GREAT INVESTMENT IDEAS World Scientific Series in Finance (ISSN: 2010-1082) Series Editor: William T Ziemba (University of British Columbia (Emeritus), ICMA Centre, University of Reading and Visiting Professor of University of Cyprus, Luiss Guido Carli University, Rome, Sabanci University, Istanbul and Korea Institute of Science and Technology) Advisory Editors: Greg Connor (National University of Ireland, Maynooth, Ireland) George Constantinides (University of Chicago, USA) Espen Eckbo (Dartmouth College, USA) Hans Foellmer (Humboldt University, Germany) Christian Gollier (Toulouse School of Economics, France) Thorsten Hens (University of Zurich, Switzerland) Robert Jarrow (Cornell University, USA) Hayne Leland (University of California, Berkeley, USA) Haim Levy (The Hebrew University of Jerusalem, Israel) John Mulvey (Princeton University, USA) Marti Subrahmanyam (New York University, USA) Published*: Vol Euro Bonds: Markets, Infrastructure and Trends by Marida Bertocchi (University of Bergamo, Italy), Giorgio Consigli (University of Bergamo, Italy), Rita D’Ecclesia (University of Sapienza, Rome, Italy), Rosella Giacometti (University of Bergamo, Italy), Vittorio Moriggia (University of Bergamo, Italy) & Sergio Ortobelli (University of Bergamo, Italy) Vol Risk, Value and Default by Oliviero Roggi (University of Florence, Italy & New York University, USA) Vol Great Investment Ideas by William T Ziemba (UBC & London School of Economics, UK & Korea Institute of Science and Technology, Korea) Forthcoming Problems in Portfolio Theory and the Fundamentals of Financial Decision Making by Leonard C MacLean (Dalhousie University, Canada) & William T Ziemba (University of British Columbia, Canada) Quantitative Methods in Risk Analysis: A Practitioner’s Guide by Michael Foster & Leonard MacLean *To view the complete list of the published volumes in the series, please visit: www.worldscientific.com/series/wssf GREAT INVESTMENT IDEAS William T Ziemba University of British Columbia (Emeritus) and London School of Economics Published by World Scientific Publishing Co Pte Ltd Toh Tuck Link, Singapore 596224 USA office: 27 Warren Street, Suite 401-402, Hackensack, NJ 07601 UK office: 57 Shelton Street, Covent Garden, London WC2H 9HE British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library World Scientific Series in Finance — Vol GREAT INVESTMENT IDEAS Copyright © 2017 by World Scientific Publishing Co Pte Ltd All rights reserved This book, or parts thereof, may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written permission from the publisher For photocopying of material in this volume, please pay a copying fee through the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA In this case permission to photocopy is not required from the publisher ISBN 978-981-3144-36-1 ISBN 978-981-3144-37-8 (pbk) Desk Editor: Alisha Nyugen Typeset by Stallion Press Email: enquiries@stallionpress.com Printed in Singapore Contents Preface Acknowledgements Comment on “Why a Weekend Effect?” William T Ziemba The Effect of Errors in Means, Variances, and Covariances on Optimal Portfolio Choice Vijay K Chopra and William T Ziemba The Turn-of-the-Month Effect in the U.S Stock Index Futures Markets, 1982–1992 Chris R Hensel, Gordon A Sick and William T Ziemba Stock Ownership Decisions in Defined Contribution Pension Plans Julian Douglass, Owen Wu and William Ziemba The Symmetric Downside-Risk Sharpe Ratio and the Evaluation of Great Investors and Speculators William T Ziemba The Predictive Ability of the Bond Stock Earnings Yield Differential Model Klaus Berge, Giorgio Consigli and William T Ziemba Do Seasonal Anomalies Still Work? Constantine Dzhabarov and William