Paper: arXiv 2407.21791

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.

Complexity vs Empirical Score

  • Math Complexity: 8.0/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced neural network architectures with end-to-end optimization and regularization techniques, indicating high mathematical complexity. It demonstrates strong empirical rigor through backtesting on a decade of S&P 100 options data, rigorous data filtering, and evaluation against rules-based strategies.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Develop End-to-End DL Model for Options Trading"] --> B["Data Input<br>S&P 100 Equity Options >10 Years"]
  B --> C["Methodology<br>Deep Learning vs. Traditional Models"]
  C --> D["Computational Process<br>Training with Turnover Regularization"]
  D --> E["Backtesting<br>Risk-Adjusted Performance Evaluation"]
  E --> F["Key Findings<br>Improved Performance Over Rules-Based Strategies"]
  F --> G["Outcome<br>Robust Signals with High Transaction Costs"]

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0 of 51 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.