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Jackknifing stock return predictions

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  • Benjamin Chiquoine
  • Erik Hjalmarsson

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.

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Bibliographic Info

Paper provided by Board of Governors of the Federal Reserve System (U.S.) in its series International Finance Discussion Papers with number 932.

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Date of creation: 2008
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Handle: RePEc:fip:fedgif:932

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Keywords: Jackknife (Statistics) ; Stocks - Rate of return;

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Citations

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Cited by:
  1. Ye Chen & Jun Yu, 2012. "Optimal Jackknife for Discrete Time and Continuous Time Unit Root Models," Working Papers 15-2012, Singapore Management University, School of Economics.
  2. Morales-Arias, Leonardo & Moura, Guilherme V., 2013. "Adaptive forecasting of exchange rates with panel data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 493-509.
  3. Leonardo Morales-Arias & Alexander Dross, 2010. "Adaptive Forecasting of Exchange Rates with Panel Data," Research Paper Series 285, Quantitative Finance Research Centre, University of Technology, Sydney.
  4. Michael Scholz & Jens Perch Nielsen & Stefan Sperlich, 2012. "Nonparametric prediction of stock returns guided by prior knowledge," Graz Economics Papers 2012-02, University of Graz, Department of Economics.

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