T Ziemba How Does the Fortune’s Formula-Kelly Capital Growth Model Perform? Leonard C MacLean, Edward O Thorp, Yonggan Zhao and William T Ziemba Great Investors: Their Methods, Results and Evaluation Olivier Gergaud and William T Ziemba 10 Is the 60-40 Stock-Bond Pension Fund Rule Wise? William T Ziemba 11 When to Sell Apple and the NASDAQ? Trading Bubbles with a Stochastic Disorder Model A N Shiryaev, M V Zhitlukhin and W T Ziemba 12 A Response to Professor Paul A Samuelson’s Objections to Kelly Capital Growth Investing William T Ziemba Index Preface This book contains twelve articles with great investment ideas These papers were published in the Journal of Portfolio Management from 1993 to 2015 In “The Effect of Errors in Mean, Variance and Co-Variance Estimates on Optimal Portfolio Choice” by Vijay Chopra and me, we investigate the effect of errors in means, variances and co-variances in portfolio selection problems Earlier in 1981 and 1984 papers with my Ph.D student Jerry Kallberg from University of British Columbia, I found that the impact of errors on expected utility were about 20:2:1 for means, variances and covariances, respectively So errors in means were much more important than variance or co-variance errors and variance errors were about twice as important as co-variance errors In this paper done with Chopra while I was consulting at the Frank Russell Company from 1989–1998, until they sold the company, we redid the earlier studies on new data and investigated the impact of risk aversion on these relative errors The main result is that the lower the Arrow-Pratt risk aversion is, the greater is the impact so with utility like log with essentially zero risk aversion, the relative errors are more like 100:3:1 During 1988–1989, I was fortunate to be the first visiting Yamaichi Professor of Finance at the University of Tsukuba in Japan and consultant to the Yamaichi Research Institute in Tokyo There I studied stock market crashes and stock market anomalies That led to three books and a number of research papers In “Comment on ’Why a Weekend Effect?”’, I tested Miller’s weekend hypothesis for the Japanese stock market Miller argued that the weekend effect could be explained by a tendency for self-initiated sell orders to exceed self-initiated buy trades over the weekend, while brokerinitiated buy trades result in a surplus of buying during the remainder of the week This causes security prices to fall over the weekend and during the day on Monday as market makers sell back stocks on the open Prices then move higher during the week because of broker-induced buying This, like most anomalies, is strongest for small cap stocks Miller’s idea is predicated on the fact that people are too busy to think about stocks during the week If they anything it is to buy based on brokers’ recommendations Miller did not test his theory with real data I tested the theory using daily Japanese data from May 16, 1949 to September 28, 1988 Individual investors were not selling stocks in Japan as well as the US 1981–1989 At that time, there were one and two day weekends with Saturday trading two weeks each month Saturday returns were high What I found was that in two day weekends the Monday fell on average but on the one day weekend the fall was on Tuesday Studies in the US, Japan and other countries tend to rise at the turn of the month (TOM) The reasons seem to be institutional since pensions and other investments are made then, and employees receive their salaries then so the excess cash flows have a tendency to go to a large extent into the stock market In the US, the TOM is trading days −1 to +4, where −1 is the last trading day of the previous month and +1 