Deep Learning, Predictability, and Optimal Portfolio Returns

49 Pages Posted: 27 Oct 2020 Last revised: 11 Sep 2022

See all articles by Mykola Babiak

Mykola Babiak

Lancaster University Management School

Jozef Baruník

Charles University in Prague - Department of Economics; Institute of Information Theory and Automation, Prague

Date Written: June 22, 2021

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

Keywords: Return Predictability, Portfolio Allocation, Machine Learning, Neural Networks, Empirical Asset Pricing

JEL Classification: C45, C53, E37, G11, G17

Suggested Citation

Babiak, Mykola and Barunik, Jozef, Deep Learning, Predictability, and Optimal Portfolio Returns (June 22, 2021). Available at SSRN: https://ssrn.com/abstract=3688577 or http://dx.doi.org/10.2139/ssrn.3688577

Mykola Babiak (Contact Author)

Lancaster University Management School ( email )

Bailrigg
Lancaster, LA1 4YX
United Kingdom

HOME PAGE: http://https://sites.google.com/site/mykolababiak/home

Jozef Barunik

Charles University in Prague - Department of Economics ( email )

Opletalova 26
Prague 1, 110 00
Czech Republic

HOME PAGE: http://ies.fsv.cuni.cz/en/staff/barunik

Institute of Information Theory and Automation, Prague ( email )

Pod vodarenskou vezi 4
CZ-18208 Praha 8
Czech Republic

HOME PAGE: http://staff.utia.cas.cz/barunik/home.htm

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