Filter Rules Based on Price and Volume in Individual Security Overreaction

I present evidence of predictability in a sample constructed to minimize concerns about time-varying risk premia and market-microstructure effects. I use filter rules on lagged return and lagged volume information to uncover weekly over-reaction profits on large-capitalization NYSE and AMEX securities. I find that decreasing-volume stocks experience greater reversals. Increasing-volume stocks exhibit weaker reversals and positive autocorrelation. A real-time simulation of the filter strategies suggests that an investor who pursues the filter strategy with relatively low transaction costs will strongly outperform an investor who follows a buy-and-hold strategy. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.

[1]  Stephen R. Foerster,et al.  Direct Evidence of Non-Trading of NYSE and Amex Stocks , 1999 .

[2]  P. Bossaerts,et al.  Implementing Statistical Criteria to Select Return Forecasting Models: What Do We Learn? , 1999 .

[3]  Franklin Allen,et al.  Using genetic algorithms to find technical trading rules , 1999 .

[4]  Gautam Kaul,et al.  An Anatomy of Trading Strategies , 1998 .

[5]  Donald B. Keim,et al.  Execution Costs and Investment Performance: An Empirical Analysis of Institutional Equity Trades , 1998 .

[6]  Donald B. Keim,et al.  Transactions costs and investment style: an inter-exchange analysis of institutional equity trades , 1997 .

[7]  Gautam Kaul,et al.  Profitability of Short-Term Contrarian Strategies: Implications for Market Efficiency , 1997 .

[8]  David Easley,et al.  Liquidity, Information, and Infrequently Traded Stocks , 1996 .

[9]  Roni Michaely,et al.  Trading Volume with Private Valuation: Evidence from the Ex-Dividend Day , 1996 .

[10]  A. Timmermann,et al.  Predictability of Stock Returns: Robustness and Economic Significance , 1995 .

[11]  M. Lipson,et al.  Continuations, Reversals, and Adverse Selection on the NASDAQ and NYSE/AMEX , 1995 .

[12]  F. Fabozzi,et al.  Predicting Intraday Price Reversals , 1995 .

[13]  Charles E. Wasley,et al.  Can We Implement Research on Stock Trading Rules? , 1995 .

[14]  H. Markowitz,et al.  Data Mining Corrections , 1994 .

[15]  Allaudeen Hameed,et al.  Volume and Autocovariances in Short-Horizon Individual Security Returns , 1994 .

[16]  David R. Peterson,et al.  Stock Returns following Large One‐Day Declines: Evidence on Short‐Term Reversals and Longer‐Term Performance , 1994 .

[17]  Maureen O'Hara,et al.  Market Statistics and Technical Analysis: The Role of Volume , 1994 .

[18]  Jiang Wang,et al.  A Model of Competitive Stock Trading Volume , 1994, Journal of Political Economy.

[19]  Charles M. C. Lee,et al.  Spreads, Depths, and the Impact of Earnings Information: An Intraday Analysis , 1993 .

[20]  Narasimhan Jegadeesh,et al.  Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency , 1993 .

[21]  Filter Rule Tests Of The Economic Significance Of Serial Dependencies In Daily Stock Returns , 1992 .

[22]  Jiang Wang,et al.  Trading Volume and Serial Correlation in Stock Returns , 1992 .

[23]  Randall S. Jones,et al.  The Chinese Economic Area: Economic Integration without a Free Trade Agreement , 1992 .

[24]  Blake LeBaron,et al.  Persistence of the Dow Jones Index on Rising Volume , 1992 .

[25]  R. Sweeney,et al.  The Reversal of Large Stock-Price Decreases , 1991 .

[26]  Grant R. Mcqueen,et al.  Are Stock Returns Predictable? A Test Using Markov Chains , 1991 .

[27]  Robert S. Harris,et al.  The postmerger share-price performance of acquiring firms , 1991 .

[28]  Josef Lakonishok,et al.  Anomalous Price Behavior Around Repurchase Tender Offers , 1990 .

[29]  L. Glosten,et al.  Economic Significance of Predictable Variations in Stock Index Returns , 1989 .

[30]  Bruce D. Grundy,et al.  Trade and the Revelation of Information through Prices and Direct Disclosure , 1989 .

[31]  A. Lo,et al.  Data-Snooping Biases in Tests of Financial Asset Pricing Models , 1989 .

[32]  A. Lo,et al.  When are Contrarian Profits Due to Stock Market Overreaction? , 1989 .

[33]  W. D. Bondt.  Stock Price Reversals and Overreaction to News Events: A Survey of Theory and Evidence , 1989 .

[34]  R. Sweeney,et al.  Some New Filter Rule Tests: Methods and Results , 1988, Journal of Financial and Quantitative Analysis.

[35]  Gautam Kaul,et al.  Time-Variation in Expected Returns , 1988 .

[36]  A. Gallant,et al.  Nonlinear Statistical Models , 1988 .

[37]  K. French,et al.  Stock return variances: The arrival of information and the reaction of traders , 1986 .

[38]  W. Newey,et al.  A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelationconsistent Covariance Matrix , 1986 .

[39]  R. Sweeney,et al.  Beating the Foreign Exchange Market , 1986 .

[40]  R. Kenneth,et al.  FRENCH, and . Stock return variances: The arrival of information and the reaction of traders, Journal of Financial Economics, , . , 1986 .

[41]  R. Thaler,et al.  Does the Stock Market Overreact , 1985 .

[42]  Christopher A. Sims,et al.  Martingale-Like Behavior of Prices and Interest Rates , 1984 .

[43]  R. Roll,et al.  A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficient Market , 2008 .

[44]  L. Hansen.  Large Sample Properties of Generalized Method of Moments Estimators , 1982 .

[45]  T. Day.  Asset returns and inflation , 1981 .

[46]  G. William Schwert,et al.  Asset returns and inflation , 1977 .

[47]  E. Fama,et al.  Filter Rules and Stock-Market Trading , 1966 .