Let's Do It Again: Bagging Equity Premium Predictors
AbstractThe literature on excess return prediction has considered a wide array of estimation schemes, among them unrestricted and restricted regression coefficients. We consider bootstrap aggregation (bagging) to smooth parameter restrictions. Two types of restrictions are considered: positivity of the regression coefficient and positivity of the forecast. Bagging constrained estimators can have smaller asymptotic mean-squared prediction errors than forecasts from a restricted model without bagging. Monte Carlo simulations show that forecast gains can be achieved in realistic sample sizes for the stock return problem. In an empirical application using the data set of Campbell, J., and S. Thompson (2008): “Predicting the Equity Premium Out of Sample: Can Anything Beat the Historical Average?”, Review of Financial Studies 21, 1511-1531, we show that we can improve the forecast performance further by smoothing the restriction through bagging.
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Bibliographic InfoPaper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2012-41.
Date of creation: 30 Sep 2012
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Constraints on predictive regression function; Bagging; Asymptotic MSE; Equity premium; Out-of-sample forecasting; Economic value functions.;
Other versions of this item:
- Erik Hillebrand & Tae-Hwy Lee & Marcelo Cunha Medeiros, 2012. "Let´s do it again: bagging equity premium predictors," Textos para discussÃ£o 604, Department of Economics PUC-Rio (Brazil).
- C5 - Mathematical and Quantitative Methods - - Econometric Modeling
- E4 - Macroeconomics and Monetary Economics - - Money and Interest Rates
- G1 - Financial Economics - - General Financial Markets
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-10-20 (All new papers)
- NEP-ECM-2012-10-20 (Econometrics)
- NEP-FOR-2012-10-20 (Forecasting)
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