Deep Learning, Predictability, and Optimal Portfolio Returns

Mykola Babiak and Jozef Baruník

CERGE-EI Working Papers from The Center for Economic Research and Graduate Education - Economics Institute, Prague

Abstract: We study dynamic portfolio choice of a long-horizon investor who uses deep learning methods to predict equity returns when forming optimal portfolios. Our results show statistically and economically significant benefits from using deep learning to form optimal portfolios through certainty equivalent returns and Sharpe ratios. Return predictability via deep learning also generates substantially improved portfolio performance across different subsamples, particularly during recessionary periods. These gains are robust to including transaction costs, short-selling and borrowing constraints.

Keywords: return predictability; portfolio allocation; machine learning; neural networks; empirical asset pricing (search for similar items in EconPapers)
JEL-codes: C45 C53 E37 G11 G17 (search for similar items in EconPapers)
Date: 2020-12
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fmk, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Working Paper: Deep Learning, Predictability, and Optimal Portfolio Returns (2021)
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