P1: a/b P2: c/d QC: e/f T1: g ind JWBT136-Narang July 9, 2009 11:49 Printer: Yet to come

Index

Academic literature, 127–128 Amazon.com, 180–181 Accidental , 9 Ameritrade, 155 Accounting scandals, 36 Analyst(s): Active Portfolio Management expectations, 36 (Grinold/Kahn), 92 functions of, 204 Adaptiveness, empirical models, 63 recommendations, 137 Aggressive orders, 101–103, 105, 190 sell-side, 36 Aite Group, 5 Annual/annualized returns, 4, 139 Algorithmic execution, 5 Anti-correlated instruments, 89 Algorithmic trading, 6, 201 Arbitrage, types of, 6–7, 12, 62, 129, 160, Algorithms, types of: 164, 179, 195, 203 learning/genetic, 46, 94 Arbitrageurs, 7, 164 machine learning, 46 Architectural errors, 153–154 order execution, 16, 100–105 Arms race. See High-frequency trading All-or-none orders, 101 Art/science debate, 169–170 Alpha, generally: Asian currency crisis, 172 defined, 55 Asset allocation, 80 exposure, 149 Asset classes, 15, 43–44, 59, 65, 61, 159, forecasts, 93 166, 172, 176, 201, 203 models, see Alpha model(s) , 5 portfolio, 198, 200–201 ATM effect, 160, 162–163 signal, 135 Australia, 178 time decay of, 137–138 Automated trading: Alpha model(s): benefits of, xiii, 170 applications of, generally, 21–22, 54 execution of, 5, 16 blending, 48–54 functions of, 3 characteristics of, 15–16, 21–22, 67 Average rate of return, 132–133 data-driven, 37–38 Axcom, 27–28 forecasting, 92 information processing, 111 Back-history, 119, 121 model risk, 150, 153 Back-of-the-envelope analysis, 21 portfolio optimization,COPYRIGHTED 87, 92–93, Backtesting, MATERIAL 120–121, 132 95–96 Bad prints, 118 risk management, 60–61 Bailout packages, 173–174 significance of, 203 Balance sheets, 114, 173 strategy implementation, 38–48 Ball, Ray, 33 theory-driven, 24–37, 48 Bamberger, Gerry, 29 types of, 22–24, 187 Banking industry, model, 9 Alpha-oriented strategies, xiv, 7, 13 Bank of America, 13–14 Amaranth, 179 Bankruptcy, 173

213 P1: a/b P2: c/d QC: e/f T1: g ind JWBT136-Narang July 9, 2009 11:49 Printer: Yet to come

214 INDEX

Bank stocks, 36, 82 Boutique quant traders, 4, 6, 165, 178–179, Bar charts, 135 205 Barclay Group, 5–6 Breakout, trend strategies, 45 BARRA, 166 Brokerage houses, 36. See also Brokers Basis points, 100 Brokers: Bear markets, 127, 174, 203 commissions/fees, 46, 68–69, 100 Bear Stearns, 159 functions of, 99–100, 178–179 Benchmarks, 21 online, 155 Berlekamp, Elwyn, 27–28 trading infrastructure, 107 Beta: Buffett, Warren, 22, 62 exposure, 149 Bull markets, 127 implications of, 32 Burning data, 143 strategies, characteristics of, xiv Buy orders, 5, 101 Bet shops, 178 Buy strategies, 29 Bet structure: characteristics of, 39, 42–44, 47 Calmar ratio, 136–137 high-probability, 105 Cancelled orders, 105 importance of, 199–201 Capital markets, xiii, 5, 9, 14, 143, 169 limitations, 57–59 Carry trades, 33–34 risk exposure, 56 Cash bonds, 44 Bid/ask spread, 29 Cash-flow statement, 114 Bid/offer prices, 71, 101–103, 153 Cause-and-effect relationships, 50 Biotechnology stocks, 44 Caxton, 179 Black, Fischer, 91 CDOs, xiv Black box, see Black box schematics; Charles Schwab (SCHW), 29–30, 154–155 Black-box trading strategy Charts/charting: alpha models, 21–54 risk monitoring, 167 defined, 17, 111 test model metrics, 132 risk models, 55–65 Cheap stocks, 8, 22, 56, 130, 180 Black box schematics: Chhikara, Sudhir, 123 alpha model, 53 Citadel, 179 data, 123 Clearing process, 68–69 execution model, 109 Closing price, 63 portfolio construction model, 96 Colocation, 107 research, 145 Commissions, 15, 46, 68–69, 144 risk model, 65 Commodities trading advisors (CTAs), 26, transaction cost model, 78 174 Black-box trading strategy: Commodity market, 62, 157 characteristics of, 5, 13 Commodity trading, 47 implementation of, 7 Commodity trading advisors (CTAs), 5 origins of, 12, 203 Common investor risk, 159–166, 168 schematics, see Black box schematics Comparative advantage, 45 Black-Litterman optimization, 91–92 Competition, significance of, 37, 193, 195 Bond market: Complex quantitative trading strategies, 12 alpha model applications, 47 Compounded returns, 133 influential factors, 25, 34, 44, 62, 157 Computer(s): risk management, 59, 61–62 automated trading, xiii, 3, 5, 16, 170 Book value, 114 electronic market, 5, 45 Book value per share, 34 hardware systems, 108 Book-to-price ratio, 32, 34 software, see Software programs P1: a/b P2: c/d QC: e/f T1: g ind JWBT136-Narang July 9, 2009 11:49 Printer: Yet to come

