Jackknifing stock return predictions

Benjamin Chiquoine and Erik Hjalmarsson

No 932, International Finance Discussion Papers from Board of Governors of the Federal Reserve System (U.S.)

Abstract: We show that the general bias reducing technique of jackknifing can be successfully applied to stock return predictability regressions. Compared to standard OLS estimation, the jackknifing procedure delivers virtually unbiased estimates with mean squared errors that generally dominate those of the OLS estimates. The jackknifing method is very general, as well as simple to implement, and can be applied to models with multiple predictors and overlapping observations. Unlike most previous work on inference in predictive regressions, no specific assumptions regarding the data generating process for the predictors are required. A set of Monte Carlo experiments show that the method works well in finite samples and the empirical section finds that out-of-sample forecasts based on the jackknifed estimates tend to outperform those based on the plain OLS estimates. The improved forecast ability also translates into economically relevant welfare gains for an investor who uses the predictive regression, with jackknifed estimates, to time the market.

Keywords: Jackknife (Statistics); Stocks - Rate of return (search for similar items in EconPapers)
Date: 2008
New Economics Papers: this item is included in nep-ecm, nep-fmk and nep-for
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Citations: View citations in EconPapers (4)

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Journal Article: Jackknifing stock return predictions (2009)
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