https://rady.ucsd.edu/faculty/directory/valkanov/pub/classes/mfe/docs/fama_french_jfe_1993.pdf

Common risk factors in the returns on stocks and bonds

This paper documents that strategies which buy stocks that have performed well in the past and sell stocks that have performed poorly in the past generate significant positive returns over 3- to 12-month holding periods. We find that the profitability of these strategies are not due to their systematic risk or to delayed stock price reactions to common factors. However, part of the abnormal returns generated in the first year after portfolio formation dissipates in the following two years. A similar pattern of returns around the earnings announcements of past winners and losers is also documented

/pdf/returns-to-buying-winners-and-selling-losers-implications-47r2et8t86.pdf

Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency

This paper introduces an ARCH model (exponential ARCH) that (1) allows correlation between returns and volatility innovations (an important feature of stock market volatility changes), (2) eliminates the need for inequality constraints on parameters, and (3) allows for a straightforward interpretation of the "persistence" of shocks to volatility. In the above respects, it is an improvement over the widely-used GARCH model. The model is applied to study volatility changes and the risk premium on the CRSP Value-Weighted Market Index from 1962 to 1987. Copyright 1991 by The Econometric Society.

Conditional heteroskedasticity in asset returns: a new approach

Data Mining Practical Machine Learning Tools and Techniques

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

This book is an ambitious effort by three well-known and
 well-respected scholars to fill an acknowledged void in the
 literature—a text covering the burgeoning field of empirical finance.
 As the authors note in the preface, there are several excellent books
 covering financial theory at a level suitable for a Ph.D. class or as
 a reference for academics and practitioners, but there is little or
 nothing similar that covers econometric methods and applications.
 Perhaps the closest existing text is the recent addition to the Wiley
 Series in Financial and Quantitative Analysis. written by Cuthbertson
 (1996). The major difference between the books is that Cuthbertson
 focuses exclusively on asset pricing in the stock, bond, and foreign
 exchange markets, whereas Campbell, Lo, and MacKinlay (henceforth CLM)
 consider empirical applications throughout the field of finance,
 including corporate finance, derivatives markets, and market
 microstructure. The level of anticipation preceding publication
 can be partly measured by the fact that at least three reviews
 (including this one) have appeared since the book arrived. Moreover,
 in their reviews, both Harvey (1998) and Tiso (1998) comment on the
 need for such a text, a sentiment that has been echoed by numerous
 finance academics.

/pdf/the-econometrics-of-financial-markets-gsz6uyl6wd.pdf

The econometrics of financial markets

In this article we test the random walk hypothesis for weekly stock market returns by comparing variance estimators derived from data sampled at different frequencies. The random walk model is strongly rejected for the entire sampleperiod (1962-1985) and for all subperiods for a variety of aggregate returns indexes and size-sorted porfolios. Although the rejections are due largely to the behavior of small stocks, they cannot be attributed completely to the effects of infrequent trading or timevarying volatilities. Moreover, the rejection of the random walk for weekly returns does not support a mean-reverting model of assetprices.

/pdf/stock-market-prices-do-not-follow-random-walks-evidence-from-bopk1assge.pdf

Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test

In this paper, we test the random walk hypothesis for weekly stock market returns by comparing variance estimators derived from data sampled at different frequencies. The random walk model is strongly rejected for the entire sample period (1962-1985) and for all sub-periods for a variety of aggregate returns indexes and size-sorted portfolios. Although the rejections are largely due to the behavior of small stocks, they cannot be ascribed to either the effects of infrequent trading or time-varying volatilities. Moreover, the rejection of the random walk cannot be interpreted as supporting a mean-reverting stationary model of asset prices, but is more consistent with a specific nonstationary alternative hypothesis.

Stock Market Prices Do Not Follow Random Walks: Evidence From a Simple Specification Test

A test for long-run memory that is robust to short-range dependence is developed. It is a simple extension of Mandelbrot's "range over standard deviation" or R/S statistic, for which the relevant asymptotic sampling theory is derived via functional central limit theory. This test is applied to daily, weekly, monthly, and annual stock returns indexes over several different time periods. Contrary to previous findings, there is no evidence of long-range dependence in any of the indexes over any sample period or sub-period once short-term autocorrelations are taken into account. Illustrative Monte Carlo experiments indicate that the modified R/S test has power against at least two specific models of long-run memory, suggesting that stochastic models of short-range dependence may adequately capture the time series behavior of stock returns.

https://papers.ssrn.com/sol3/Delivery.cfm/nber_w2984.pdf?abstractid=463442&mirid=1&type=2

Long-Term Memory in Stock Market Prices

A test for long-term memory that is robust to short-range dependence is developed. It is a modification of the R/S statistic, and the relevant asymptotic sampling theory is derived via functional central limit theory. Contrary to previous findings, when applied to daily and monthly stock returns indexes over several time periods this test yields no evidence of long-range dependence once short-range dependence is accounted for. Monte Carlo experiments show that the modified R/S test has power against at least two specific models of long-term memory, suggesting that models with short-range dependence may adequately capture the behavior of historical stock returns. Copyright 1991 by The Econometric Society.

/pdf/long-term-memory-in-stock-market-prices-2l68x2fgkd.pdf

Andrew W. Lo

Papers

The econometrics of financial markets

Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test

Stock Market Prices Do Not Follow Random Walks: Evidence From a Simple Specification Test

Long-Term Memory in Stock Market Prices

Long-term memory in stock market prices