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Beyond delta neutrality: Confidence-scaled hedging with machine learning forecasts

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  • Li, Boyan
  • Wu, Chongfeng

Abstract

We study whether machine learning (ML) forecasts can enhance option portfolio performance by relaxing strict delta neutrality. We propose a confidence-scaled hedging framework that dynamically adjusts hedge ratios according to the classification results of ML models. Using option and underlying ETF data, we find that moderate confidence scaling improves Sharpe ratios relative to a benchmark, while aggressive scaling increases volatility and weakens long-term returns. The results highlight that ML forecasts can be translated into economically meaningful improvements in derivatives trading and risk management.

Suggested Citation

  • Li, Boyan & Wu, Chongfeng, 2026. "Beyond delta neutrality: Confidence-scaled hedging with machine learning forecasts," Finance Research Letters, Elsevier, vol. 87(C).
    • Handle: RePEc:eee:finlet:v:87:y:2026:i:c:s1544612325023475
    DOI: 10.1016/j.frl.2025.109098
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    References listed on IDEAS

    as
    1. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    2. Leippold, Markus & Wang, Qian & Zhou, Wenyu, 2022. "Machine learning in the Chinese stock market," Journal of Financial Economics, Elsevier, vol. 145(2), pages 64-82.
    3. Ledoit, Oliver & Wolf, Michael, 2008. "Robust performance hypothesis testing with the Sharpe ratio," Journal of Empirical Finance, Elsevier, vol. 15(5), pages 850-859, December.
    4. Wang, Jianqiu & Wu, Ke & Yang, Sijie & Zhou, Dexin, 2024. "Asymmetry and the Cross-section of Option Returns," Journal of Financial Markets, Elsevier, vol. 71(C).
    5. Gurdip Bakshi & Nikunj Kapadia, 2003. "Delta-Hedged Gains and the Negative Market Volatility Risk Premium," The Review of Financial Studies, Society for Financial Studies, vol. 16(2), pages 527-566.
    6. Shao, Hualu & Zhou, Baicheng & Gong, Shaoqing, 2025. "Prediction of the implied volatility surface–An empirical analysis of the SSE 50ETF option based on CNNs," Finance Research Letters, Elsevier, vol. 77(C).
    7. Ofek, Eli & Richardson, Matthew & Whitelaw, Robert F., 2004. "Limited arbitrage and short sales restrictions: evidence from the options markets," Journal of Financial Economics, Elsevier, vol. 74(2), pages 305-342, November.
    8. Fan, Qingqian & Feng, Sixian, 2022. "An empirical study on the characterization of implied volatility and pricing in the Chinese option market," Finance Research Letters, Elsevier, vol. 49(C).
    9. Black, Fischer, 1976. "The pricing of commodity contracts," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 167-179.
    10. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    11. Li, Zhe & Shen, Jiashuang & Xiao, Weilin, 2024. "Volatility risk premium, good volatility and bad volatility: Evidence from SSE 50 ETF options," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
    12. Xintong (Eunice) Zhan & Bing Han & Jie Cao & Qing Tong, 2022. "Option Return Predictability," The Review of Financial Studies, Society for Financial Studies, vol. 35(3), pages 1394-1442.
    13. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    14. Turan G Bali & Heiner Beckmeyer & Mathis Mörke & Florian Weigert & Stefano Giglio, 2023. "Option Return Predictability with Machine Learning and Big Data," The Review of Financial Studies, Society for Financial Studies, vol. 36(9), pages 3548-3602.
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    Keywords

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    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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