Index 215

Computerized trading strategies, 170. See Database, as information resource. See Data also Software programs Data cube, 123 Conditional models, blending alphas, 51–52 Data-driven alpha model: Confidence intervals, 59 applications, 37–38 Consensus building, 25 blending with theory-driven model, 48–53 Consumer spending, 25 implementation of, 38–48 Contagion risk, 159–166, 168 theory-based alpha model compared with, Contrarians, 29 37–38 Convertible bonds, 45 Data-driven risk models, 58 Corporate bonds, 45, 61 Data edge, 194–195 Corporation, as data source, 116 Data feed handlers, 111, 150 Correlation coefficients, 135, 151 Data miners/mining, 23, 38, 46, 58, 94, 170, Correlations, portfolio construction, 43, 58, 179–182, 189 87–91, 93, 162, 177 Data sourcing, 189 Cost minimization, 77, 100 Debt crisis, historical 9 Countertrends, 25, 176 Decision makers, evaluation of, 185 Covariance, 58 Decision trees, 83–85 Credit crisis of 2008, 9, 36–37, 160, 173, Deleveraging, 165 203 Dennis, Richard, 128 Credit spreads, 162 D. E. Shaw, 128, 170, 179 Criticisms of quant trading: Developing markets, 203 art/science debate, 169–170 Directional forecast, 54, 93 data mining, 179–182 Direct market access (DMA), 99–100, 107 handling market changes, 174–176 Discretionary investment style, 8, 10, 12–13, large quants can thrive in long run, 204 178–179 Discretionary traders, characteristics of, 24, underestimating risk, 171–174 28–29, 31, 35, 100, 129, 172, 182, similarities/differences in quants, 176–177 199–200 Cross-asset yield, 161 Discretionary trading, 185, 204 Cross-border yield, 161 Dispersion, risk model, 58–59 Cross the spread orders, 102 Disposition effect, 10 Crowded trade effect, 162–163 Diversity/diversification: Cumulative profits, 132 alpha models, 39, 47–49, 54, 137, 161 Currency exchange, 118–119 importance of, 199–201 Currency market, influential factors, 62, 157 risk models, 61 Currency trading, 5, 47, 114 Dividends, 119 Dividend yield, 32 Daily volume, influential factors, 12 Dodd, David, 34, 49 Data: Donchain, Richard, 27, 127 asynchronicity, 120–121 Dot-com bubble, 22, 28, 173 cleaning, 117–122, 189, 195 Dow Jones Industrial Average, 172 collection methods, 114 Downtrends, 41 importance of, 111–113 Drawdowns, 81, 83, 87, 133–134, 136–137, incorrect, 113, 117–122 172–173, 195 management of, 189 Due diligence, 193 missing, 117–118 nature of, 112 Earnings before interest, taxes, depreciation, sources of, 115–116 and amortization (EBITDA), 34 storage, 112, 122–123 Earnings per share (EPS), 35–36 types of, 113–115 Earnings quality, 36 P1: a/b P2: c/d QC: e/f T1: g ind JWBT136-Narang July 9, 2009 11:49 Printer: Yet to come

