Does a lot help a lot? Forecasting stock returns with pooling strategies in a data†rich environment

Fabian Baetje

Journal of Forecasting, 2018, vol. 37, issue 1, 37-63

Abstract: A variety of recent studies provide a skeptical view on the predictability of stock returns. Empirical evidence shows that most prediction models suffer from a loss of information, model uncertainty, and structural instability by relying on low†dimensional information sets. In this study, we evaluate the predictive ability of various lately refined forecasting strategies, which handle these issues by incorporating information from many potential predictor variables simultaneously. We investigate whether forecasting strategies that (i) combine information and (ii) combine individual forecasts are useful to predict US stock returns, that is, the market excess return, size, value, and the momentum premium. Our results show that methods combining information have remarkable in†sample predictive ability. However, the out†of†sample performance suffers from highly volatile forecast errors. Forecast combinations face a better bias–efficiency trade†off, yielding a consistently superior forecast performance for the market excess return and the size premium even after the 1970s.

Date: 2018
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https://doi.org/10.1002/for.2475

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:37:y:2018:i:1:p:37-63

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