Cover image for A non-random walk down Wall Street
Title:
A non-random walk down Wall Street
Author:
Lo, Andrew W. (Andrew Wen-Chuan)
Publication Information:
Princeton, N.J. : Princeton University Press, [1999]

©1999
Physical Description:
xxiii, 424 pages : illustrations ; 25 cm
Language:
English
ISBN:
9780691057743
Format :
Book

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Central Library HG4915 .L6 1999 Adult Non-Fiction Non-Fiction Area
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Summary

Summary


For over half a century, financial experts have regarded the movements of markets as a random walk--unpredictable meanderings akin to a drunkard's unsteady gait--and this hypothesis has become a cornerstone of modern financial economics and many investment strategies. Here Andrew W. Lo and A. Craig MacKinlay put the Random Walk Hypothesis to the test. In this volume, which elegantly integrates their most important articles, Lo and MacKinlay find that markets are not completely random after all, and that predictable components do exist in recent stock and bond returns. Their book provides a state-of-the-art account of the techniques for detecting predictabilities and evaluating their statistical and economic significance, and offers a tantalizing glimpse into the financial technologies of the future.


The articles track the exciting course of Lo and MacKinlay's research on the predictability of stock prices from their early work on rejecting random walks in short-horizon returns to their analysis of long-term memory in stock market prices. A particular highlight is their now-famous inquiry into the pitfalls of "data-snooping biases" that have arisen from the widespread use of the same historical databases for discovering anomalies and developing seemingly profitable investment strategies. This book invites scholars to reconsider the Random Walk Hypothesis, and, by carefully documenting the presence of predictable components in the stock market, also directs investment professionals toward superior long-term investment returns through disciplined active investment management.



Author Notes

Andrew W. Lo is the Harris & Harris Group Professor of Finance at the Sloan School of Management, Massachusetts Institute of Technology
A. Craig MacKinlay is Joseph P. Wargrove Professor of Finance at the Wharton School, University of Pennsylvania


Reviews 1

Choice Review

The phrase "random walk" was initially applied to security prices by Paul Samuelson in 1965 but was popularized by Burton Malkiel, whose A Random Walk Down Wall Street is a popular press classic currently in its sixth edition (CH, Apr'96). Malkiel explained how successive stock prices are independent of each other, and because stock prices patterns are virtually nonexistent, studying past price behavior would not lead to superior investment results. The obvious investment strategy becomes buy-and-hold, which minimizes transaction costs and taxes, or the acquisition of index funds that track the market. This book mimics Malkiel's title, but any similarity in volumes immediately ends after the introduction. Instead, in this collection of their research papers Lo (Sloan School, MIT) and MacKinlay (Wharton School, Univ. of Pennsylvania) shows security prices do not necessarily follow a random walk (i.e., they are predictable). While the authors readily admit achieving superior investment returns remains exceedingly difficult, their position is that such performance is possible. Since this book brings together important research on efficient financial markets, it should be in every library serving graduate programs in investments. General readers and most undergraduates will find most of the material virtually unfathomable. H. Mayo; The College of New Jersey


Table of Contents

List of Figuresp. xiii
List of Tablesp. xv
Prefacep. xxi
1 Introductionp. 3
1.1 The Random Walk and Efficient Marketsp. 4
1.2 The Current State of Efficient Marketsp. 6
1.3 Practical Implicationsp. 8
Part I

