Improving Option Price Performance Using Machine Learning Algorithms with Investor Sentiment: Evidence from the Taiwan Options Market

Chih-Yang Cheng et al.

Review of Pacific Basin Financial Markets and Policies2026https://doi.org/10.1142/s0219091526500189article
ABDC B
Weight
0.50

What the paper says

This study examines the effectiveness of incorporating investor sentiment into machine learning models based on decision trees– specifically, Random Forest, XGBoost (Extreme Gradient Boosting), and LightGBM (Light Gradient Boosting Machine) — for option pricing in the Taiwan market. The empirical results demonstrate that these machine learning models significantly outperform the traditional Black–Scholes model in pricing accuracy. Notably, adding investor sentiment enhances the models’ pricing performance, especially for at-the-money and in-the-money options, where pricing errors are reduced by 4 times and 2.6 times, respectively. The Random Forest model exhibits the best performance overall.

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https://doi.org/https://doi.org/10.1142/s0219091526500189

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@article{chih-yang2026,
  title        = {{Improving Option Price Performance Using Machine Learning Algorithms with Investor Sentiment: Evidence from the Taiwan Options Market}},
  author       = {Chih-Yang Cheng et al.},
  journal      = {Review of Pacific Basin Financial Markets and Policies},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1142/s0219091526500189},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.