216 INDEX

Earnings yield (E/P), 32–33, 51, 130–131 European Xetra electronic order-matching Eckhardt, William, 128 system, 5 Economic cycles, 172 EUROSTXX Index, 16 Edge: Excess risk, 15 data, 194–195 Exchange-traded funds (ETFs), 156 defined, 193 Exchanges: identification of, 201 as data source, 115 lack-of-competition, 195 fees, 69 monitoring and, 194 order execution, 101–102 structural, 195–196 Execution: Efficient Asset Management (Michaud), components of, 99–100, 109, 189 93 high-frequency trading, 105–107 Efficient frontier, 86 model risk, 150, 153–154 Efficient markets, 6 monitoring, 166–167 Einstein, Albert, 126–127, 141 order execution algorithms, 16, 100–105 Electronic Communication Networks trading infrastructure, 107–109 (ECNs), 72, 99, 104, 196 Exogenous shock risk, 158–159, 168, 172 Electronic markets, 5, 45 Exotic options, 13 Electronic matching networks, 69 Expectations, analyst, 36 Electronic trading, 99–100, 108 Expected correlation/correlation matrix, 86, Emerging markets, 45, 161 88–91 Empirical risk models, 61–65 Expected returns, 39, 54, 57, 87, 95 Empirical scientists, 23–24 Expected volatility, 87 Enron, 36 Expensive stocks, 56, 130, 180 Enterprise value (EV), 34 Exposure monitoring tools, 166 Equal risk, 51 Equal-weighting, 51, 93 Factor portfolios, optimization strategies, Equilibrium, 25, 29 92–93 Equity indices, 44 Failed quants, 4, 6 Equity investments, see Equity trading False signals, 38 growth strategies, 35–36 Falsification, 126 value trades, 34 Fama, Eugene, 31–32, 34 Equity market-neutral strategies, 96, Fama-French three factor model, 32, 34 144–145 Fat tails, 151 Equity market-neutral traders, 177, 203 Federal Reserve, 4, 34, 63, 181 Equity markets, 5 Fed funds rate, 4 premium, 11, 135 Fees, types of, 4, 28, 68–69, 100, 144 Equity traders, 9, 29, 166, 203. See also Fidelity Investments, 108 Equity market-neutral traders Fiduciaries, 196 Equity trading: Filled orders, 100–101 elements of, 59, 114 Fill rate, 167 portfolio optimization, 95 Fill-or-kill orders, 101 risk management, 59 Finance industry, 91 risk models, 63 Financial distress, 160 volume and, 3 Financial Information eXchange (FIX) E*Trade, 155 protocol, 108 Europe: Financial market, near-collapse in 1998, 9 quant trades/trading, 8, 45 Financial Select Sector SPDR, 174 stock market hours, 121 Financial statements, 114, 116, 120 European Central Bank, 34 Financial stocks, 174 P1: a/b P2: c/d QC: e/f T1: g ind JWBT136-Narang July 9, 2009 11:49 Printer: Yet to come

