Jackknifing stock return predictions

Benjamin Chiquoine and Erik Hjalmarsson

Journal of Empirical Finance, 2009, vol. 16, issue 5, 793-803

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 jackknife 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 jackknife estimates, to time the market.

Keywords: Bias; correction; Jackknifing; Predictive; regression; Stock; return; predictability (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:16:y:2009:i:5:p:793-803

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