Equity premium prediction and optimal portfolio decision with Bagging

Anwen Yin

The North American Journal of Economics and Finance, 2020, vol. 54, issue C

Abstract: We propose using the statistical method of Bagging to forecast the equity premium out-of-sample for multivariate regression models. Bagging allows for the flexible and efficient extraction of valuable informational content from a large set of predictors, leading to statistically and economically significant gains relative to not only the historical mean, but also other soft-threshold methods such as forecast combinations and shrinkage estimators in our empirical results. Furthermore, we find that the source of economic gains for Bagging primarily comes from the fact that it encourages the investor to actively manage portfolio by flexibly utilizing short selling or leveraging to better time the market following correctly prognosticated trends. However, other strategies such as forecast combinations keep the equity shares nearly fixed regardless of the predicted market prospect.

Keywords: Equity premium; Optimal portfolio; Bagging; Soft threshold (search for similar items in EconPapers)
JEL-codes: C53 C58 G11 G17 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:54:y:2020:i:c:s1062940820301686

DOI: 10.1016/j.najef.2020.101274

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