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What can we learn from the return predictability over the business cycle?

Li Liu, Zhiyuan Pan and Yudong Wang

Journal of Forecasting, 2021, vol. 40, issue 1, 108-131

Abstract: In this paper, we forecast stock returns using time‐varying parameter (TVP) models with parameters driven by economic conditions. An in‐sample specification test shows significant variation in the parameters. Out‐of‐sample results suggest that the TVP models outperform their constant coefficient counterparts. We also find significant return predictability from both statistical and economic perspectives with the application of TVP models. The out‐of‐sample R2 of an equal‐weighted combination of TVP models is as high as 2.672%, and the gains in the certainty equivalent return are 214.7 basis points. Further analysis indicates that the improvement in predictability comes from the use of information on economic conditions rather than simply from allowing the coefficients to vary with time.

Date: 2021
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