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A Non-Random Walk Down Wall Street - Andrew W. Lo

A Non-Random Walk Down Wall Street

Paperback

Published: 15th January 2002
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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.

"What Andrew W. Lo and A. Craig MacKinlay impressively do ... [is look] for hard statistical evidence of predictable patterns in stock prices... Here they marshal the most sophisticated techniques of financial theory to show that the market is not completely random after all."--Jim Holt, Wall Street Journal "With all its equations, this book is going to turn out to be a classic text in the theory of finance. But it is also one for practitioners."--Diane Coyle, The Independent (London) "Where are today's exploitable anomalies? Lo and MacKinlay argue that fast computers, chewing on newly available, tick-by-tick feeds of market-transaction data, can detect regularities in stock prices that would have been invisible as recently as five years ago. One example: 'clientele bias,' in which certain stocks are popular with investors who have certain trading styles. A case in point that doesn't take a supercomputer to detect, is day traders' current enthusiasm for Internet stocks. Lo says that day traders tend to overreact to news--whether that news is positive or negative--so it should be possible to profit by taking the opposite side of their trades."--Peter Coy, Business Week

List of Figuresp. xiii
List of Tablesp. xv
Prefacep. xxi
Introductionp. 3
The Random Walk and Efficient Marketsp. 4
The Current State of Efficient Marketsp. 6
Practical Implicationsp. 8
p. 13
Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Testp. 17
The Specification Testp. 19
Homoskedastic Incrementsp. 20
Heteroskedastic Incrementsp. 24
The Random Walk Hypothesis for Weekly Returnsp. 26
Results for Market Indexesp. 27
Results for Size-Based Portfoliosp. 30
Results for Individual Securitiesp. 32
Spurious Autocorrelation Induced by Nontradingp. 34
The Mean-Reverting Alternative to the Random Walkp. 38
Conclusionp. 39
Proof of Theoremsp. 41
The Size and Power of the Variance Ratio Test in Finite Samples: A Monte Carlo Investigationp. 47
Introductionp. 47
The Variance Ratio Testp. 49
The IID Gaussian Null Hypothesisp. 49
The Heteroskedastic Null Hypothesisp. 52
Variance Ratios and Autocorrelationsp. 54
Properties of the Test Statistic under the Null Hypothesesp. 55
The Gaussian IID Null Hypothesisp. 55
A Heteroskedastic Null Hypothesisp. 61
Powerp. 68
The Variance Ratio Test for Large qp. 69
Power against a Stationary AR(1) Alternativep. 70
Two Unit Root Alternatives to the Random Walkp. 73
Conclusionp. 81
An Econometric Analysis of Nonsynchronous Tradingp. 85
Introductionp. 85
A Model of Nonsynchronous Tradingp. 88
Implications for Individual Returnsp. 90
Implications for Portfolio Returnsp. 93
Time Aggregationp. 95
An Empirical Analysis of Nontradingp. 99
Daily Nontrading Probabilities Implicit in Autocorrelationsp. 101
Nontrading and Index Autocorrelationsp. 104
Extensions and Generalizationsp. 105
Proof of Propositionsp. 108
When Are Contrarian Profits Due to Stock Market Overreaction?p. 115
Introductionp. 115
A Summary of Recent Findingsp. 118
Analysis of Contrarian Profitabilityp. 121
The Independently and Identically Distributed Benchmarkp. 124
Stock Market Overreaction and Fadsp. 124
Trading on White Noise and Lead-Lag Relationsp. 126
Lead-Lag Effects and Nonsynchronous Tradingp. 127
A Positively Dependent Common Factor and the Bid-Ask Spreadp. 130
An Empirical Appraisal of Overreactionp. 132
Long Horizons Versus Short Horizonsp. 140
Conclusionp. 142
p. 143
Long-Term Memory in Stock Market Pricesp. 147
Introductionp. 147
Long-Range Versus Short-Range Dependencep. 149
The Null Hypothesisp. 149
Long-Range Dependent Alternativesp. 152
The Rescaled Range Statisticp. 155
The Modified R/S Statisticp. 158
The Asymptotic Distribution of Q[subscript n]p. 160
