price or quantity of trading orders. It divides large trades into smaller trades or vice versa to manage market impact and risk. It generates small but frequent returns by providing liquidity to other buyers and sellers. Macro funds make large, speculative, leveraged bets on currencies, stocks, interest rates, and commodities.

Index NUMBERS 13 Bankers, 294 60 Minutes, 343 401(k) plans, 48 1720 Bubble Act, 53 A AAA tranches, 203 Abacus transactions, 197-199, 339 Abbey, Edward, 362 ABN-Amro, 197 ABS CP (asset-backed securities commercial paper), 190 ABS PAYG CDS (asset-backed securities pay-as-you-go credit default swaps), 196 Absolute Strategy Research, 357 ABX.HE (asset-backed securities home equity), 196 Abyssinian Baptist Church, 164 ACA Capital, 197 accounting ark-to-market, 56 errors, 285 mark-to-market (MtM), 286-288 rules, 81, 349 standards, 289 value, 286-287 accreting interest rate swaps, 160 accumulator contracts, 219 Ackoff, Russell, 309 acquisitions, 57-59, 310 General Electric (GE), 61 Adelphia, 154 adjustable rate bonds, 213 adjustable rate mortgages (ARMs), 148, 182-184 adjusted model prices, 289 Aeneid, 338 affinity fraud, 313 Affluent Society, The, 43 affluenza, 46 Africa, 22 Against The Gods: The Remarkable History of Risk, 129 Age of Turbulence, The, 302 Agnelli, Giovanni, 222 AIG, 230-234, 270 Airline Partners Australia (APA), 156 airlines, leveraged buyouts (LBO), 156-157 airports, 158, 161 Albanese, Tom, 59 Alcan, 59 Alcar, 124 alchemy, 131-132 algorithmic trading, 242 Alice in Wonderland, 31 Allco Equity Partners, 156 Allco Finance Group, 156 Allen, Paul, 179 Allied Signal, 60 alpha (outperformance), 241 Alt A (Alternative A) mortgages, 182 alternatives assets, 154 investments, 252 paper money, 35 Altman, Edward, 143 amakudari (descent from heaven), 316 Amaranth, 227 hedge funds, 250-252 Amazon.com, 97 America. See also United States China’s relationship with, 87 dollars, 22 American Express, 71 American Finance Association, 127 American Nairn Linoleum Company, 133 American Psycho, 313 American Rust, 141 Ameriquest, 195 Amherst, 196 amortization, 182 Amphlett, Christina, 218 Andersson, Roy, 50 Angelides, Phil, 291 Angstrom, Harry “Rabbit”, 27, 46, 363 annihilation, 38 annuity income, 70 anti-debt, 93 AOL (America Online), 58 apical ballooning syndrome, 177 Applegarth, Adam, 200 AQR Capital Management, 126, 255 arbitrage, 190, 235, 242 Arnason, Ragnar, 277 Arnold, John, 319 Arrow, Kenneth, 128 Artus, Patrick, 303 Ascent of Money, The, 157 Asian crisis, 280 Asness, Clifford, 126, 255 Asquith, Paul, 144 assay marks, 25 asset-backed securities (ABSs), 169 assets hiding, 288 lite strategies, 55 loans, 70 rise of prices, 296 supply of, 267 asymmetric information, 119 Atlantic Monthly, 95 Atlas Shrugged, 110, 302 attachment theories, 316 aussies (Australian dollars), 21 austerity, 357 Australia, 91, 159 Austrian School, 103 Austro-Hungarian Empire, 103 automated credit scores, 181 automobile industry bailouts, 344 Autovolanter, 322 avoidance of taxes, 48-49 Aztec cultures, invention of money, 24 B BAA (British Airport Authority), 161 Babson, Robert, 347 Bacevich, Andrew, 43 Bachelier, Louis, 118, 121 backfill bias, 243 Bacon, Louis, 250, 254 Bagehot, Walter, 63, 67, 201, 278 Bailey, George, 65, 74, 180 bailouts, 344 AIG, 234.


pages: 677 words: 206,548

Future Crimes: Everything Is Connected, Everyone Is Vulnerable and What We Can Do About It by Marc Goodman