the first trading day of the current month In Japan, the TOM has been −5 to +2 when the salaries are paid, see Calendar Anomalies and Arbitrage, published in 2012, by World Scientific In “Investment Results from Exploiting Turn of the Month Effects”, Chris Hensel and I discuss these results from 1928 to 1993 Three of the TOM days had significantly high returns on average while no other days in the month had excess returns Overall, all days returned 0.0186% daily, the TOMs returned 0.1236 (with a t = 5.94), the first half namely trading days −1 to +9 returned 0.0703((4.13) and the rest of the month returned −0.0235(−3.71) The paper discusses the use of these results by institutional investors for buying and selling timing for the S&P500 and also for small cap stocks and other assets A story about this paper is in the November 7, 1996 the Wall Street Journal One of the biggest Wall Street scandals was the rise and demise of ENRON They were an energy trading company and rose from modest beginnings to become one of the most valuable US companies There was fraud and eventually the stock value collapsed to essentially zero In “Stock Ownership Decisions in Defined Contribution Pension Plans”, Julian Douglass, Owen Wu and I studied the effects of this decline on the employees’ pensions Most of the employees had their pensions solely in ENRON stock This was all lost Also they lost their jobs In the paper, we analyse with two models (a static mean variance and a stochastic programming) when it is optimal to have most of one’s pension in a single MacLean, L., W T Ziemba, and G Blazenko (1992) Growth versus security in dynamic investment analysis Management Science, 16, 1562–85 MacLean, L C., R Sanegre, Y Zhao, and W T Ziemba (2004) Capital growth with security Journal of Economic Dynamics and Control, 16(4), 937–954 MacLean, L C., E O Thorp, Y Zhao, and W T Ziemba (2011) How does the Fortune’s FormulaKelly capital growth model perform? Journal of Portfolio Management, 16(4), 96–11 MacLean, L C., Y Zhao, and W T Ziemba (2015) Optimal capital growth with convex shortfall penalties Quantitative Finance (in progress) MacLean, L C., W T Ziemba, and Y Li (2005) Time to wealth goals in capital accumulation and the optimal trade-off of growth versus security Quantitative Finance, 16(4), 343–357 Markowitz, H M (1976) Investment for the long run: New evidence for an old rule Journal of Finance, 16(5), 1273–1286 Markowitz, H M (2006) Samuelson and investment for the long run Samuelsonian Economics and the Twenty-first Century, pp 252–261 New York, NY: Oxford University Press Merton, R C and P A Samuelson (1974) Fallacy of the log-normal approximation to optimal portfolio decision-making over many periods Journal of Financial Economics, 16, 67–94 Mossin, J (1968) Optimal multi period portfolio policies Journal of Business, 16(2), 215–229 Ohlson, J (1975) The asymptotic validity of quadratic utility with trading interval approaches zero In W T Ziemba and R G Vickson (Eds.), Stochastic Optimization Models in Finance, pp 221–234 New York, NY: Academic Press Pabrai, M (2007) The Dhandho Investor: The Low-risk Value Method to High Returns New York, NY: Wiley Poundstone, W (2005) Fortune” Formula: The Untold Story of the Scientific System that Beat the Casinos and Wall Street New York, NY: Hill and Wang Roll, R (1973) Evidence on the growth optimum model The Journal of Finance, 16(3), 551–566 Rubinstein, M (1976) The strong case for the generalized logarithmic utility model as the premier model of financial markets Journal of Finance, 16(2), 551–571 Samuelson, P A (1963) Risk and uncertainty: a fallacy of large numbers Scientia (6th Series, 57th year, April-May), 153–158 