A Non-Random Walk Down Wall Street

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Format: Paperback
Pub. Date: 2001-12-26
Publisher(s): Princeton Univ Pr
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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 Biography

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

Table of Contents

List of Figures
xiii
List of Tables
xv
Preface xxi
Introduction
3(10)
The Random Walk and Efficient Markets
4(2)
The Current State of Efficient Markets
6(2)
Practical Implications
8(5)
Part I 13(172)
Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test
17(30)
The Specification Test
19(7)
Homoskedastic Increments
20(4)
Heteroskedastic Increments
24(2)
The Random Walk Hypothesis for Weekly Returns
26(8)
Results for Market Indexes
27(3)
Results for Size-Based Portfolios
30(2)
Results for Individual Securities
32(2)
Spurious Autocorrelation Induced by Nontrading
34(4)
The Mean-Reverting Alternative to the Random Walk
38(1)
Conclusion
39(8)
Appendix A2: Proof of Theorems
41(6)
The Size and Power of the Variance Ratio Test in Finite Samples: A Monte Carlo Investigation
47(38)
Introduction
47(2)
The Variance Ratio Test
49(6)
The IID Gaussian Null Hypothesis
49(3)
The Heteroskedastic Null Hypothesis
52(2)
Variance Ratios and Autocorrelations
54(1)
Properties of the Test Statistic under the Null Hypothesis
55(13)
The Gaussian IID Null Hypothesis
55(6)
A Heteroskedastic Null Hypothesis
61(7)
Power
68(13)
The Variance Ratio Test for Large q
69(1)
Power against a Stationary AR(1) Alternative
70(3)
Two Unit Root Alternatives to the Random Walk
73(8)
Conclusion
81(4)
An Econometric Analysis of Nonsynchronous Trading
85(30)
Introduction
85(3)
A Model of Nonsynchronous Trading
88(7)
Implications for Individual Returns
90(3)
Implications for Portfolio Returns
93(2)
Time Aggregation
95(4)
An Empirical Analysis of Nontrading
99(6)
Daily Nontrading Probabilities Implicit in Autocorrelations
101(3)
Nontrading and Index Autocorrelations
104(1)
Extensions and Generalizations
105(10)
Appendix A4: Proof of Propositions
108(7)
When Are Contrarian Profits Due to Stock Market Overreaction?
115(32)
Introduction
115(3)
A Summary of Recent Findings
118(3)
Analysis of Contrarian Profitability
121(11)
The Independently and Identically Distributed Benchmark
124(1)
Stock Market Overreaction and Fads
124(2)
Trading on White Noise and Lead-Lag Relations
126(1)
Lead-Lag Effects and Nonsynchronous Trading
127(3)
A Positively Dependent Common Factor and the Bid-Ask Spread
130(2)
An Empirical Appraisal of Overreaction
132(8)
Long Horizons Versus Short Horizons
140(2)
Conclusion
142(5)
Appendix A5
143(4)
Long-Term Memory in Stock Market Prices
147(38)
Introduction
147(2)
Long-Range Versus Short-Range Dependence
149(6)
The Null Hypothesis
149(3)
Long-Range Dependent Alternatives
152(3)
The Rescaled Range Statistic
155(10)
The Modified R/S Statistic
158(2)
The Asymptotic Distribution of Qn
160(1)
The Relation Between Qn and Qn
161(2)
The Behavior of Qn Under Long Memory Alternatives
163(2)
R/S Analysis for Stock Market Returns
165(6)
The Evidence for Weekly and Monthly Returns
166(5)
Size and Power
171(8)
The Size of the R/S Test
171(3)
Power Against Fractionally-Differenced Alternatives
174(5)
Conclusion
179(6)
Appendix A6: Proof of Theorems
181(4)
Part II 185(100)