p. 13

2 Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Testp. 17
2.1 The Specification Testp. 19
2.1.1 Homoskedastic Incrementsp. 20
2.1.2 Heteroskedastic Incrementsp. 24
2.2 The Random Walk Hypothesis for Weekly Returnsp. 26
2.2.1 Results for Market Indexesp. 27
2.2.2 Results for Size-Based Portfoliosp. 30
2.2.3 Results for Individual Securitiesp. 32
2.3 Spurious Autocorrelation Induced by Nontradingp. 34
2.4 The Mean-Reverting Alternative to the Random Walkp. 38
2.5 Conclusionp. 39
Appendix A2 Proof of Theoremsp. 41
3 The Size and Power of the Variance Ratio Test in Finite Samples: A Monte Carlo Investigationp. 47
3.1 Introductionp. 47
3.2 The Variance Ratio Testp. 49
3.2.1 The IID Gaussian Null Hypothesisp. 49
3.2.2 The Heteroskedastic Null Hypothesisp. 52
3.2.3 Variance Ratios and Autocorrelationsp. 54
3.3 Properties of the Test Statistic under the Null Hypothesesp. 55
3.3.1 The Gaussian IID Null Hypothesisp. 55
3.3.2 A Heteroskedastic Null Hypothesisp. 61
3.4 Powerp. 68
3.4.1 The Variance Ratio Test for Large qp. 69
3.4.2 Power against a Stationary AR(1) Alternativep. 70
3.4.3 Two Unit Root Alternatives to the Random Walkp. 73
3.5 Conclusionp. 81
4 An Econometric Analysis of Nonsynchronous Tradingp. 85
4.1 Introductionp. 85
4.2 A Model of Nonsynchronous Tradingp. 88
4.2.1 Implications for Individual Returnsp. 90
4.2.2 Implications for Portfolio Returnsp. 93
4.3 Time Aggregationp. 95
4.4 An Empirical Analysis of Nontradingp. 99
4.4.1 Daily Nontrading Probabilities Implicit in Autocorrelationsp. 101
4.4.2 Nontrading and Index Autocorrelationsp. 104
4.5 Extensions and Generalizationsp. 105
Appendix A4 Proof of Propositionsp. 108
5 When Are Contrarian Profits Due to Stock Market Overreaction?p. 115
5.1 Introductionp. 115
5.2 A Summary of Recent Findingsp. 118
5.3 Analysis of Contrarian Profitabilityp. 121
5.3.1 The Independently and Identically Distributed Benchmarkp. 124
5.3.2 Stock Market Overreaction and Fadsp. 124
5.3.3 Trading on White Noise and Lead-Lag Relationsp. 126
5.3.4 Lead-Lag Effects and Nonsynchronous Tradingp. 127
5.3.5 A Positively Dependent Common Factor and the Bid-Ask Spreadp. 130
5.4 An Empirical Appraisal of Overreactionp. 132
5.5 Long Horizons Versus Short Horizonsp. 140
5.6 Conclusionp. 142
Appendix A5

p. 143

6 Long-Term Memory in Stock Market Pricesp. 147
6.1 Introductionp. 147
6.2 Long-Range Versus Short-Range Dependencep. 149
6.2.1 The Null Hypothesisp. 149
6.2.2 Long-Range Dependent Alternativesp. 152
6.3 The Rescaled Range Statisticp. 155
6.3.1 The Modified R/S Statisticp. 158
6.3.2 The Asymptotic Distribution of Q[subscript n]p. 160
6.3.3 The Relation Between Q[subscript n] and Q[subscript n]p. 161
6.3.4 The Behavior of Q[subscript n] Under Long Memory Alternativesp. 163
6.4 R/S Analysis for Stock Market Returnsp. 165
6.4.1 The Evidence for Weekly and Monthly Returnsp. 166
6.5 Size and Powerp. 171
6.5.1 The Size of the R/S Testp. 171
6.5.2 Power Against Fractionally-Differenced Alternativesp. 174
6.6 Conclusionp. 179
Appendix A6 Proof of Theoremsp. 181
Part II

