Deep Learning for Options Trading: An End-To-End Approach

Wee Ling Tan, Stephen Roberts and Stefan Zohren

Papers from arXiv.org

Abstract: We introduce a novel approach to options trading strategies using a highly scalable and data-driven machine learning algorithm. In contrast to traditional approaches that often require specifications of underlying market dynamics or assumptions on an option pricing model, our models depart fundamentally from the need for these prerequisites, directly learning non-trivial mappings from market data to optimal trading signals. Backtesting on more than a decade of option contracts for equities listed on the S&P 100, we demonstrate that deep learning models trained according to our end-to-end approach exhibit significant improvements in risk-adjusted performance over existing rules-based trading strategies. We find that incorporating turnover regularization into the models leads to further performance enhancements at prohibitively high levels of transaction costs.

Date: 2024-07
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp and nep-mst
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Published in ICAIF '24: Proceedings of the 5th ACM International Conference on AI in Finance, 2024

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