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23andMe, 3D printing, active measures, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, airport security, Albert Einstein, algorithmic trading, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, Bill Joy: nanobots, bitcoin, Black Swan, blockchain, borderless world, Brian Krebs, business process, butterfly effect, call centre, Chelsea Manning, cloud computing, cognitive dissonance, computer vision, connected car, corporate governance, crowdsourcing, cryptocurrency, data acquisition, data is the new oil, Dean Kamen, disintermediation, don't be evil, double helix, Downton Abbey, drone strike, Edward Snowden, Elon Musk, Erik Brynjolfsson, Filter Bubble, Firefox, Flash crash, future of work, game design, Google Chrome, Google Earth, Google Glasses, Gordon Gekko, high net worth, High speed trading, hive mind, Howard Rheingold, hypertext link, illegal immigration, impulse control, industrial robot, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jaron Lanier, Jeff Bezos, job automation, John Harrison: Longitude, John Markoff, Jony Ive, Julian Assange, Kevin Kelly, Khan Academy, Kickstarter, knowledge worker, Kuwabatake Sanjuro: assassination market, Law of Accelerating Returns, Lean Startup, license plate recognition, lifelogging, litecoin, M-Pesa, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Metcalfe’s law, mobile money, more computing power than Apollo, move fast and break things, move fast and break things, Nate Silver, national security letter, natural language processing, obamacare, Occupy movement, Oculus Rift, off grid, offshore financial centre, optical character recognition, Parag Khanna, pattern recognition, peer-to-peer, personalized medicine, Peter H. Diamandis: Planetary Resources, Peter Thiel, pre–internet, RAND corporation, ransomware, Ray Kurzweil, refrigerator car, RFID, ride hailing / ride sharing, Rodney Brooks, Satoshi Nakamoto, Second Machine Age, security theater, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart grid, smart meter, Snapchat, social graph, software as a service, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, Stuxnet, supply-chain management, technological singularity, telepresence, telepresence robot, Tesla Model S, The Future of Employment, The Wisdom of Crowds, Tim Cook: Apple, trade route, uranium enrichment, Wall-E, Watson beat the top human players on Jeopardy!, Wave and Pay, We are Anonymous. We are Legion, web application, Westphalian system, WikiLeaks, Y Combinator, zero day

Just one news service alone, Thomson Reuters, feeds these HFT algos by scanning fifty thousand distinct news sources and four million social media sites at speeds no human being could ever possibly match. The vast networks of HFT machines can collectively make trillions of calculations per second, and trades can be executed in less than half a millionth of a second, thousands of times faster than the blink of an eye. When the artificial-intelligence-based algorithmic trade bots came across a tweet mentioning “explosions,” “Obama,” and “White House” in the same sentence from a source they had been trained to trust, the Associated Press, it took them just a few thousandths of a second to respond. As they did, other algorithms picked up on the activity, and soon a full-on snowball effect was in play. Algorithms began selling en masse, erasing $136 billion in valuation in an amazing three minutes.

When your GPS device provides you with directions using narrow AI to process the request, it is making decisions for you about your route based on an instruction set somebody else has programmed. While there may be a hundred ways to get from your home to your office, your navigation system has selected one. What happened to the other ninety-nine? In a world run increasingly by algorithms, it is not an inconsequential question or a trifling point. Today we have the following: • algorithmic trading on Wall Street (bots carry out stock buys and sells) • algorithmic criminal justice (red-light and speeding cameras determine infractions of the law) • algorithmic border control (an AI can flag you and your luggage for screening) • algorithmic credit scoring (your FICO score determines your creditworthiness) • algorithmic surveillance (CCTV cameras can identify unusual activity by computer vision analysis, and voice recognition can scan your phone calls for troublesome keywords) • algorithmic health care (whether or not your request to see a specialist or your insurance claim is approved) • algorithmic warfare (drones and other robots have the technical capacity to find, target, and kill without human intervention) • algorithmic dating (eHarmony and others promise to use math to find your soul mate and the perfect match) Though the inventors of these algorithmic formulas might wish to suggest they are perfectly neutral, nothing could be further from the truth.


