← Back to results Improving Option Price Performance Using Machine Learning Algorithms with Investor Sentiment: Evidence from the Taiwan Options Market Chih-Yang Cheng et al.
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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@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},
} TY - JOUR
TI - Improving Option Price Performance Using Machine Learning Algorithms with Investor Sentiment: Evidence from the Taiwan Options Market
AU - al., Chih-Yang Cheng et
JO - Review of Pacific Basin Financial Markets and Policies
PY - 2026
ER - Chih-Yang Cheng et al. (2026). Improving Option Price Performance Using Machine Learning Algorithms with Investor Sentiment: Evidence from the Taiwan Options Market. *Review of Pacific Basin Financial Markets and Policies*. https://doi.org/https://doi.org/10.1142/s0219091526500189 Chih-Yang Cheng et al.. "Improving Option Price Performance Using Machine Learning Algorithms with Investor Sentiment: Evidence from the Taiwan Options Market." *Review of Pacific Basin Financial Markets and Policies* (2026). https://doi.org/https://doi.org/10.1142/s0219091526500189. 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 Policies · 2026
https://doi.org/https://doi.org/10.1142/s0219091526500189 Copy
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Flag this paper Evidence weight Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
F · citation impact 0.50 × 0.4 = 0.20 M · momentum 0.50 × 0.15 = 0.07 V · venue signal 0.50 × 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.