Stock return predictability from a mixed model perspective

Zhifeng Dai and Huan Zhu

Pacific-Basin Finance Journal, 2020, vol. 60, issue C

Abstract: We find that mixing existing forecasting models can significantly improve prediction performance of stock returns. Empirical results suggest that the stock return forecasting by three proposed mixed models are more significant both in statistical and economic terms than the corresponding models in Campbell and Thompson (2008), Wang et al. (2018) and Zhang et al. (2019). This improvement of predictability is also remarkable when we employ the multivariate information to predict stock return. The prediction performance of mixed models is robust to a series of robustness test. Particularly, the three proposed mixed models obtain superior out-of-sample forecasting performance of stock return for business cycles, rolling window predictions and different out-of-sample periods.

Keywords: Mixed models; Stock return predictability; Out-of-sample forecast; Asset allocation (search for similar items in EconPapers)
JEL-codes: C53 G11 G17 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (21)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:pacfin:v:60:y:2020:i:c:s0927538x1930633x

DOI: 10.1016/j.pacfin.2020.101267

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