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

A Non-Random Walk Down Wall Street

byAndrew W. Lo

Paperback | January 15, 2002

Pricing and Purchase Info

$69.97 online 
$87.95 list price save 20%
Earn 350 plum® points

Prices and offers may vary in store


In stock online

Ships free on orders over $25

Not available in stores


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.

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. With John Y. Campbell, they are the authors of The Econometrics of Financial Markets (...
Title:A Non-Random Walk Down Wall StreetFormat:PaperbackDimensions:448 pagesPublished:January 15, 2002Publisher:Princeton University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0691092567

ISBN - 13:9780691092560

Look for similar items by category:


Table of Contents

List of Figures

List of Tables


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 Largeq

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 ModifiedR/SStatistic

6.3.2 The Asymptotic Distribution ofQn

6.3.3 The Relation BetweenQnand [tilde]Qn

6.3.4 The Behavior ofQnUnder Long Memory Alternatives

6.4R/SAnalysis 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 theR/STest

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 InterceptF-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 onF-Tests

8.2.3F-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 MaximalR2

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]tkandIBSk

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



Editorial Reviews

"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 Diego