pages: 342 words: 94,762

Wait: The Art and Science of Delay by Frank Partnoy

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algorithmic trading, Atul Gawande, Bernie Madoff, Black Swan, blood diamonds, Cass Sunstein, Checklist Manifesto, cognitive bias, collapse of Lehman Brothers, collateralized debt obligation, computerized trading, corporate governance, Daniel Kahneman / Amos Tversky, delayed gratification, Flash crash, Frederick Winslow Taylor, George Akerlof, Google Earth, Hernando de Soto, High speed trading, impulse control, income inequality, information asymmetry, Isaac Newton, Long Term Capital Management, Menlo Park, mental accounting, meta analysis, meta-analysis, Nick Leeson, paper trading, Paul Graham, payday loans, Ralph Nader, Richard Thaler, risk tolerance, Robert Shiller, Robert Shiller, Ronald Reagan, Saturday Night Live, six sigma, Spread Networks laid a new fibre optics cable between New York and Chicago, statistical model, Steve Jobs, The Market for Lemons, the scientific method, The Wealth of Nations by Adam Smith, upwardly mobile, Walter Mischel

Scott Harrison, the man Perold called, told me about Perold’s pitch: “He said we were going to save UNX. We’d raise money from some Wall Street banks and build a brand-new platform with the best trading technology. It was going to be a serious challenge, but also an exciting opportunity. Our goal was to become faster and cheaper than anyone.” Harrison was one of a handful of people in the world who had the two skill sets the UNX job required. He had managed algorithmic trading operations, including complicated computer programs with names like Triton and QuantEX. But Harrison also was a visionary and a builder. Most managers of high-frequency firms spend their careers trading stocks and crunching numbers. Before Harrison began working in finance, he was an architect at Skidmore, Owings & Merrill. When I first spoke to Harrison, on a fall weekday in 2010, he wasn’t trading.


pages: 329 words: 95,309

Digital Bank: Strategies for Launching or Becoming a Digital Bank by Chris Skinner

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algorithmic trading, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, bank run, Basel III, bitcoin, business intelligence, business process, business process outsourcing, call centre, cashless society, clean water, cloud computing, corporate social responsibility, credit crunch, crowdsourcing, cryptocurrency, demand response, disintermediation, don't be evil, en.wikipedia.org, fault tolerance, fiat currency, financial innovation, Google Glasses, high net worth, informal economy, Infrastructure as a Service, Internet of things, Jeff Bezos, Kevin Kelly, Kickstarter, M-Pesa, margin call, mass affluent, mobile money, Mohammed Bouazizi, new economy, Northern Rock, Occupy movement, Pingit, platform as a service, Ponzi scheme, prediction markets, pre–internet, QR code, quantitative easing, ransomware, reserve currency, RFID, Satoshi Nakamoto, Silicon Valley, smart cities, software as a service, Steve Jobs, strong AI, Stuxnet, trade route, unbanked and underbanked, underbanked, upwardly mobile, We are the 99%, web application, Y2K

This is because banks do not analyse data on an enterprise basis, but usually hold this data in divisional stores organised around products, channels and lines of business, and the politics internally are the greatest blockage for change and, without that change, banks are stuck with piecemeal data sets being analysed in pieces. That is not good enough today. This is why banks need to completely rearchitect their enterprise technologies to enable deep data mining across all data, and create semantic marketing programs that sense customers’ needs proactively and pre-emptively. Like algorithmic trading in capital markets where algorithmic news feeds allow trading in equities to move in real-time high frequency blackbox strategies that maximise returns, we’re talking of applying the same technologies to retail transaction services for customer loyalty and wallet share. That’s the battle about to begin as we move from managing data to using information as a competitive weapon and, at the core of predictive marketing is Big Data.


pages: 364 words: 99,897

The Industries of the Future by Alec Ross

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23andMe, 3D printing, Airbnb, algorithmic trading, AltaVista, Anne Wojcicki, autonomous vehicles, banking crisis, barriers to entry, Bernie Madoff, bioinformatics, bitcoin, blockchain, Brian Krebs, British Empire, business intelligence, call centre, carbon footprint, cloud computing, collaborative consumption, connected car, corporate governance, Credit Default Swap, cryptocurrency, David Brooks, disintermediation, Dissolution of the Soviet Union, distributed ledger, Edward Glaeser, Edward Snowden, en.wikipedia.org, Erik Brynjolfsson, fiat currency, future of work, global supply chain, Google X / Alphabet X, industrial robot, Internet of things, invention of the printing press, Jaron Lanier, Jeff Bezos, job automation, John Markoff, knowledge economy, knowledge worker, lifelogging, litecoin, M-Pesa, Marc Andreessen, Mark Zuckerberg, Mikhail Gorbachev, mobile money, money: store of value / unit of account / medium of exchange, new economy, offshore financial centre, open economy, Parag Khanna, peer-to-peer, peer-to-peer lending, personalized medicine, Peter Thiel, precision agriculture, pre–internet, RAND corporation, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Rubik’s Cube, Satoshi Nakamoto, selective serotonin reuptake inhibitor (SSRI), self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, social graph, software as a service, special economic zone, supply-chain management, supply-chain management software, technoutopianism, The Future of Employment, underbanked, Vernor Vinge, Watson beat the top human players on Jeopardy!, women in the workforce, Y Combinator, young professional

