Let´s do it again: bagging equity premium predictors

Arbeitspapier

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 tudies 21, 1511-1531, we show that we can improve the forecast performance further by smoothing the restriction through bagging.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Texto para discussão ; No. 604

Klassifikation und Themen

• Klassifikation: Wirtschaft
• Thema: Risikoprämie ; Kapitalmarkttheorie ; Prognoseverfahren

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Hillebrand, Eric ; Lee, Tae-hwy ; Medeiros, Marcelo C.
• Ereignis: Veröffentlichung
• (wer): Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Departamento de Economia
• (wo): Rio de Janeiro
• (wann): 2012

Weitere Informationen

• Handle: http://hdl.handle.net/10419/176087
• Letzte Aktualisierung: 10.03.2025, 11:42 MEZ