Index 217

FIX engine, 108 Great Depression, 172–173 Fixed income securities, 44, 61 Greater fools theory, 26 Flat file databases, 122 Greed, 173 Flat transaction cost models, 73–74 Gross domestic product (GDP), 35–36, 112, Flight-to-quality investments, 36 114 Forecasting: Grouping techniques: alpha model approaches, 25, 39, 42–44, alpha model, 42–43 47, 49–51 risk model, 58–59, 61 data mining, 180 Growth investing, 24, 35–36, 164–165 equity trading, 60 Growth stocks, 156–157, 161, 175–176 high-frequency, 59 Guerilla strategy, 106 implications of, 9, 15, 29, 38, 45 risk models, 59 Hard-to-borrow lists, 144 scientific method applications, 126–127 Head and shoulders pattern, 129 Foreign exchange markets, 5, 38, 44 funds, 3, 5–6, 27, 43, 77, 162, 174, Foreign exchange trading, 35 177, 179, 195, 205 Francioni, Reto, 6 Hedging, 60 Free cash flow, 36 Heuristics, 42–43, 79 French, Kenneth, 31–32, 34 Hidden orders, 104, 106, 190 Fundamental data: Highbridge, 128 growth, 30, 35–36 High-frequency traders/trading, 3–4, 38, 41, overview, 30–32 47, 105–107, 117, 144, 203 quality, 30, 36–37 High-quality companies, 36 sources of, 113–114, 116 High-rank stocks, 34–35 value/yield, 30, 32–35 High-yield stocks, 33, 51, 55–56 Fundamental strategies, 7, 36 Historical data, 32, 36, 61, 92, 117, 130, Futures contracts, 118–119 145, 151, 154, 174, 180, 183 Futures market, 15, 27, 38, 44 Historical regression, 50–51 Futures trading, 5–6, 26, 31, 203 Historical trends, 126–127 Historical volatility, 87–88 Game theory, 27 Hite, Larry, 27 Generalized autoregressive conditional Hit rate, 167 heteroskedasticity (GARCH), 87–88 Holding periods, 181–182 General relativity theory, 126–127 Hong Kong, 44, 178 Geography, investment universe, 44, 188, Housing market, 25. See also Mortgage 201 industry; Real estate German bund, 119 Human behavior, 170 German 10-Year Bonds, 16 Human discretion/judgment, 13–14, 17, Global Alpha Fund, 5 64–65, 191 Global market, 150 Human Genome Project, 23 Global stocks, 8 Going long, 60. See also Long/short Iceberging, 104, 106–107 strategies Iceland, 173 Gold, 42 Idea generation, 127–129 Goldman Sachs, 5, 128 Illiquidity, 45, 160 Good-till-cancelled orders, 101 Implementation programs, 6, 10, 18 Government, generally: Implied volatility, 172 bonds, 61, 114 Improving, 103 as data source, 116 Incentive fees, 28 Graham, Benjamin, 34, 49 Income statement, 114 P1: a/b P2: c/d QC: e/f T1: g ind JWBT136-Narang July 9, 2009 11:49 Printer: Yet to come

218 INDEX

Index funds, 7 retail, 77, 161 Indexed flat files, 122 systematic, 10 Individual investors, 161 Iraq War, economic impact of, 62, 159 Industry groups, 42–43, 89, 122 Inflation, 25 Japan, quant trading in, 45, 164 Information, see Data Joining, 103 gathering, 189 management, 193 Lack-of-competition edge, 195 ratio, 136 Large-cap securities, 8, 61, 77, 153, 161 Infrastructure: Leverage, 14, 7, 37, 59–60, 160–163, 173 errors, 167–168 Limit order book, 101 trade execution, 107–109 Limit orders, 101–104, 15, 167 Input/output model, 111 Linear forecasting, 49–51 In-sample testing, 129–131, 142–144 Linear relationship, 151–152 Insider information, 102 Linear transaction cost models, 74–75, 77 Institutional investors, 161 Liquidation crisis (August 2007), 5, 7, 56, Instrument class, 44, 188, 201 153, 161–167, 171, 175 Insurance industry, 173 Liquidity: , 61 implications of, 16, 28, 44–45, 71–73, 77, Interest rates, 34–35, 181 102, 104, 160, 164 Internet bubble, 62, 143, 155 pools, 104–105 Internet stocks, 70. See also Technology Liquid quant strategies, 166 stocks Live production trading system, 17 Interrogator, The (Toliver), 186–188 Long position, 61, 163 Intraday, generally: Long/short strategies, 144–145, 161, data, 38, 63, 120 163–164, 203 ticks, 38, 106 Long Term Capital Management (LTCM), 4, Intrinsic alpha, 96, 187, 191 8–9, 56, 160–161, 169, 172, 179 Intrinsic bets, 43 Long-term strategies, 41 Investment decisions: Look-ahead bias, 120–121, 143, 195 alpha forecast applications, 21–54 Losing trades, 56, 68, 192 based on inaccurate information, 13 Low-rank stocks, 34–35 influential factors, xiv, 10, 17 Low-yielding stock, 51, 55–56 systematic approach to, 13–14 Lumpiness, test model metric, 132–133 Investment horizon, 161. See also Time horizon Machine learning: Investment strategy: algorithms, 46 conception of, 12–13 characteristics of, 94, 203 influential factors, 8 forecasting model, 49–50, 52 Investment universe, 39, 44–47, 53, 190, techniques, 46, 106 201 Macroeconomics, 35, 112, 114, 121, 195 Investor(s): Macro traders/trading, 5, 31 attrition, 178 “Made-to-order” , 64 capital, 161 Madoff, Bernie, 197–198 individual, 166 Management fees, 28 institutional, 166 Manias, 28, 172 market-neutral, 166 Margin calls, 160 nonquant, 7, 46 Market capitalization, 32, 90, 166 quality, 24, 36–37 Market capitalization risk, 61 P1: a/b P2: c/d QC: e/f T1: g ind JWBT136-Narang July 9, 2009 11:49 Printer: Yet to come

