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

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 i...

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Bibliographic Details
Published in:arXiv.org 2021-09
Main Authors: Al-Ameer, Ali, Alshehri, Khaled
Format: Article
Language:English
Subjects:
Algorithms
Convexity
Learning
Risk
Robustness (mathematics)
Online Access:Get full text
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Summary: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.
ISSN:2331-8422