A Non-random Walk Down Wall Street (ebook)

Argumento de A Non-random Walk Down Wall Street (ebook)

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.

"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

"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

"This provocative collection of essays provides careful empirical analyses of the major anomalies that have appeared in financial markets in the thirty-five years since Paul Cootner's influential Random Character of Stock Market Prices. It provides convincing evidence against the random walk as applied to stock markets, and at the same time warns us of the dangers of finding spurious anomalies. It is a worthy successor to Cootner's classic."--Michael Brennan, University of California, Los Angeles

"This book is highly recommended to academic and private-sector economists who are interested in understanding better the behavior of financial market returns."--Lars Peter Hansen, University of Chicago

"The common feature of this work . . . is that it is guided by simple economic intuitions while simultaneously being econometrically rigorous and careful."--Bruce N. Lehmann, UC-San Diego0List of Figures
List of Tables
Preface

1 Introduction
1.1 The Random Walk and Efficient Markets
1.2 The Current State of Efficient Markets
1.3 Practical Implications

Part I
2 Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test
2.1 The Specification Test
2.1.1 Homoskedastic Increments
2.1.2 Heteroskedastic Increments
2.2 The Random Walk Hypothesis for Weekly Returns
2.2.1 Results for Market Indexes
2.2.2 Results for Size-Based Portfolios
2.2.3 Results for Individual Securities
2.3 Spurious Autocorrelation Induced by Nontrading
2.4 The Mean-Reverting Alternative to the Random Walk
2.5 Conclusion
Appendix A2: Proof of Theorems

3 The Size and Power of the Variance Ratio Test in Finite Samples: A Monte Carlo Investigation
3.1 Introduction
3.2 The Variance Ratio Test
3.2.1 The IID Gaussian Null Hypothesis
3.2.2 The Heteroskedastic Null Hypothesis
3.2.3 Variance Ratios and Autocorrelations
3.3 Properties of the Test Statistic under the Null Hypotheses
3.3.1 The Gaussian IID Null Hypothesis
3.3.2 A Heteroskedastic Null Hypothesis
3.4 Power
3.4.1 The Variance Ratio Test for Large q
3.4.2 Power against a Stationary AR(1) Alternative
3.4.3 Two Unit Root Alternatives to the Random Walk
3.5 Conclusion

4 An Econometric Analysis of Nonsynchronous Trading
4.1 Introduction
4.2 A Model of Nonsynchronous Trading
4.2.1 Implications for Individual Returns
4.2.2 Implications for Portfolio Returns
4.3 Time Aggregation
4.4 An Empirical Analysis of Nontrading
4.4.1 Daily Nontrading Probabilities Implicit in Autocorrelations
4.4.2 Nontrading and Index Autocorrelations
4.5 Extensions and Generalizations
Appendix A4: Proof of Propositions

5 When Are Contrarian Profits Due to Stock Market Overreaction?
5.1 Introduction
5.2 A Summary of Recent Findings
5.3 Analysis of Contrarian Profitability
5.3.1 The Independently and Identically Distributed Benchmark
5.3.2 Stock Market Overreaction and Fads
5.3.3 Trading on White Noise and Lead-Lag Relations
5.3.4 Lead-Lag Effects and Nonsynchronous Trading
5.3.5 A Positively Dependent Common Factor and the Bid-Ask Spread
5.4 An Empirical Appraisal of Overreaction
5.5 Long Horizons Versus Short Horizons
5.6 Conclusion
Appendix A5

6 Long-Term Memory in Stock Market Prices
6.1 Introduction
6.2 Long-Range Versus Short-Range Dependence
6.2.1 The Null Hypothesis
6.2.2 Long-Range Dependent Alternatives
6.3 The Rescaled Range Statistic
6.3.1 The Modified R/S Statistic
6.3.2 The Asymptotic Distribution of Q_n
6.3.3 The Relation Between Q_n and [tilde]Q_n
6.3.4 The Behavior of Q_n Under Long Memory Alternatives
6.4 R/S Analysis for Stock Market Returns
6.4.1 The Evidence for Weekly and Monthly Returns
6.5 Size and Power
6.5.1 The Size of the R/S Test
6.5.2 Power Against Fractionally-Differenced Alternatives
6.6 Conclusion
Appendix A6: Proof of Theorems