By using local sensor inputs to determine just the right amount of water and fertilizer to use, precision agriculture holds the promise of growing more food while polluting less, all with the help of big data. FINTECH: THE FINANCIAL DATA SYSTEM Wall Street has taken advantage of big data as much as any industry. Of the roughly 7 billion shares that are traded in US equity markets every day, two-thirds are traded by preprogrammed computer algorithms that crunch data about share prices, timing, and quantity in order to maximize gains and minimize risk. This is called black-box or algorithmic trading and is now the norm in finance. The next impact of big data in the finance world will be in retail banking, the area where average people are the customers, as opposed to investment banks or commercial banks that focus on serving corporations. The application of big data to enhance operations and product development in retail banking is known as “fintech.” The technology that undergirds the banking system’s current infrastructure is obsolete.


pages: 326 words: 103,170

The Seventh Sense: Power, Fortune, and Survival in the Age of Networks by Joshua Cooper Ramo

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Airbnb, Albert Einstein, algorithmic trading, barriers to entry, Berlin Wall, bitcoin, British Empire, cloud computing, crowdsourcing, Danny Hillis, defense in depth, Deng Xiaoping, drone strike, Edward Snowden, Fall of the Berlin Wall, Firefox, Google Chrome, income inequality, Isaac Newton, Jeff Bezos, job automation, market bubble, Menlo Park, Metcalfe’s law, natural language processing, Network effects, Norbert Wiener, Oculus Rift, packet switching, Paul Graham, price stability, quantitative easing, RAND corporation, recommendation engine, Republic of Letters, Richard Feynman, Richard Feynman, road to serfdom, Robert Metcalfe, Sand Hill Road, secular stagnation, self-driving car, Silicon Valley, Skype, Snapchat, social web, sovereign wealth fund, Steve Jobs, Steve Wozniak, Stewart Brand, Stuxnet, superintelligent machines, technological singularity, The Coming Technological Singularity, The Wealth of Nations by Adam Smith, too big to fail, Vernor Vinge, zero day

“I alone was responsible for tomorrow, and if I failed, the dreadful results would rest on judgment day against my soul.” Policy gets implemented through operations. This is where clever bureaucrats and parasitic office politicians prey, where they can most easily undermine the ambitions of visionaries. But it is also the place where inspiration springs from the will and passion of companies, armies, and research labs. Server farms, data-mining algorithms, trade treaties—these are the operational chessboards of our era. Operations is where the bolt tightening for revolutionary change occurs. It is intense, relentless operations that ensure stability in the face of shock or growth or collapse. “The exploding popularity of Internet services has created a new class of computing systems that we have named warehouse-scale computers,” the Google data engineers Luiz André Barroso and Urs Hölzle wrote in a famous paper several years ago as they described the operational revolution that lets Google serve terabytes of data, instantly, every day.


pages: 413 words: 117,782

What Happened to Goldman Sachs: An Insider's Story of Organizational Drift and Its Unintended Consequences by Steven G. Mandis

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activist fund / activist shareholder / activist investor, algorithmic trading, Berlin Wall, bonus culture, BRICs, business process, collapse of Lehman Brothers, collateralized debt obligation, commoditize, complexity theory, corporate governance, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, disintermediation, diversification, Emanuel Derman, financial innovation, fixed income, friendly fire, Goldman Sachs: Vampire Squid, high net worth, housing crisis, London Whale, Long Term Capital Management, merger arbitrage, Myron Scholes, new economy, passive investing, performance metric, risk tolerance, Ronald Reagan, Saturday Night Live, Satyajit Das, shareholder value, short selling, sovereign wealth fund, The Nature of the Firm, too big to fail, value at risk

