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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

Indonesian

Artikel ini mempelajari waktu keluar untuk portofolio SPX pendek-straddle menggunakan kerangka kerja pengawasan dan penguatan-pelatihan dua tahap. Episode historis pertama menghasilkan label hold/exit optimal ex-post untuk pelatihan perceptron multilayer (MLP) menggunakan hanya variabel negara yang tersedia secara bersamaan pada saat implementasi. MLP yang dilatih kemudian memulai akting pengembangan yang disesuaikan dengan imbalan perdagangan yang menyadari risiko. Evaluasi out-of-sample selama 2024-2026 menunjukkan bahwa MLP yang diawasi secara substansial melebihi holding pasif dan aturan output berbasis waktu tetap dan keuntungan dan kerugian yang representatif. Melalui tujuh benih independen, MLP-initiated Actor-Criticages rata-rata 37.54% pengembalian kumulatif dan rasio Sharpe dari 1.705, dibandingkan dengan 35.0

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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