Taming the Greeks: Option Portfolios with Inductive Biases

2026-09-27

AI Summary

This study addresses the limitation of existing deep learning-based options trading strategies, which lack explicit control over sensitivity to specific risk factors. We propose an end-to-end deep learning framework that, distinct from reinforcement learning simulation paradigms, directly optimizes risk-adjusted returns jointly with directional constraints using historical data. The core innovation lies in introducing a differentiable regularization technique that embeds option Greeks as penalty terms within the deep neural network loss function, thereby enforcing portfolio risk neutrality while maximizing returns. Experimental results demonstrate that the proposed approach significantly improves out-of-sample risk-adjusted returns on Nasdaq-100 options and effectively reduces directional exposure.

Abstract

We present an end-to-end deep learning framework for systematic options trading that directly embeds hedging behavior through explicit control of portfolio-level risk exposures. While neural networks trained to optimize risk-adjusted performance have been shown to outperform traditional rules-based strategies, such approaches remain agnostic to the sensitivities of the resulting portfolios with respect to specific underlying risk factors. We propose a general training objective that combines a performance-driven loss with a differentiable risk-sensitivity penalty, enforcing neutrality to selected risk dimensions. Unlike reinforcement learning methods that approximate optimal hedging policies via simulated market dynamics, our framework operates entirely on historical data and jointly optimizes risk-adjusted returns and targeted risk constraints in a single learning problem. We instantiate the framework on static delta-neutral straddle portfolios with the penalty directed at first-order directional exposure, and evaluate two penalty variants -- an exposure-normalized penalty and a Greek-ratio drift penalty. Empirical results on Nasdaq 100 equity options demonstrate that appropriately calibrated regularization simultaneously improves out-of-sample risk-adjusted performance relative to an unregularized baseline while reducing realized directional exposure.

Problem

• options trading
• risk exposure
• Greeks
• portfolio hedging
• deep learning

Innovation

• End-to-end deep learning
• Differentiable risk-sensitivity penalty
• Options trading
• Inductive biases
• Greeks hedging