Portfolio constructions in cryptocurrency market: A CVaR-based deep reinforcement learning approach

Tianxiang Cui, Shusheng Ding, Huan Jin and Yongmin Zhang

Economic Modelling, 2023, vol. 119, issue C

Abstract: Cryptocurrency markets have much larger tail risk than traditional financial markets, and constructing portfolios with such large tail risk assets would be challenging. Therefore, cryptocurrency funds demand new superior risk management models and Conditional Value at Risk (CVaR) is a prevailing risk measure for constructing portfolios in stock markets with large tail risk. Consequently, our paper contributes to the literature by developing a new cryptocurrency portfolio model framework based on the CVaR risk measure and a deep reinforcement learning optimization framework. We use the data from cryptocurrency market starting 2015 to 2021, unfolding that CVaR measure with deep learning outperforms the traditional portfolio construction technique. Compared with traditional economic parameter-based portfolio models, our model free based approach can capture the nonlinear compounding effect of multiple risk shocks by deep reinforcement learning on the risk distribution with economic structural breakdown. It can guide investments in financial markets with high tail risks.

Keywords: Cryptocurrency market; Portfolio optimization; Efficient frontier; Reinforcement learning (search for similar items in EconPapers)
JEL-codes: G11 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:119:y:2023:i:c:s0264999322003157

DOI: 10.1016/j.econmod.2022.106078

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