The theory that stock prices move randomly was earlier proposed by Maurice Kendall in his 1953 paper, The Analysis of Economic Time Series, Part 1: Prices. The term was popularized by the 1973 book A Random Walk Down Wall Street by Burton Malkiel, a professor of economics at Princeton University, and was used earlier in Eugene Fama's 1965 article 'Random Walks In Stock Market Prices', which was a less technical version of his Ph.D. The same ideas were later developed by MIT Sloan School of Management professor Paul Cootner in his 1964 book The Random Character of Stock Market Prices. dissertation titled 'The Theory of Speculation' (1900) included some remarkable insights and commentary. The concept can be traced to French broker Jules Regnault who published a book in 1863, and then to French mathematician Louis Bachelier whose Ph.D. Long-Term Memory in Stock Market Prices6.1 Introduction 6.2 Long-Rangeversus 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 Qn 6.3.3 The Relation Between Qn and Qn 6.3.4 The Behavior of Qn, 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.The random walk hypothesis is a financial theory stating that stock market prices evolve according to a random walk (so price changes are random) and thus cannot be predicted. 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-Askspread 5.4 An Empirical Appraisal of Overreaction 5.5 Long Horizons Versus Short Horizons 5.6 Conclusion Appendix A5. 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 Nontradin 4.4.1 Daily Nontrading Probabilities Implicit in Autocorrelations 4.4.2 Nontrading and Index Autocorrelations 4.5 Extensions and Generalizations. 3.4.1 The Variance Ratio Test for Large q3.4.2 Power against a Stationary AR(1) Alternative 3.4.3 Two Unit Root Alternatives to the Random Walk 3.5 Conclusion 4.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.

2.3 Spurious Autocorrelation Induced by Nontrading2.4 The Mean-Reverting Alternative to the Random Walk 2.5 Conclusion Appendix A2: Proof of Theorems 3.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 SizeBased Portfolios 2.2.3 Results for Individual Securities.

Cover Title Page Copyright Page Table of Contents 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.

A not so random walk on wall street