Samuelson, P A (1969) Lifetime portfolio selection by dynamic stochastic programming Review of Economics and Statistics, 16, 239–246 Samuelson, P A (1970) The fundamental approximation theorem of portfolio analysis in terms of means, variances and higher moments Review of Economic Studies, 16(4), 537–542 Samuelson, P A (1977) St Petersburg paradoxes: Defanged, dissected and historically described Journal of Economic Literature, 16(1), 24–55 Samuelson, P A (1979) Why we should not make mean log of wealth big though years to act are long Journal of Banking and Finance, 16, 305–307 Samuelson, P A (1991) Long-run risk tolerance when equity returns are mean regressing: Pseudoparadoxes and vindication of businessmen’s risk In W C Brainard, W D Nordhaus, and H W Watts (Eds.), Money, Macroeconomics and Economic Policy, pp 181–200 Cambridge, MA: MIT Press Samuelson, P A (various) Letters to W T Ziemba, December 13, 2006, May 17, 2007, and May 12, 2008 Siegel, L B., K F Kroner, and S W Clifford (2001) Greatest return stories ever told Journal of Investing, 16(2), 91–102 Sommer, L (1975) Translation of an exposition of a new theory on the measurement of risk by D Bernoulli (1738) Econometrica, 16, 23–36 Thorp, E O (1960) Beat the Dealer New York, NY: Random House Thorp, E O (2010) Understanding the Kelly criterion Wilmott Thorp, E O (2011) The Kelly criterion in blackjack, sports betting and the stock market In L C MacLean, E O Thorp, and W T Ziemba (Eds.), The Kelly Capital Growth Investment Criterion, pp 789–832 Singapore: World Scientific Thorp, E O and Whitley (1972) Concave utilities are distinguished by their optimal strategies Colloquia Mathematica Societatis Janos Bolyai pp 813-830 Ziemba, R E S and W T Ziemba (2013) Investing in the Modern Age Singapore: World Scientific Ziemba, W T (2005) The symmetric downside risk sharpe ratio and the evaluation of great investors and speculators Journal of Portfolio Management Fall, 108–122 Ziemba, W T (2012a) Calendar Anomalies and Arbitrage Singapore: World Scientific Ziemba, W T (2012b) Stochastic programming and optimization in horserace betting In H I Gassman and W T Ziemba (Eds.), Stochastic Programming Applications in Finance, Energy and Production, pp 221–256 Singapore: World Scientific Ziemba, W T (2016) Exotic Betting at the Racetrack Singapore: World Scientific Ziemba, W T and D B Hausch (1984) Beat the Racetrack San Diego, CA: Harcourt Ziemba, W T and D B Hausch (1986) Betting at the Racetrack Dr Z Investments, Inc Ziemba, W T and D B Hausch (1987) Dr Z’s Beat the Racetrack William Morrow 1Berlekamp was a main intellectual force in the Renaissance Medallion hedge fund, arguably the world’s most successful hedge fund, see Gergaud and Ziemba (2012) Later he was a professor of mathematics at the University of California, Berkeley 2For one or two assets with fixed odds, take derivatives and solve for the optimal wagers; for multiasset bets under constraints; and when portfolio choices affect returns (odds), one must solve a stochastic nonlinear program which, possibly, is non-concave 3See Harry Markowitz’s proof in Ziemba (2003) and the more general proof of Thorp (2011) and the graphs in MacLean, Ziemba and Blazenko (1992) Index A AAPL Rises and Falls, 232 Alan Greenspan, 94, 236 Algoet and Cover, 258 algorithmic trading, 184 all forecasts equal, 23 Amarath, 151 annual size premium, 126 Apple Computer stock, 232 Apple price bubble, 235 arbitrage, 58 Arrow-Pratt absolute risk aversion, 150 Arrow-Pratt risk aversion, vii Arrow-Pratt risk aversion index, 51 asymptotic rate of asset growth, 149 B bad news is delayed, 27 Bank of Canada, 99 Barack Obama, 216 Bayesian approach, 246 Beat the Dealer, 270 Bicksler