Reinforcement Learning and Portfolio Allocation: Challenging Traditional Allocation Methods

Matus Lavko, Tony Klein and Thomas Walther

No 2023/01, QBS Working Paper Series from Queen's University Belfast, Queen's Business School

Abstract: We test the out-of-sample trading performance of model-free reinforcement learning (RL) agents and compare them with the performance of equally-weighted portfolios and traditional mean-variance (MV) optimization benchmarks. By dividing European and U.S. indices constituents into factor datasets, the RL-generated portfolios face different scenarios defined by these factor environments. The RL approach is empirically evaluated based on a selection of measures and probabilistic assessments. Training these models only on price data and features constructed from these prices, the performance of the RL approach yields better risk-adjusted returns as well as probabilistic Sharpe ratios compared to MV specifications. However, this performance varies across factor environments. RL models partially uncover the nonlinear structure of the stochastic discount factor. It is further demonstrated that RL models are successful at reducing left-tail risks in out-of-sample settings. These results indicate that these models are indeed useful in portfolio management applications.

Keywords: Asset Allocation; Reinforcement Learning; Machine Learning; Portfolio Theory; Diversification (search for similar items in EconPapers)
JEL-codes: C44 C55 C58 G11 (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fmk, nep-ifn and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:qmsrps:202301

DOI: 10.2139/ssrn.4346043

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