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A Non-Random Walk Down Wall Street

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TLDR
Lo and MacKinlay as discussed by the authors found that markets are not completely random after all, and that predictable components do exist in recent stock and bond returns, and pointed out 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.
Abstract
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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Empirical properties of asset returns: stylized facts and statistical issues

TL;DR: In this paper, the authors present a set of stylized empirical facts emerging from the statistical analysis of price variations in various types of financial markets, including distributional properties, tail properties and extreme fluctuations, pathwise regularity, linear and nonlinear dependence of returns in time and across stocks.
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Quantitative Risk Management: Concepts, Techniques, and Tools

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The Efficient Market Hypothesis and Its Critics

TL;DR: The idea of a "random walk" was first proposed by Fama as discussed by the authors, who argued that if the flow of information is unimpeded and information is immediately ree ected in stock prices, then tomorrow's price change will re- ect only tomorrow's news and will be independent of the price changes today.
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The Adaptive Markets Hypothesis

TL;DR: In this paper, the authors argue that much of what behaviorists cite as counter-examples to economic rationality is in fact consistent with an evolutionary model of individual adaptation to a changing environment via simple heuristics.
Posted Content

The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective

TL;DR: The Adaptive Markets Hypothesis as discussed by the authors proposes a new framework that reconciles market efficiency with behavioral alternatives by applying the principles of evolution - competition, adaptation, and natural selection - to financial interactions.
References
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Book

Asymptotic theory for econometricians

Halbert White
TL;DR: The Linear Model and Instrumental Variables Estimators as mentioned in this paper have been used to estimate Asymptotic Covariance Matrices, and Central Limit Theory has been applied to this problem.
Journal ArticleDOI

Inferring the Components of the Bid-Ask Spread: Theory and Empirical Tests

Hans R. Stoll
- 01 Mar 1989 - 
TL;DR: In this article, the relationship between the quoted bid-ask spread and two serial covariances, the serial covariance of transaction returns and the serial correlation of quoted returns, is modeled as a function of the probability of a price reversal, 7r, and the magnitude of price change, a, where a is stated as a fraction of the quoted spread.
Journal ArticleDOI

A Model of Competitive Stock Trading Volume

TL;DR: In this article, a model of competitive stock trading is developed in which investors are heterogeneous in their information and private investment opportunities and rationally trade for both informational and noninformational motives.
Journal ArticleDOI

An Investigation of Transactions Data for NYSE Stocks

TL;DR: In this paper, the behavior of returns and characteristics of trades at the micro level is examined using transactions data, and a minute-by-minute market return series is formed and tested for normality and autocorrelation.