Index 219

Market conditions: Momentum strategies, 25, 106, 164–165, impact of, 3, 46, 77, 162 191 risk models and, 62–63 Money market funds, 173–174 transaction cost models, 77 Monte Carlo simulation, 93–94 Market conditions, changes in, 174–176, Mortgage industry, 173 182. See also Regime changes Mortgage-related businesses, 37 Market direction risk, 60, 71 Moving average: Market dislocations, 174–176 characteristics of, 45 Market exposure, measurement of, 9 crossover, 26–27, 126–127 Market inefficiencies, 6–7 trend following strategy, 40–41 Market liquidity, 7. See also Liquidity Moving the market, 45–46, 70 Market makers, 73, 77 Multi-security bets, 43 Market-neutral investors, 166 Multiple regression, 50 Market neutral trading, 128 Multistrategy hedge funds, 162, 179 Market neutral value model, 61 Mutual funds, 49, 59, 77 Market-on-close order, 101 Market orders, 71, 100–101, 103 Naked short sale, 145 Market pricing, 7 Natural selection, 205 , 63 Negative market impact, 71 Market timing, 45 Negative trends, 26, 45 Markowitz, Harry, 85, 128, 136 News agencies, as data source, 116 Mars Climate Orbiter (MCO), 112 Newton, Isaac, gravity theory, 126 Mathematical models, 150 New York Stock Exchange (NYSE), 115 Mean reversion: Nikkei 225, 89, 93, 121–122 implications of, 43, 70, 106 Nonlinear forecasting, 49–51 trading strategies, 28–31, 37, 72, 81, 154, Nonlinear relationship, 151–152 159, 191 Nonquant investors, 7, 46 Mean variance optimization, 86, 128 Normal distribution, 151 Medallion Fund, 4 Northfield, 166 Medium-term strategies, 41, 47 Merger arbitrage, 160 Mergers, 13–14 Objective function, 79, 86–87, 94, 190 Merrill Lynch (MER), 13–14, 29–30, Occam’s razor, 141 154–155 Oil prices, 65 Mexican bonds, 34 Omega ratio, 136 Mexican currency crisis, 118, 172 100-day moving average, 41 Microeconomic growth strategy, 35 Opaqueness, 12, 18, 46 Microsoft (MSFT), 137–138 Opening price, 137 Microstructure alphas, 105 Optimization, see Portfolio Millennium Partners, 129 optimizers/optimization Mint Investments, 27 constrained, 91 Misspecification, 153 unconstrained, 91, 95 Model risk: Optimizers, in portfolio construction, 79 defined, 150 Options trading, 13, 195, 203 implementation errors, 150, 153–154 Order books, 101, 106 inapplicability of modeling, 150–152 Order execution algorithms: model misspecification, 150, 153 aggressive vs. passive, 101–103, 105, (MPT), 86 190 Momentum portfolio, 92 cancelling orders, 105 P1: a/b P2: c/d QC: e/f T1: g ind JWBT136-Narang July 9, 2009 11:49 Printer: Yet to come