Even with Goldman’s success, it is still valued less than many internet or dot-com companies. Some of the best and brightest are now more interested in working at a technology company than at Goldman (C). Other firms, such as Donaldson, Lufkin & Jenrette, start to offer significantly higher compensation than Goldman, especially at the entry-level positions (C). Goldman acquires Hull Trading Company, a leading technology-driven algorithmic trading firm and electronic market maker, for $531 million (C, T). Technology-driven trading is starting to dominate (T). In November, Goldman establishes the Pine Street Leadership Development Initiative, in part, to help socialize larger numbers of managers (O). The Euro becomes an accounting currency and was scheduled to enter circulation in 2002, helping to accelerate pan-European banking consolidation. 2000: The Commodity Futures Modernization Act determines that credit default swaps are neither futures nor securities and therefore are not subject to regulation by the Securities and Exchange Commission or the Commodities Futures Trading Commission (CFTC) (R, T).


pages: 504 words: 139,137

Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined by Lasse Heje Pedersen

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activist fund / activist shareholder / activist investor, algorithmic trading, Andrei Shleifer, asset allocation, backtesting, bank run, banking crisis, barriers to entry, Black-Scholes formula, Brownian motion, buy low sell high, capital asset pricing model, commodity trading advisor, conceptual framework, corporate governance, credit crunch, Credit Default Swap, currency peg, David Ricardo: comparative advantage, declining real wages, discounted cash flows, diversification, diversified portfolio, Emanuel Derman, equity premium, Eugene Fama: efficient market hypothesis, fixed income, Flash crash, floating exchange rates, frictionless, frictionless market, Gordon Gekko, implied volatility, index arbitrage, index fund, interest rate swap, late capitalism, law of one price, Long Term Capital Management, margin call, market clearing, market design, market friction, merger arbitrage, money market fund, mortgage debt, Myron Scholes, New Journalism, paper trading, passive investing, price discovery process, price stability, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, random walk, Renaissance Technologies, Richard Thaler, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, selection bias, shareholder value, Sharpe ratio, short selling, sovereign wealth fund, statistical arbitrage, statistical model, survivorship bias, systematic trading, technology bubble, time value of money, total factor productivity, transaction costs, value at risk, Vanguard fund, yield curve, zero-coupon bond

They are distinguished by diversification, sticking to their process with discipline, and the ability to engineer portfolio characteristics. —Cliff Asness (2007) Quantitative equity investing—quant equity, for short—means model-driven equity investing, performed, for instance, by equity market neutral hedge funds. Quants codify their trading rules in computer systems and execute orders with algorithmic trading overseen by humans. There are several advantages and disadvantages of quantitative investing relative to discretionary trading. The disadvantages are that the trading rule cannot be as tailored to each specific situation and it cannot be based on “soft” information such as phone calls and human judgment. These disadvantages may be diminishing as computing power and sophistication increase.

Investment: A History by Norton Reamer, Jesse Downing

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activist fund / activist shareholder / activist investor, Albert Einstein, algorithmic trading, asset allocation, backtesting, banking crisis, Berlin Wall, Bernie Madoff, break the buck, Brownian motion, buttonwood tree, California gold rush, capital asset pricing model, Carmen Reinhart, carried interest, colonial rule, credit crunch, Credit Default Swap, Daniel Kahneman / Amos Tversky, debt deflation, discounted cash flows, diversified portfolio, equity premium, estate planning, Eugene Fama: efficient market hypothesis, Fall of the Berlin Wall, family office, Fellow of the Royal Society, financial innovation, fixed income, Gordon Gekko, Henri Poincaré, high net worth, index fund, information asymmetry, interest rate swap, invention of the telegraph, James Hargreaves, James Watt: steam engine, joint-stock company, Kenneth Rogoff, labor-force participation, land tenure, London Interbank Offered Rate, Long Term Capital Management, loss aversion, Louis Bachelier, margin call, means of production, Menlo Park, merger arbitrage, money market fund, moral hazard, mortgage debt, Myron Scholes, negative equity, Network effects, new economy, Nick Leeson, Own Your Own Home, Paul Samuelson, pension reform, Ponzi scheme, price mechanism, principal–agent problem, profit maximization, quantitative easing, RAND corporation, random walk, Renaissance Technologies, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, Sand Hill Road, Sharpe ratio, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, spinning jenny, statistical arbitrage, survivorship bias, technology bubble, The Wealth of Nations by Adam Smith, time value of money, too big to fail, transaction costs, underbanked, Vanguard fund, working poor, yield curve

