Deep Learning, Predictability, and Optimal Portfolio Returns

Mykola Babiak and Jozef Baruník

Papers from arXiv.org

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. We demonstrate that a long-short-term-memory recurrent neural network, which excels in learning complex time-series dependencies, generates a superior performance among a variety of networks considered. Return predictability via deep learning 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.

Date: 2020-09, Revised 2021-07
New Economics Papers: this item is included in nep-big, nep-cmp and nep-fmk
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Citations: View citations in EconPapers (2)

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Working Paper: Deep Learning, Predictability, and Optimal Portfolio Returns (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2009.03394

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