220 INDEX

Order execution algorithms (Continued ) output of, 95 characteristics of, 29, 100–101 quantitative, 79 hidden order vs. visible order, 104, 106, rule-based, 79–85, 97 190 run frequency, 201 large order vs. small order, 103 selection factors, 96–97, 189 replacing orders, 105 specification of, 201 where to send orders, 104–105 transaction costs, impact of, see Order management system, 108 Transaction costs Order placement. See Order execution Portfolio insurance, 169 algorithms Portfolio management, day-to-day, 13 Out-of-favor exposure, 149 Portfolio of strategies, 137 Out-of-sample testing, 129, 142–144 Portfolio optimizers/optimization: Outperformance, 7, 60–61, 130, 157, 178, byproducts of, 94–95 180, 199 inputs to optimization, 87–94 Over-the-counter (OTC) market, 45 objective function, 86–87 Overbought/oversold securities, 28 theoretical influences, 85–86, 91–93 Overfitting, 46, 139–142, 181–182 Position limits, constraints and penalty, Overvaluation, 34, 92 57–59 Positive carry, 34 Pairs trading, 7, 12–13, 42 Positive market impact, 71 Panics, 172 Positive trend, 27 Parsimony, 141 Predictive power, 133–135, 183 Passive orders, 101–103, 190 Price data: Paulson, Henry, 173 defined, 113 Penalty functions, size of position, sources of, 115–116 57–58 Price discovery, 7 Percentage winning trades, 136 Price/earnings-to-growth (PEG) ratio, 35 Performance statistics, 5 Price target, 39 Permutations, 47 Price-to-earnings (P/E) ratio, 7–8, 32–33, Piecewise-linear transaction cost models, 139 75–76 Primary data vendors, 115–117 Playing the market, xiv Princeton/Newport Partners, 29, 170 Popper, Karl, 126, 141 Principal component analysis (PCA), 62 Portfolio bidding, 100 Private equity, 59 Portfolio construction: Profitability, 46 elements of, 53, 188 Profit and loss monitoring, 166–167 importance of, 80, 204 Profit curve, 133 model, see Portfolio construction model Profit margins, 195 quantitative analysis of, 86 Programming errors, 153–154 risk exposure and, 56 Proprietary data vendors/generators, 116 size of portfolio, 21, 189 Proprietary information, 187 techniques, 49 Proprietary trading desk, 3, 6, 161–162 transaction costs, 67–73 Publicly traded companies, 36 Portfolio construction model: Pulte Homes (PHM), 163, 165 balance considerations, 79 elements of, 15, 18 Quadratic curve, 73 goal of, 79 Quadratic transaction cost models, 75–77 information processing, 111 Quality: model risk, 150, 153 investors, 24, 36–37 optimizers, 79–80, 85–95 significance of, 30 P1: a/b P2: c/d QC: e/f T1: g ind JWBT136-Narang July 9, 2009 11:49 Printer: Yet to come

Index 221

Quant(s), generally: Recession economy, 35 characteristics of, 12–14, 17–18 Refitting, 46 collective structure, 205 Regime change: defined, xiii impact of, 182–183 equity investments, 35–36 risk, 154–158, 168, 174–176 evaluation methods, Regulations, importance of, 205 information-gathering, 186–188 Regulators, as data source, 115 funds, 11, 205 Relational database, 122–123, 188 future directions for, 203–205 Relative alpha strategy, 43, 55–56, 60, 90, trading strategies, see Quant strategies 159, 187, 191 evaluation Relative bets, 43 Quant long/short (QLS), 34–36 Relative mean reversion strategy, 154 Quant strategies evaluation: Relative value, 43–44, 164 benefits of, 185–186 Relative value equity arbitrage, 160 components of, 188–191 Renaissance Technologies, 4, 7, 27, edge, 193–196 128–129, 170 Quant trader(s): Resampled efficiency, 93–94 characteristics of, 10 Research: defined, xiii constant, 127 evaluation of, see Quant trader evaluation evaluation methods, 194–195 reference checks, 196–197 idea generation, 127–129 success factors, 10, 191–193, 196–198 importance of, 3, 14–15, 17–18, 38, 46, Quant trader evaluation: 58, 125, 146 acumen, 191–193 mistakes in, 146 edge, 193–196, 201 organization of, 188, 201 integrity, 196–198 scientific method, 125–127, 140, 195 portfolio fit, 198–201 strategy development and, 189 questions to ask, 189–190 testing strategies, 129–145 Quantitative , 4 Researchers, success factors, 142 Quantitative risk models, 57 Resource allocation, 185 Quantitative trading system: Retail investors, 77, 161 benefits of, 7–8, 55 Return vs. risk ratios, 136–137 conception of 12–13 Returns: high-frequency of, 3 expected, 39, 54, 57, 87, 95 implementation of, 6, 10, 18 risk-adjusted, 86, 136, 154, 182 long/short positions, 34–36 risk vs., ratios of, 136–137 performance of, 3–4 total, 45, 139 risk measurement/mismeasurement, variability of, 133, 136 8–10 Risk, generally: strategy evaluation, see Quant strategies aversion, 63, 86 evaluation exposure to, see Risk exposure typical structure of, 14–18 management, 13–14, 17–18, 33, 55, 182, volume per day, 5 204 Quasi-quant traders, 14 measurement, 58, 81, 162 mitigation, 61 Ratings agencies, 173 model of, see Risk models Real estate, 173 monitoring, 166–168, 189 Real-time data, 117, 120 -taking strategies, 6, 65 Real-time runs, 47 targeting, 162 Rebate programs, 72 underestimation of, 171–174 P1: a/b P2: c/d QC: e/f T1: g ind JWBT136-Narang July 9, 2009 11:49 Printer: Yet to come

