Contents of Lo & MacKinlay: A Non-Random Walk down Wall Street

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

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

• 3.1 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

• 4.1 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

• 5.1 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

• 6.1 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

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

• 8.1 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

• 9.1 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

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

• 11.1 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

• 12.1 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