and Thorp Example I, 161 Bicksler-Thorp Example I, 155 Bicksler-Thorp Example II, 155, 165 bid-ask spreads, 32 Bill Gross, 251, 270 bond prices, 112 bond stock prediction model, 93 BSEYD in the danger zone, 108 BSEYD Model, xi, 94, 106, 238, 240, 242 bubble, xii buy and hold, 132 buy and hold strategies, 109 buy orders, Buy/Sell Ratios, C 1987 crisis, 107 Calendar Anomalies and Arbitrage, viii Canada had 12 corrections, 101 Capital Asset Pricing Model, 49, 53 cash equivalent, 17–19 cash equivalent loss, 21 cash flow, 28, 140 Cash returns, 229 Chicago Board of Trade, 29 Chicago Mercantile Exchange, 29 Chris Hensel, xii Clinton’s first election, 219 Clinton’s second term, 216 co-integration of the FED model, 115 co-movement, 111 Commodity Corporation, 182 Commodity Pool Operators, 190, 194 Commodity Trading Advisors, 190, 196 company stock, 49, 54, 62 concave utility, 56 Congressional Effect, 225 Constantine Dzbaharov, xi convex function of wealth shortfall, 57 Corning stock, 48 Corrections in the UK 1980–2005, 110 Corrections in the US 1980–2005, 110 covariances, 16 Cox process, 114 creating scenarios, 58 CRSP equal-weighted index, 225 CRSP value-weighted index, 225 D danger zone, xi, 97, 109 Daniel Bernoulli, 148 data snooping biases, 44 day-of-the-week effect, 5, 28 DC plan investors, 61 Declining inflation, 112 Defined Contribution Plan Assets, 48 discount factor of future earnings, 95 discretionary wealth, 151 Disney, 234 distribution of final wealth, 168 Dow Jones industrials, 137 downside symmetric information ratio, 181 Downside Symmetric Sharpe Ratio, 180 E earnings expectations, 106 Edward O Thorp, x Edward Thorp, 250 effect of errors in mean, vii, 150 effect of our bets on the odds (prices), 149 effect of transactions costs, 267 efficient market theory, 125 Election Cycles, 214 Elwyn Berlekamp, 250 employee’s problem, 51, 59 endowment of King’s College, Cambridge University, 178 ENRON, viii, 47, 48 entry and exit signals, 109 equity market entry and exit values, 100 Equity Yield, 95 errors in covariances, 19 errors in means, 16, 19 errors in means and variances, 21 errors in variances, 16, 19 ex-dividend dates, 31 ex-dividend explanation does not hold, 31 excess investment in company stock, 62 expected utility capital growth model, 266 expected utility of wealth, 17 exponential, 214 exponential GARCH Intervention model, 214 extreme events, 60 extreme negative event, 56 extreme scenario, 60 F FED model, 94, 112, 113 Federal Reserve, 107 Fidelity Investments, 250 final wealth is not normally distributed, 157 final wealth levels, 254 Final Wealth Statistics, 256 Final Wealth Trajectories, 255 financial engineering, xii first half of the month, 25, 27, 28, 137 first visiting Yamaichi Professor of Finance at the University of Tsukuba, vii Five Investors in the Samuelson Experiment, 262 Ford Foundation Endowment, ix Fortune’s Formula, xi, 147, 152, 271 Fractional Kelly Strategies, 151, 152 Frank Russell Company, vii full Kelly, 148, 154 Fund of Funds, 190 fundamental factor model, 125 futures market, 140 futures market in Singapore, 27, 141 G GARCH Intervention model, 214 Gauss Poisson process, 114 geometric Brownian motion, 245, 247 geometric mean, 179 geometric mean gain, x George Soros, xii, 231, 232, 269 George W Bush, 216 Germany had 13 corrections, 101 GJ Investment Fund, 185 graph of AAPL prices, 236 great investors and hedge fund managers, 175 Growth of $1 Investment, 40 H half Kelly, 252, 261 half Kelly betting, 253 Harville probability, 267 Hedge Funds, 190, 202 high company stock holdings, 62 high price earnings ratios, 236 high return expectations, 50 higher average returns in January, 140 higher DSSRs, x higher risk aversion, 61 