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A Non-Random Walk Down Wall Street, Andrew Lo & A. Craig MacKinlay
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eBay-Artikelnr.:134499028406
Artikelmerkmale
- Artikelzustand
- ISBN
- 9780691092560
Über dieses Produkt
Product Identifiers
Publisher
Princeton University Press
ISBN-10
0691092567
ISBN-13
9780691092560
eBay Product ID (ePID)
2232680
Product Key Features
Number of Pages
448 Pages
Publication Name
Non-Random Walk Down Wall Street
Language
English
Subject
Investments & Securities / Analysis & Trading Strategies, Investments & Securities / General
Publication Year
2002
Type
Textbook
Subject Area
Business & Economics
Format
Trade Paperback
Dimensions
Item Height
1.3 in
Item Weight
25 Oz
Item Length
9.1 in
Item Width
6.1 in
Additional Product Features
Intended Audience
College Audience
Reviews
"With all its equations, this book is going to turn out to be a classic text in the theory of finance. But it is also one for practitioners." --Diane Coyle, The Independent (London), What Andrew W. Lo and A. Craig MacKinlay impressively do . . . [is look] for hard statistical evidence of predictable patterns in stock prices. . . . Here they marshal the most sophisticated techniques of financial theory to show that the market is not completely random after all., 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, With all its equations, this book is going to turn out to be a classic text in the theory of finance. But it is also one for practitioners. -- Diane Coyle, The Independent, "What Andrew W. Lo and A. Craig MacKinlay impressively do . . . [is look] for hard statistical evidence of predictable patterns in stock prices. . . . Here they marshal the most sophisticated techniques of financial theory to show that the market is not completely random after all."-- Jim Holt, Wall Street Journal, What Andrew W. Lo and A. Craig MacKinlay impressively do . . . [is look] for hard statistical evidence of predictable patterns in stock prices. . . . Here they marshal the most sophisticated techniques of financial theory to show that the market is not completely random after all. -- Jim Holt, Wall Street Journal, "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, "What Andrew W. Lo and A. Craig MacKinlay impressively do . . . [is look] for hard statistical evidence of predictable patterns in stock prices. . . . Here they marshal the most sophisticated techniques of financial theory to show that the market is not completely random after all." --Jim Holt, Wall Street Journal, With all its equations, this book is going to turn out to be a classic text in the theory of finance. But it is also one for practitioners., "With all its equations, this book is going to turn out to be a classic text in the theory of finance. But it is also one for practitioners."-- Diane Coyle, The Independent (London), 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., "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
Dewey Edition
21
TitleLeading
A
Illustrated
Yes
Dewey Decimal
332.63222
Table Of Content
List of Figures List of Tables Preface1 Introduction 1.1 The Random Walk and Efficient Markets 1.2 The Current State of Efficient Markets 1.3 Practical ImplicationsPart 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 Theorems3 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 Conclusion4 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 Propositions5 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 A56 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 ofQ n 6.3.3 The Relation BetweenQ n and [tilde]Q n 6.3.4 The Behavior ofQ n Under 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 TheoremsPart 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 Conclusion8 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 f
Synopsis
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. This work puts the Random Walk Hypothesis to the test., 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., 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.
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Verkäuferbewertungen (4'644)
- g***d (80)- Bewertung vom Käufer.Letzte 6 MonateBestätigter KaufReceived the vintage book VERY FAST, in only 2 days. The package was carefully wrapped and the 80 year old book is in excellent condition! The book was illustrated by a relative my immediate family never met so this means a lot to me. The seller was gracious to give me a discount when there was some confusion over a Venmo special coupon savings that was not part of the seller’s original listing. Thanks Seth for staying in contact with me throughout the purchase process and all your help!
- x***l (218)- Bewertung vom Käufer.Letzte 6 MonateBestätigter KaufVERY PLEASED!!! Item arrived QUICKLY, in a plastic bag to protect it in case of delivery on a rainy day, and then inside a nice box. Looked BETTER IN REAL LIFE THAN in the seller's high-quality photos. Got a great deal on a book I've wanted for a while. Seller was STRAIGHTFORWARD, RESPONSIVE, PERSONABLE, and EASY TO WORK WITH. I felt like I was in a brick-and-mortar shop. I would happily buy from them again!Elementary Number Theory: A Problem Oriented Approach by Joe Roberts (Nr. 135172763189)
- .***j (6)- Bewertung vom Käufer.Letzte 6 MonateBestätigter KaufItems arrived exactly as described - in unused and sealed condition. These brushes are pretty expensive generally so I was really happy to come across this posting from the seller - this was a great deal. Packaged nicely and no extra charge for shipping was great. Really happy with the purchase. Thank you!