Multifactor Models Do Not Explain Deviations from the CAPM
189(24)
Introduction
189(3)
Linear Pricing Models, Mean-Variance Analysis, and the Optimal Orthogonal Portfolio
192(3)
Squared Sharpe Measures
195(1)
Implications for Risk-Based Versus Nonrisk-Based Alternatives
196(12)
Zero Intercept F-Test
197(1)
Testing Approach
198(8)
Estimation Approach
206(2)
Asymptotic Arbitrage in Finite Economies
208(4)
Conclusion
212(1)
Data-Snooping Biases in Tests of Financial Asset Pricing Models
213(36)
Quantifying Data-Snooping Biases With Induced Order Statistics
215(15)
Asymptotic Properties of Induced Order Statistics
216(3)
Biases of Tests Based on Individual Securities
219(5)
Biases of Tests Based on Portfolios of Securities
224(4)
Interpreting Data-Snooping Bias as Power
228(2)
Monte Carlo Results
230(8)
Simulation Results for &thetas;p
231(1)
Effects of Induced Ordering on F-Tests
231(5)
F-Tests With Cross-Sectional Dependence
236(2)
Two Empirical Examples
238(5)
Sorting By Beta
238(2)
Sorting By Size
240(3)
How the Data Get Snooped
243(3)
Conclusion
246(3)
Maximizing Predictability in the Stock and Bond Markets
249(36)
Introduction
249(3)
Motivation
252(5)
Predicting Factors vs. Predicting Returns
252(2)
Numerical Illustration
254(2)
Empirical Illustration
256(1)
Maximizing Predictability
257(3)
Maximally Predictable Portfolio
258(1)
Example: One-Factor Model
259(1)
An Empirical Implementation
260(13)
The Conditional Factors
261(1)
Estimating the Conditional-Factor Model
262(7)
Maximizing Predictability
269(2)
The Maximally Predictable Portfolios
271(2)
Statistical Inference for the Maximal R2
273(3)
Monte Carlo Analysis
273(3)
Three Out-of-Sample Measures of Predictability
276(7)
Naive vs. Conditional Forecasts
276(3)
Merton's Measure of Market Timing
279(2)
The Profitability of Predictability
281(2)
Conclusion
283(2)
Part III 285(110)
An Ordered Probit Analysis of Transaction Stock Prices
287(60)
Introduction
287(3)
The Ordered Probit Model
290(5)
Other Models of Discreteness
294(1)
The Likelihood Function
294(1)
The Data
295(12)
Sample Statistics
297(10)
The Empirical Specification
307(3)
The Maximum Likelihood Estimates
310(10)
Diagnostics
316(2)
Endogeneity of Δtk and IBSk
318(2)
Applications
320(18)
Order-Flow Dependence
321(1)
Measuring Price Impact Per Unit Volume of Trade
322(9)
Does Discreteness Matter?
331(7)
A Larger Sample
338(6)
Conclusion
344(3)
Index-Futures Arbitrage and the Behavior of Stock Index Futures Prices
347(22)
Arbitrage Strategies and the Behavior of Stock Index Futures Prices
348(4)
Forward Contracts on Stock Indexes (No Transaction Costs)
349(1)
The Impact of Transaction Costs
350(2)
Empirical Evidence
352(15)
Data
353(1)
Behavior of Futures and Index Series
354(6)
The Behavior of the Mispricing Series
360(4)
Path Dependence of Mispricing
364(3)
Conclusion
367(2)
Order Imbalances and Stock Price Movements on October 19 and 20, 1987
369(26)
Some Preliminaries
370(3)
The Source of the Data
371(1)
The Published Standard and Poor's Index
372(1)
The Constructed Indexes
373(5)
Buying and Selling Pressure
378(9)
A Measure of Order Imbalance
378(2)
Time-Series Results
380(1)
Cross-Sectional Results
381(4)
Return Reversals
385(2)
Conclusion
387(8)
Appendix A12
389(1)
Index Levels
389(4)
Fifteen-Minute Index Returns
393(2)
References 395(22)
Index 417

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