Dynamic Delta Hedging with Confidence-Weighted Signals

Delta hedging is a critical component of option portfolio management. In the research literature, most studies assume strict delta hedging, where portfolio delta is maintained at zero. Reference [1] relaxes this restriction by introducing a partial delta hedging technique that conditions the hedge ratio on the confidence of the underlying’s directional prediction.

Specifically, the approach applies a multiplier to the hedge ratio: when delta is positive, and the model anticipates an upward move, the hedge is reduced to retain more exposure, and similarly, when delta is negative, and a decline is predicted, the hedge magnitude is again reduced. The authors pointed out,

We introduces a confidence-based hedge adjustment mechanism that integrates ML forecasts into option portfolio construction. By scaling hedge ratios according to model confidence, the approach moves beyond rigid delta neutrality and captures incremental returns otherwise missed. Empirically, we find that confidence scaling materially affects portfolio risk–return trade-offs: moderate scaling delivers the highest Sharpe ratios, while aggressive scaling yields higher volatility and weaker long-term performance. Our objective is not to identify the most predictive ML model or factor set, but to demonstrate a paradigm shift in hedging design. By relaxing the strict delta-neutral constraint, predictive signals can be more effectively incorporated into option strategies. Future research could extend this framework to multi-asset portfolios, employ more advanced ML models, or explore higher-dimensional feature sets, which may yield even stronger results and further validate the practical relevance of confidence-scaled hedging.

In short, the paper proposes a confidence-based delta hedging framework that dynamically adjusts hedge ratios based on the directional confidence of machine-learning classifiers. Results show that moderate scaling improves Sharpe ratios relative to a benchmark, while aggressive scaling increases volatility and deteriorates long-term performance.

This is a meaningful contribution, as it reinforces the importance of identifying market regimes and dynamically under- or over-hedging according to prevailing conditions.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Li, B., & Wu, C. (2026). Beyond delta neutrality: Confidence-scaled hedging with machine learning forecasts. Finance Research Letters, 87, 109098.

Originally Published Here: Dynamic Delta Hedging with Confidence-Weighted Signals

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