A Non-Random Walk Down Wall Street

Zusatztext "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 Informationen zum Autor Andrew W. Lo & A. Craig MacKinlay Klappentext 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. Zusammenfassung 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...

Auteur

Andrew W. Lo & A. Craig MacKinlay

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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.

Contenu
List 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 Al…