Managers—those who consistently outperform and perhaps even those well-organized firms who do not add significant value for their clients—may continue to be well compensated, barring major changes in fee structures or introduction of exogenous downward pressure on fees to relieve the 316 Investment: A History asymmetry issues discussed in this chapter. Thus, this era of vast earnings by top investment managers, while likely to moderate slowly over time, is unlikely to disappear completely. At the end of the day, though, the next generation of financial innovations will likely take the spotlight. The changes may occur in niche corners of the market, like algorithmic trading, commodities strategies, or real assets, or they may involve a holistic approach like the endowment-style or multistrategy firm. Only time will tell what financial innovations the future holds. Conclusion Investment in the Twenty-First Century THIS NARRATIVE HAS MADE IT CLEAR that investment is a fundamental human enterprise. It is not an overstatement to elevate it to be a basic element of the major economic and cultural activities of society: what individuals do for work, what they consume for physical sustenance, how they educate their youth and cultivate their intellectual interests, and how they invest their resources.


pages: 479 words: 144,453

Homo Deus: A Brief History of Tomorrow by Yuval Noah Harari

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23andMe, agricultural Revolution, algorithmic trading, Anne Wojcicki, anti-communist, Anton Chekhov, autonomous vehicles, Berlin Wall, call centre, Chris Urmson, cognitive dissonance, Columbian Exchange, computer age, Deng Xiaoping, don't be evil, drone strike, European colonialism, experimental subject, falling living standards, Flash crash, Frank Levy and Richard Murnane: The New Division of Labor, glass ceiling, global village, Intergovernmental Panel on Climate Change (IPCC), invention of writing, invisible hand, Isaac Newton, job automation, John Markoff, Kevin Kelly, lifelogging, means of production, Mikhail Gorbachev, Minecraft, Moneyball by Michael Lewis explains big data, mutually assured destruction, new economy, pattern recognition, Peter Thiel, placebo effect, Ray Kurzweil, self-driving car, Silicon Valley, Silicon Valley ideology, stem cell, Steven Pinker, telemarketer, The Future of Employment, too big to fail, trade route, Turing machine, Turing test, ultimatum game, Watson beat the top human players on Jeopardy!, zero-sum game

It then bounced back, returning to its pre-crash level in a little over three minutes. That’s what happens when super-fast computer programs are in charge of our money. Experts have been trying ever since to understand what happened in this so-called ‘Flash Crash’. We know algorithms were to blame, but we are still not sure exactly what went wrong. Some traders in the USA have already filed lawsuits against algorithmic trading, arguing that it unfairly discriminates against human beings, who simply cannot react fast enough to compete. Quibbling whether this really constitutes a violation of rights might provide lots of work and lots of fees for lawyers.5 And these lawyers won’t necessarily be human. Movies and TV series give the impression that lawyers spend their days in court shouting ‘Objection!’ and making impassioned speeches.


pages: 422 words: 113,830

Bad Money: Reckless Finance, Failed Politics, and the Global Crisis of American Capitalism by Kevin Phillips

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algorithmic trading, asset-backed security, bank run, banking crisis, Bernie Madoff, Black Swan, Bretton Woods, BRICs, British Empire, collateralized debt obligation, computer age, corporate raider, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, currency peg, diversification, Doha Development Round, energy security, financial deregulation, financial innovation, fixed income, Francis Fukuyama: the end of history, George Gilder, housing crisis, Hyman Minsky, imperial preference, income inequality, index arbitrage, index fund, interest rate derivative, interest rate swap, Joseph Schumpeter, Kenneth Rogoff, large denomination, Long Term Capital Management, market bubble, Martin Wolf, Menlo Park, mobile money, money market fund, Monroe Doctrine, moral hazard, mortgage debt, Myron Scholes, new economy, oil shale / tar sands, oil shock, old-boy network, peak oil, Plutocrats, plutocrats, Ponzi scheme, profit maximization, Renaissance Technologies, reserve currency, risk tolerance, risk/return, Robert Shiller, Robert Shiller, Ronald Reagan, Satyajit Das, shareholder value, short selling, sovereign wealth fund, The Chicago School, Thomas Malthus, too big to fail, trade route