highest and lowest final wealth trajectories, 167, 254 historical range of BSEYDs, 98 holiday, xi Holiday Effects, 135 human capital, 55 I Ida May Fuller, 261 iid investments, 258 index arbitrage, 28, 31 Individual investors, viii inflation, 219 Internet index, 242 Invest Japan, ix investment rule, 213 J James Simons, 184 January and Monthly Effects, 126 January Barometer, xi, 131 January effect, 126 January turn-of-the-year, 127 Janus, 270 Japan had seven corrections, 101 Jim Simons, 251, 268 job risk, 50 John Maynard Keynes, 178, 269 June 1992 crisis, 111 K Kelly bettor cannot go bankrupt, 154 Kelly Capital Growth Investment Strategy, xiii, 249 Kelly criterion optimizer, 261 King’s College Cambridge endowment, 269 L large cap stocks with Republicans, xii large-capitalized, Larry Siegel of the Ford Foundation, 269 Lawrence Siegel, ix Lintner, 182 log utility, 150 log-normal wealth, 157 log-wealth, 160 logit function, 61 lognormal investments, 253 Logos Trading Inc, 185 long bond yield, 241 Long Term Capital’s 1998 demise, 156 long term optimal growth rate, 151 low interest rate policy, 236 low interest rates, 109 low price earnings ratios, 236 low risk aversion, 50, 150 LTCM, 151 lunch with Warren Buffett, 150 M 60/40 mix, 216 MacLean and Ziemba, 264 Madoff, 188 mark-to-market, 31 Market corrections, 102 market index, 179 market makers, Markowitz, 259 maximizes the asymptotic long run growth rate, 148 maximizing the long run exponential rate of asset growth, 149 maximum and minimum final wealth trajectories, 161 maximum expected log strategy, 178 mean excess return, 119 mean return, 25 mean-reversion, 241 mean-standard deviation trade-off, 169 mean-variance, 50, 58 mean-variance analysis, 15 Medallion Fund, 185 Miller’s Hypothesis, Miller’s weekend hypothesis, vii minimizes the time to achieve asymptotically large investment goals, 148 minimizing the time to large asymptotic goals, 249 mis-specification, 15 Misspecification of the parameters, 16 Mohnish Pabrai, 150, 269 Monday declines, Monday returns, Monte Carlo integral approximation, 58 monthly effect, 131 mood-based hypothesis, 225 Morningstar, 170 Motley Fools, 170 Multi-Strategy Funds of Funds, 201 N Nasdaq (NDX100), 232 Nassau trend follower, 269 negative exponential utility, 18 Negative January Returns, 132 negative power utility, 150 negative returns in October, 44 NeXT platform, 234 Niederhoffer, 151 Nikkei stock average, 135 no company stock holdings, 53 normal distribution, 56 normality test, 42 O one-day weekends, ordinary Sharpe ratio, ix over betting, 271 own company stock, 47 own company stock ownership, 52 P 60/40 portfolio investment strategy, 226 Paul Samuelson, 171 Paul Samuelson was a critic, xiii, 249 pension fund, xii, 47 Performance of the Chest Fund, 179 Performance of the Strategies for Canada, 121 Performance of the Strategies for Germany, 120 Performance of the Strategies for Japan, 123 Performance of the Strategies for the UK, 122 Performance of the Strategies for the US, 119 Petroleo Brasileiro, 179 Pixar, 234 Political Effects, 224 political party effect, 227 Ponzi Scheme, 188, 191 positive January, 132 positive power maximizer, 261 positive power utility, 151 Potash Corporation of Saskatchewan, 179 power law, 57 preferences on infinite sequences of wealth, 257 preholiday, 6, 136 Presidential Election Cycle effect, 214 Princeton Newport, x Princeton Newport hedge fund, x probability of termination, 61 Proportional Investment Strategies, 155 pseudorandom sequences, 60 Q Quantum Fund of George Soros, ix Quantum hedge fund, 242 R racetrack place and show bets, 266 Rachel Ziemba, x Renaissance Medallion, x, 183 Renaissance Mediallion, 251 rent-seeking hypothesis, 226 