222 INDEX

Risk exposure: Securitized mortgages, 150–151 common investor risk, 159–166, 168 Security Analysis (Graham/Dodd), 34 contagion risk, 159–166, 168 Security master, 116 exogenous shock risk, 158–159, 168 SEDOL code, 116 impact of, 8, 15, 22, 55–56, 60, 65, Sell orders, 5, 71, 101 regime change risk, 154–158, 168, Sell strategies, 29 174–176 Sensitivity, 139 types of, 149, 168 Sentiment-based strategy, 36, 154–158 Risk models: Sentiment data, 114 amount of risk, limitations of, 57–60 Settlement, 68–69 characteristics of, 15–16, 18, 56, 67, 153 Seykota, Ed, 27, 170 information processing, 111 Shark strategy, 106 types of risk, limitations of, 60–64 Sharpe, William, 86, 136 Risk-adjusted return, 86, 136, 154, 182 , 136 Risk-adjusted returns, 86 Shaw, David, 29 Risk-free rate, 136 Shell, 160 Riskless profit, 6 Short positions, 34, 164 Rolling out-of-sample technique, 142 Short selling, 13, 25, 37, 51, 55, 60, 144, Rolling yearly correlation, 89 163, 174 Rotation models, blending alphas, 51–52 Short-term strategies, 41, 47 Rothman, Matthew, 177 Short-term traders, 174 Royal Dutch, 160 Signal-mixing models, of alphas, 53 R-squared (R2), 135, 142 Signal strength, 39 Rube Goldberg device, 11 Silver, 42 Rule-based portfolio construction models: Simons, James, 7, 28 alpha-driven weighting, 82–83, 95 Simulation, 92. See also Monte Carlo decision-tree models, 83–85 simulation equal position weighting, 80–81, 93 Size-limited models, 58–59 equal risk weighting, 81–82, 95 60-day moving average, 41 overview of, 80, 85, 97 Size of position: Rumors, economic impact of, 14, 159 limiting, 57 Run frequency, 39, 46–47, 201 market impact and, 70–71, 167 Russian debt crisis, 9, 160, 172 significance of, 189 transaction costs, 77–78 Salomon Brothers, 108 Slippage, 69–72, 144, 167 S&P 500, xiv, 13, 16, 26–27, 28, 31, 40–41, Slow-moving strategies, 47 88–89, 94, 121, 131–135, 138–139, Small-cap stocks, 61, 77, 82 151, 158, 181 Smart order routing, 104–105 S&P Growth index, 156–157 Sniper strategy, 106 S&P Value index, 156–157 Software programs: Scientific method, 125–127, 140, 195 alpha model, see Alpha model(s) Scientists, types of, 23 back-testing, 150 Secondary data vendors, 115–117 checking data errors, 120 Secretive quantitative trading strategies, 12 CRM, 180 Sector: FIX protocol, 108 bets, 56, 60 implementation errors, 153–154 defined, 122 risk exposure monitoring, 166 risk, 61 risk models, 64 Securities and Exchange Commission (SEC), transaction cost estimates, 73 159, 174 Soros, George, 172 P1: a/b P2: c/d QC: e/f T1: g ind JWBT136-Narang July 9, 2009 11:49 Printer: Yet to come