The Relation Between Q[subscript n] and Q[subscript n]p. 161
The Behavior of Q[subscript n] Under Long Memory Alternativesp. 163
R/S Analysis for Stock Market Returnsp. 165
The Evidence for Weekly and Monthly Returnsp. 166
Size and Powerp. 171
The Size of the R/S Testp. 171
Power Against Fractionally-Differenced Alternativesp. 174
Conclusionp. 179
Proof of Theoremsp. 181
p. 185
Multifactor Models Do Not Explain Deviations from the CAPMp. 189
Introductionp. 189
Linear Pricing Models, Mean-Variance Analysis, and the Optimal Orthogonal Portfoliop. 192
Squared Sharpe Measuresp. 195
Implications for Risk-Based Versus Nonrisk-Based Alternativesp. 196
Zero Intercept F-Testp. 197
Testing Approachp. 198
Estimation Approachp. 206
Asymptotic Arbitrage in Finite Economiesp. 208
Conclusionp. 212
Data-Snooping Biases in Tests of Financial Asset Pricing Modelsp. 213
Quantifying Data-Snooping Biases With Induced Order Statisticsp. 215
Asymptotic Properties of Induced Order Statisticsp. 216
Biases of Tests Based on Individual Securitiesp. 219
Biases of Tests Based on Portfolios of Securitiesp. 224
Interpreting Data-Snooping Bias as Powerp. 228
Monte Carlo Resultsp. 230
Simulation Results for [theta subscript p]p. 231
Effects of Induced Ordering on F-Testsp. 231
F-Tests With Cross-Sectional Dependencep. 236
Two Empirical Examplesp. 238
Sorting By Betap. 238
Sorting By Sizep. 240
How the Data Get Snoopedp. 243
Conclusionp. 246
Maximizing Predictability in the Stock and Bond Marketsp. 249
Introductionp. 249
Motivationp. 252
Predicting Factors vs. Predicting Returnsp. 252
Numerical Illustrationp. 254
Empirical Illustrationp. 256
Maximizing Predictabilityp. 257
Maximally Predictable Portfoliop. 258
Example: One-Factor Modelp. 259
An Empirical Implementationp. 260
The Conditional Factorsp. 261
Estimating the Conditional-Factor Modelp. 262
Maximizing Predictabilityp. 269
The Maximally Predictable Portfoliosp. 271
Statistical Inference for the Maximal R[subscript 2]p. 273
Monte Carlo Analysisp. 273
Three Out-of-Sample Measures of Predictabilityp. 276
Naive vs. Conditional Forecastsp. 276
Merton's Measure of Market Timingp. 279
The Profitability of Predictabilityp. 281
Conclusionp. 283
p. 285
An Ordered Probit Analysis of Transaction Stock Pricesp. 287
Introductionp. 287
The Ordered Probit Modelp. 290
Other Models of Discretenessp. 294
The Likelihood Functionp. 294
The Datap. 295
Sample Statisticsp. 297
The Empirical Specificationp. 307
The Maximum Likelihood Estimatesp. 310
Diagnosticsp. 316
Endogeneity of [Delta]t[subscript k] and IBS[subscript k]p. 318
Applicationsp. 320
Order-Flow Dependencep. 321
Measuring Price Impact Per Unit Volume of Tradep. 322
Does Discreteness Matter?p. 331
A Larger Samplep. 338
Conclusionp. 344
Index-Futures Arbitrage and the Behavior of Stock Index Futures Pricesp. 347
Arbitrage Strategies and the Behavior of Stock Index Futures Pricesp. 348
Forward Contracts on Stock Indexes (No Transaction Costs)p. 349
The Impact of Transaction Costsp. 350
Empirical Evidencep. 352
Datap. 353
Behavior of Futures and Index Seriesp. 354
The Behavior of the Mispricing Seriesp. 360
Path Dependence of Mispricingp. 364
Conclusionp. 367
Order Imbalances and Stock Price Movements on October 19 and 20, 1987p. 369
Some Preliminariesp. 370
The Source of the Datap. 371
The Published Standard and Poor's Indexp. 372
The Constructed Indexesp. 373
Buying and Selling Pressurep. 378
A Measure of Order Imbalancep. 378
Time-Series Resultsp. 380
Cross-Sectional Resultsp. 381
Return Reversalsp. 385
Conclusionp. 387
Appendix A12p. 389
Index Levelsp. 389
Fifteen-Minute Index Returnsp. 393
Referencesp. 395
Indexp. 417
Table of Contents provided by Syndetics. All Rights Reserved.

ISBN: 9780691092560
ISBN-10: 0691092567
Audience: Tertiary; University or College
Format: Paperback
Language: English
Number Of Pages: 448
Published: 15th January 2002
Country of Publication: US
Dimensions (cm): 23.4 x 15.7  x 2.7
Weight (kg): 0.73