Or perhaps we should say a bevy of black swans, author Nassim Nicholas Taleb’s shorthand for mathematical impossibilities that cannot occur in hedge funds’ quantitative strategies but always manage to occur two, three, seven, or eleven times in the real world of every significant financial crisis.47 The idea that policymakers have allowed the U.S. economy to be guided by a financial sector increasingly dominated by black box makers and algorithm vendors itself seems like a black swan—an impossibility, save that it’s happening. According to one U.S. consultancy, by 2010 algorithmic trading, an aspect of “quant”based investing, is expected to account for half of all trading in U.S. equity markets.48 There is no better distillation of the harm inflicted—and probably yet to be inflicted—than that of hedge fund manager Richard Bookstaber in his 2007 volume, A Demon of Our Own Design: Markets, Hedge Funds, and the Perils of Financial Innovation. His underlying point is that even though financial strategists can keep dreaming up new instruments, it’s not a good idea to do so, because each innovation adds layers of increasing complexity, tight coupling, and risk.


The Blockchain Alternative: Rethinking Macroeconomic Policy and Economic Theory by Kariappa Bheemaiah

accounting loophole / creative accounting, Ada Lovelace, Airbnb, algorithmic trading, asset allocation, autonomous vehicles, balance sheet recession, bank run, banks create money, Basel III, basic income, Ben Bernanke: helicopter money, bitcoin, blockchain, Bretton Woods, business process, call centre, capital controls, Capital in the Twenty-First Century by Thomas Piketty, cashless society, cellular automata, central bank independence, Claude Shannon: information theory, cloud computing, cognitive dissonance, collateralized debt obligation, commoditize, complexity theory, constrained optimization, corporate governance, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crowdsourcing, cryptocurrency, David Graeber, deskilling, Diane Coyle, discrete time, distributed ledger, diversification, double entry bookkeeping, ethereum blockchain, fiat currency, financial innovation, financial intermediation, Flash crash, floating exchange rates, Fractional reserve banking, full employment, George Akerlof, illegal immigration, income inequality, income per capita, inflation targeting, information asymmetry, interest rate derivative, inventory management, invisible hand, John Maynard Keynes: technological unemployment, John von Neumann, joint-stock company, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, Kevin Kelly, knowledge economy, labour market flexibility, large denomination, liquidity trap, London Whale, low skilled workers, M-Pesa, Marc Andreessen, market bubble, market fundamentalism, Mexican peso crisis / tequila crisis, money market fund, money: store of value / unit of account / medium of exchange, mortgage debt, natural language processing, Network effects, new economy, Nikolai Kondratiev, offshore financial centre, packet switching, Pareto efficiency, pattern recognition, peer-to-peer lending, Ponzi scheme, precariat, pre–internet, price mechanism, price stability, private sector deleveraging, profit maximization, QR code, quantitative easing, quantitative trading / quantitative finance, Ray Kurzweil, Real Time Gross Settlement, rent control, rent-seeking, Satoshi Nakamoto, Satyajit Das, savings glut, seigniorage, Silicon Valley, Skype, smart contracts, software as a service, software is eating the world, speech recognition, statistical model, Stephen Hawking, supply-chain management, technology bubble, The Chicago School, The Future of Employment, The Great Moderation, the market place, The Nature of the Firm, the payments system, the scientific method, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, too big to fail, trade liberalization, transaction costs, Turing machine, Turing test, universal basic income, Von Neumann architecture, Washington Consensus

This also allowed traders to take positions in anticipation of future price movements (“directional” strategy) and provided arbitrage opportunities between related assets. However, since the crisis, lower volatility, improved liquidity, rising costs of trading infrastructure, and regulatory scrutiny have declined the profitability of HFT, while dislocations such as the 2010 flash crash, the 2014 treasury flash crash, and the 2015 ETF flash crash have declined the popularity of HFT. In light of these shortcomings, FinTech firms using algorithmic trading strategies with smarter and faster machines are changing the market structure in terms of volume, liquidity, volatility, and spread of risk. Companies such as Neuro Dimension conduct technical analysis with AI (using neural networks and genetic algorithms) to “learn” patterns from historical data. They then optimise the power of big data by combining search data with multiple macroeconomic factors and quantified news insights to calculate potential upside/downside scenarios.