Part II
7 Multifactor Models Do Not Explain Deviations from the CAPM
7.1 Introduction
7.2 Linear Pricing Models, Mean-Variance Analysis, and the Optimal Orthogonal Portfolio
7.3 Squared Sharpe Measures
7.4 Implications for Risk-Based Versus Nonrisk-Based Alternatives
7.4.1 Zero Intercept F-Test
7.4.2 Testing Approach
7.4.3 Estimation Approach
7.5 Asymptotic Arbitrage in Finite Economies
7.6 Conclusion

8 Data-Snooping Biases in Tests of Financial Asset Pricing Models
8.1 Quantifying Data-Snooping Biases With Induced Order Statistics
8.1.1 Asymptotic Properties of Induced Order Statistics
8.1.2 Biases of Tests Based on Individual Securities
8.1.3 Biases of Tests Based on Portfolios of Securities
8.1.4 Interpreting Data-Snooping Bias as Power
8.2 Monte Carlo Results
8.2.1 Simulation Results for [theta]_p
8.2.2 Effects of Induced Ordering on F-Tests
8.2.3 F-Tests With Cross-Sectional Dependence
8.3 Two Empirical Examples
8.3.1 Sorting By Beta
8.3.2 Sorting By Size
8.4 How the Data Get Snooped
8.5 Conclusion

9 Maximizing Predictability in the Stock and Bond Markets
9.1 Introduction
9.2 Motivation
9.2.1 Predicting Factors vs. Predicting Returns
9.2.2 Numerical Illustration
9.2.3 Empirical Illustration
9.3 Maximizing Predictability
9.3.1 Maximally Predictable Portfolio
9.3.2 Example: One-Factor Model
9.4 An Empirical Implementation
9.4.1 The Conditional Factors
9.4.2 Estimating the Conditional-Factor Model
9.4.3 Maximizing Predictability
9.4.4 The Maximally Predictable Portfolios
9.5 Statistical Inference for the Maximal R²
9.5.1 Monte Carlo Analysis
9.6 Three Out-of-Sample Measures of Predictability
9.6.1 Naive vs. Conditional Forecasts
9.6.2 Merton's Measure of Market Timing
9.6.3 The Profitability of Predictability
9.7 Conclusion

Part III
10 An Ordered Probit Analysis of Transaction Stock Prices
10.1 Introduction
10.2 The Ordered Probit Model
10.2.1 Other Models of Discreteness
10.2.2 The Likelihood Function
10.3 The Data
10.3.1 Sample Statistics
10.4 The Empirical Specification
10.5 The Maximum Likelihood Estimates
10.5.1 Diagnostics
10.5.2 Endogeneity of [Delta]t_k and IBS_k
10.6 Applications
10.6.1 Order-Flow Dependence
10.6.2 Measuring Price Impact Per Unit Volume of Trade
10.6.3 Does Discreteness Matter?
10.7 A Larger Sample
10.8 Conclusion

11 Index-Futures Arbitrage and the Behavior of Stock Index Futures Prices
11.1 Arbitrage Strategies and the Behavior of Stock Index Futures Prices
11.1.1 Forward Contracts on Stock Indexes (No Transaction Costs)
11.1.2 The Impact of Transaction Costs
11.2 Empirical Evidence
11.2.1 Data
11.2.2 Behavior of Futures and Index Series
11.2.3 The Behavior of the Mispricing Series
11.2.4 Path Dependence of Mispricing
11.3 Conclusion

12 Order Imbalances and Stock Price Movements on October 19 and 20, 1987
12.1 Some Preliminaries
12.1.1 The Source of the Data
12.1.2 The Published Standard and Poor's Index
12.2 The Constructed Indexes
12.3 Buying and Selling Pressure
12.3.1 A Measure of Order Imbalance
12.3.2 Time-Series Results
12.3.3 Cross-Sectional Results
12.3.4 Return Reversals
12.4 Conclusion
Appendix A12
A12.1 Index Levels
A12.2 Fifteen-Minute Index Returns

References
Index