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FILTER RULE TESTS OF THE ECONOMIC SIGNIFICANCE OF SERIAL DEPENDENCIES IN DAILY STOCK RETURNS
Author(s) -
Corrado Charles J.,
Lee SukHun
Publication year - 1992
Publication title -
journal of financial research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.319
H-Index - 49
eISSN - 1475-6803
pISSN - 0270-2592
DOI - 10.1111/j.1475-6803.1992.tb00119.x
Subject(s) - econometrics , stock (firearms) , autocorrelation , transaction cost , economics , portfolio , filter (signal processing) , standard deviation , database transaction , financial economics , statistics , mathematics , finance , computer science , mechanical engineering , engineering , computer vision , programming language
In this paper we examine the ability of filter rules to predict variation in expected daily returns for a sample of 120 Dow Jones and S&P 100 stocks from 1963 through 1989. Equally weighted portfolios of filter‐rule‐traded stocks consistently outperform a buy‐and‐hold portfolio of the same stocks before accounting for transaction costs. The difference in returns between filter rule and buy‐and‐hold portfolios is eliminated by one‐way transaction costs of 12 basis points. The economic significance of daily stock return autocorrelations is estimated. A marginal 1 percent increase in a first‐order autocorrelation increases filter rule returns by an estimated 3.84 percent.