Conditional Value-at-Risk for Quantitative Trading: A Direct Reinforcement Learning Approach

Ali Al-Ameer and Khaled Alshehri

Papers from arXiv.org

Abstract: We propose a convex formulation for a trading system with the Conditional Value-at-Risk as a risk-adjusted performance measure under the notion of Direct Reinforcement Learning. Due to convexity, the proposed approach can uncover a lucrative trading policy in a "pure" online manner where it can interactively learn and update the policy without multi-epoch training and validation. We assess our proposed algorithm on a real financial market where it trades one of the largest US trust funds, SPDR, for three years. Numerical experiments demonstrate the algorithm's robustness in detecting central market-regime switching. Moreover, the results show the algorithm's effectiveness in extracting profitable policy while meeting an investor's risk preference under a conservative frictional market with a transaction cost of 0.15% per trade.

Date: 2021-09
New Economics Papers: this item is included in nep-cmp, nep-cwa and nep-rmg
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