Enhancing stock market return predictability by using a novel autoencoder-based aggregate EPU index

Xiao-Xin Li, Chi Xie, Gang-Jin Wang, You Zhu, Zhao-Chen Li and Zhi-Yu Zhang

Pacific-Basin Finance Journal, 2025, vol. 93, issue C

Abstract: We propose a novel aggregate economic policy uncertainty (EPU) index, which is constructed using an autoencoder to extract the relevant component from eight news-based EPU proxies, for examining the impact of EPU on the stock market returns. We find that the autoencoder-based aggregate EPU index (i) exhibits the strong in-sample and out-of-sample forecasting power, and outperforms the existing EPU measures as well as well-known macroeconomic variables; (ii) generates the considerable economic value for the mean-variance investors in terms of portfolio optimization; (iii) derives its predictive ability primarily from the cash flow channel; and (iv) displays the asymmetric return predictability, with heightened performance in the low-sentiment periods.

Keywords: Economic policy uncertainty; Stock market; Return predictability; Asset pricing; Autoencoder; Deep learning (search for similar items in EconPapers)
JEL-codes: C45 C53 G11 G12 G17 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:pacfin:v:93:y:2025:i:c:s0927538x25002100

DOI: 10.1016/j.pacfin.2025.102873

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