Index 223

Specialists/specialization, 44, 47, 73 10-Qs, 120 Spike filters, 118–119 Terrorist attacks, economic impact of, 14, Sponsored access, 107 158–159, 172, 180 SPY, 121 Tertiary data vendors, 116–117 Standard deviation, 58, 133, 151 Testing, in research: Standard operating procedures, 14 assumptions of, 144–145 Statistical arbitrage (stat arb), 7, 12, 29, 164, “good” model, 131–139 178, 195, 203 importance of, 129 Statistical learning algorithms, 107 in-sample, 129–131, 142–144 Statistical significance, 63 out-of-sample, 129, 142–144 Statistical techniques, 42–43, 61–62 overfitting, 139–142 Sterling ratio, 136 Theoretical scientists, 23–24 Stochastic volatility modeling, 87 Theory development. See Idea generation Stock market crash, of 1987, 4–5, 174 Theory-driven alpha model: Stock/stock investment, ranking of, 34–35 applications of, 22–24 Stock/stock market: blending with data-driven model, 48–53 alpha model applications, 47 fundamental data, 30–37 asynchronicity, 121–122 implementation of, 38–48, 200, 204 decline in 2008, 173, 177 price-related data, 24–30 fundamental data, 114 taxonomy of, 25, 48, 200 influential factors, 8, 62–63 Theory-driven approaches. See risk management, 59 Theory-driven alpha model; stock picking, 189 Theory-driven risk models stock splits, 119 Theory of gravity, 126 Stop-limit orders, 101 Thorp, Ed, 29 Stop-loss policy, 129 Time decay, 137–139, 195 Structural edges, 195–196 Time horizon, 30, 38–42, 45, 47, 176, 181, Supply and demand, 6, 29, 70–72 190, 199–201 Swaps, 44 Time period, winning, 133 Swensen, David, 11–12 Timestamps, 120 Systematic approach: Total return, 45, 139 abandonment of, 13 Trade deficits, 25 alpha model, 44 Trading desk. See Proprietary trading desk defined, xiii Trade execution. See Execution frontiers of, 13 Trading periods, amount of, 181–182 futures trading, 20 Transaction cost: to problem solving, 7 commissions, 68–69 trading rules, 129 defined, 68 Systematic decisions, 204 determination of, 190 Systematic investors, 10 estimation of, 73–75, 77, 144–145 , 61–62, 64 execution and, 101–102 Systems performance monitoring, 166–168 fees, 69, 100, 144 graphs, 73–76 TABB Group, 5 high-frequency trading, 105–107, 144 Tartaglia, Nunzio, 29 influential factors, 166 Technical traders, 129 market impact, 44, 46, 53, 70–72, 144, Technological advances, economic impact of, 167 114 minimization of, 68, 77, 101 Technology sector, 90 models, see Transaction cost models Technology stocks, 56, 82 slippage, 69–72, 144, 167 P1: a/b P2: c/d QC: e/f T1: g ind JWBT136-Narang July 9, 2009 11:49 Printer: Yet to come

224 INDEX

Transaction cost models: Value-hunting strategy, 56 construction considerations, 71 Value investing, 22, 30–35, 43, 55–56 elements of, 15–16, 187 Value stocks, 156–157, 161, 175–176 information processing, 111 Variability of returns, testing metric, 133, model risk, 150 136 overview of costs, 68–72 Variance, 86 role of, 67–68, 77 Visible orders, 104, 190 types of, 72–77 VIX, 172 Transaction data, 204 Volatility: Trend following: day-to-day management of, 56 development of, 127 defined, 58 evaluation of, 194 forecasts, 93 exogenous shock risk, 158–159 historical, 172 futures market, 15 impact of, 162–163 implications of, 15–17, 21–22, 25–28, 39 portfolio construction and, 82, 86–88, 97 moving average and, 40–41 prediction of, 135 slippage, 69–70 risk model, 59 Trend indentification, 22 risk underestimation and, 171–172 Trend model, 51 significance of, 72 Trend reversals, 81, 154, 157–159, 176, 199 transaction costs and, 73, 77 Trust-building, 186–187, 201 Volume, significance of, 3, 5, 12, 113 Tulip mania, 28 Turnover rate, 18 Wall Street, 9 Turtles program,128 War, economic impact of, 62, 159, 172 Two Sigma, 128, 170 Weak-growth economy, 35 Weighting: Uncertainty, 25, 56, 58, 133 impact of, 51–52 Underperformance, 22, 60, 79, 130, 157, in portfolio construction, 80–83 178, 199 portfolio optimization, 92–93 Undervaluation, 22, 29, 33–34 White paper, 108 Unequal weighting, 80–81 William of Occam, 141 U.S. Treasuries: Winning trades: Treasury bills, 70 characteristics of, 192 Treasury bonds, 62 testing metric, 136 Treasury notes, 131 Working capital, 205 10-year Treasury Notes, 16 WorldCom, 36 Unwinding positions, 165 World markets, 4 Uptrends, 71 Worst-case scenarios, 171 Upward triangle pattern, 129 Worst peak-to-valley (s), 133–134, 136 Valuation ratios, 100 (VaR), 59, 151, 161–163, 171 Yield: Value funds, 23 implications of, 30–35 Value/Growth spread, 156–158, 175–176 model, 51–52