risk aversion parameter, 53 risk factor, 59 risk premium, 113 risk tolerance, 22 Roubini Global Economics, x S 60-40 stock-bon, 213 Samuelson Investors, 261 Saturday returns were high, viii Saturday trading, viii, 6, 10 Saturday trading in Japan ceased, 10 seasonal anomalies in Japan, 125 second half of the month, 26, 137 second week of the month, 26 sell orders, sell-in-May-and-go-away, xi seminal application of the Kelly strategy, 268 sensitive to errors, 15 Shapiro-Wilks W statistic, 42 Sharpe ratio, 54, 180 Shiryaev and Zhitlukhin model, 245 Shiryaev and Zhutlukhin, xii, 231 Shiryaev–Roberts statistic, 246 short term predictive power, 106 shrinking breadth, 241 Simex in Singapore, 27, 140 similar levels of risk aversion, 16 Simple Presidential Investment Strategies, 226 simulations, 179, 253 skewness and kurtosis, 157 small cap effect, 215 small cap outperformance, 127 Small cap stocks returned, 218 small cap stocks with Democrats, xii small cap/large cap spread, 127 Small capitalized stocks, 135 small caps underperform, 127 small stock advantage under Democrats, 218 small stock/large stock differential, 227 small-capitalized, Static Portfolio Choice, 51 Steve Jobs, 234 stochastic programming, 50 stochastic programming model, 60 Stock Market Corrections in Canada, 121 Stock Market Corrections in Germany, 120 Stock Market Corrections in Japan, 123 Stock Market Corrections in the UK, 122 Stock Market Corrections in the US, 119 stock price rises, 236 stopping time, 246 strong monthly seasonality effect, 32 Summary of Kelly Applications, 265 Swensen’s strategies, 176 symmetric downside Sharpe ratio, x S&P 500 Index, 29 T tail utility function, 258 The Stock Traders Almanac, 126 theoretical market price, 114 theoretical S&P500 fair value, 113 Thorp, 188 Tom Schneeweis, x trade-off between wealth growth and risk, 160 Trading bubbles, 232 Trading constraints, 62 Trajectories with Final Wealth Extremes, 158 trend reversal, 235, 245 turn of the month, viii, 137, 140 turn of the month effect, 139 turn of the month in Japan, 27 turn-of-the-month, 25, 39 turn-of-the-month effect, xi, 26, 136 turn-of-the-year small firm effect, 215 two day weekends, viii, U UK had 15 corrections, 101 UK market corrections, 109 UMASS DHF Database, 176, 193 UMass hedge fund database, x United Airlines, 49 University of Massachusetts derivative, 176 US Bond Returns, 221 utility maximizing investor, 51 V value investing, 188 Value Line Cash Index, 38 Value Line Composite, 29 Value Line Composite futures contract, 30 value line index, 138 Victor, with a linear utility, 263 Victor, 261 Victor Niederhoffer, 261 volatility of individual stocks, 49 W Warren Buffett, 269 Warren Buffett’s Berkshire Hathaway, ix wealth paths, 153, 176 Weekend Effect, vii, weekend hypothesis, weight in the tails, 59 When to Sell Apple, xii Wilcox, 151 Wilmott magazine, x Windsor Fund of George Neff, ix Y Yale endowment, 176 Yale Endowment Highlights, 177 10 Year Interest Rate, 95 10 year rate, 107 yield spread, 106 Z Ziemba and Hausch Model, 156 Ziemba and Vickson, 250 ... 194 9, to December 28, 198 8 The data are broken into 475 ten-year subperiods beginning with May 194 9 to April 195 8 and ending with January 197 9 to December 198 8 Exhibit shows that individual investors... Reinganum ( 198 1), Keim ( 198 3, 198 9), Roll ( 198 3), Ritter ( 198 8), Ritter and Chopra ( 198 9), and Jaffe, Keim, and Westerfield ( 198 9) Surveys of this literature appear in Thaler ( 198 7ab), Ziemba ( 199 3a),... Ariel ( 199 0), Cadsby and Ratner ( 199 1), Pettengill ( 198 9), and Ziemba ( 199 1) Monthly effects are studied by Gultekin and Gultekin ( 198 3), Brown, Kleidon, and Marsh ( 198 3), Jacobs and Levy ( 198 8),