p. 185

7 Multifactor Models Do Not Explain Deviations from the CAPMp. 189
7.1 Introductionp. 189
7.2 Linear Pricing Models, Mean-Variance Analysis, and the Optimal Orthogonal Portfoliop. 192
7.3 Squared Sharpe Measuresp. 195
7.4 Implications for Risk-Based Versus Nonrisk-Based Alternativesp. 196
7.4.1 Zero Intercept F-Testp. 197
7.4.2 Testing Approachp. 198
7.4.3 Estimation Approachp. 206
7.5 Asymptotic Arbitrage in Finite Economiesp. 208
7.6 Conclusionp. 212
8 Data-Snooping Biases in Tests of Financial Asset Pricing Modelsp. 213
8.1 Quantifying Data-Snooping Biases With Induced Order Statisticsp. 215
8.1.1 Asymptotic Properties of Induced Order Statisticsp. 216
8.1.2 Biases of Tests Based on Individual Securitiesp. 219
8.1.3 Biases of Tests Based on Portfolios of Securitiesp. 224
8.1.4 Interpreting Data-Snooping Bias as Powerp. 228
8.2 Monte Carlo Resultsp. 230
8.2.1 Simulation Results for [theta subscript p]p. 231
8.2.2 Effects of Induced Ordering on F-Testsp. 231
8.2.3 F-Tests With Cross-Sectional Dependencep. 236
8.3 Two Empirical Examplesp. 238
8.3.1 Sorting By Betap. 238
8.3.2 Sorting By Sizep. 240
8.4 How the Data Get Snoopedp. 243
8.5 Conclusionp. 246
9 Maximizing Predictability in the Stock and Bond Marketsp. 249
9.1 Introductionp. 249
9.2 Motivationp. 252
9.2.1 Predicting Factors vs. Predicting Returnsp. 252
9.2.2 Numerical Illustrationp. 254
9.2.3 Empirical Illustrationp. 256
9.3 Maximizing Predictabilityp. 257
9.3.1 Maximally Predictable Portfoliop. 258
9.3.2 Example: One-Factor Modelp. 259
9.4 An Empirical Implementationp. 260
9.4.1 The Conditional Factorsp. 261
9.4.2 Estimating the Conditional-Factor Modelp. 262
9.4.3 Maximizing Predictabilityp. 269
9.4.4 The Maximally Predictable Portfoliosp. 271
9.5 Statistical Inference for the Maximal R[subscript 2]p. 273
9.5.1 Monte Carlo Analysisp. 273
9.6 Three Out-of-Sample Measures of Predictabilityp. 276
9.6.1 Naive vs. Conditional Forecastsp. 276
9.6.2 Merton's Measure of Market Timingp. 279
9.6.3 The Profitability of Predictabilityp. 281
9.7 Conclusionp. 283
Part III

p. 285

10 An Ordered Probit Analysis of Transaction Stock Pricesp. 287
10.1 Introductionp. 287
10.2 The Ordered Probit Modelp. 290
10.2.1 Other Models of Discretenessp. 294
10.2.2 The Likelihood Functionp. 294
10.3 The Datap. 295
10.3.1 Sample Statisticsp. 297
10.4 The Empirical Specificationp. 307
10.5 The Maximum Likelihood Estimatesp. 310
10.5.1 Diagnosticsp. 316
10.5.2 Endogeneity of [Delta]t[subscript k] and IBS[subscript k]p. 318
10.6 Applicationsp. 320
10.6.1 Order-Flow Dependencep. 321
10.6.2 Measuring Price Impact Per Unit Volume of Tradep. 322
10.6.3 Does Discreteness Matter?p. 331
10.7 A Larger Samplep. 338
10.8 Conclusionp. 344
11 Index-Futures Arbitrage and the Behavior of Stock Index Futures Pricesp. 347
11.1 Arbitrage Strategies and the Behavior of Stock Index Futures Pricesp. 348
11.1.1 Forward Contracts on Stock Indexes (No Transaction Costs)p. 349
11.1.2 The Impact of Transaction Costsp. 350
11.2 Empirical Evidencep. 352
11.2.1 Datap. 353
11.2.2 Behavior of Futures and Index Seriesp. 354
11.2.3 The Behavior of the Mispricing Seriesp. 360
11.2.4 Path Dependence of Mispricingp. 364
11.3 Conclusionp. 367
12 Order Imbalances and Stock Price Movements on October 19 and 20, 1987p. 369
12.1 Some Preliminariesp. 370
12.1.1 The Source of the Datap. 371
12.1.2 The Published Standard and Poor's Indexp. 372
12.2 The Constructed Indexesp. 373
12.3 Buying and Selling Pressurep. 378
12.3.1 A Measure of Order Imbalancep. 378
12.3.2 Time-Series Resultsp. 380
12.3.3 Cross-Sectional Resultsp. 381
12.3.4 Return Reversalsp. 385
12.4 Conclusionp. 387
Appendix A12p. 389
A12.1 Index Levelsp. 389
A12.2 Fifteen-Minute Index Returnsp. 393
Referencesp. 395
Indexp. 417

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