Eric Hillebrand

Let's Do It Again: Bagging Equity Premium Predictors

Publikation: Working paperForskning


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  • Eric Tobias Hillebrand
  • Tae-Hwy Lee, University of California, Riverside, USA
  • Marcelo C. Medeiros, Pontifical Catholic University of Rio de Janeiro, Brasilien
The 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.
UdgiverInstitut for Økonomi, Aarhus Universitet
Antal sider36
StatusUdgivet - 9 okt. 2012
SerietitelCREATES Research Papers


  • Constraints on predictive regression function, Bagging, Asymptotic MSE, Equity premium; Out-of-sample forecasting, Economic value functions.

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