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MLP-Initialized Reinforcement Learning for Exit Timing in SPX Short-Straddle Portfolios

Jianning Wang, Robert Ślepaczuk, Paweł Sakowski · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.13126 · cited by 0 works (OpenAlex) · open-access PDF

Korean

이 논문은 두 단계의 감독 및 강화 학습 프레임워크를 사용하여 SPX 짧은 스트레드 포트폴리오의 출력 타이밍을 연구합니다. 역사적 에피소드는 처음으로 여러 계층 인식자 (MLP)를 교육하기 위해 최적의 홀드 / 출력 레이블을 생성합니다. 훈련 된 MLP는 7 개의 독립적 인 씨앗을 통해 MLP-initialized Actor-Criticages 평균 37.54 %의 축적 수익률과 2024-2026 년에 대한 샘플 평가에 따르면 감독 된 MLP는 수행 할 수있는 수동적 인 홀딩과 대표적인 고정 시간 및 이익과 손실 기반 출력 규칙을 상당히 뛰어넘는 것으로 나타났습니다. ActorCritic Option for scratch-based and re

Machine translation (Meta M2M100); the English original follows.

Abstract

This paper studies exit timing for SPX short-straddle portfolios using a two-stage supervised and reinforcement-learning framework. Historical episodes first generate ex-post optimal hold/exit labels for training a multilayer perceptron (MLP) using only contemporaneously available state variables at deployment. The trained MLP then initializes reinforcement-learning actors that are fine-tuned with a risk-aware trading reward. Out-of-sample evaluation over 2024-2026 shows that the supervised MLP substantially outperforms passive holding and representative fixed-time and profit-and-loss-based exit rules. Across seven independent seeds, MLP-initialized Actor-Critic averages 37.54% cumulative return and a Sharpe ratio of 1.705, compared with 35.01% and 1.651 for the supervised MLP. Actor-Critic trained from scratch averages only 0.18% return. These findings show that oracle-guided supervised learning provides a stable foundation for reward-based policy fine-tuning in a sparse and path-dependent option-exit problem.

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Topics: Sharpe ratio, Artificial intelligence, Computer science, Machine learning, Reinforcement learning, Supervised learning

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