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題名 An intelligent option trading system based on heatmap analysis via PON/POD yields
作者 謝明華
Hsieh, Ming-Hua;Chen, Min-Kuan;Yang, Dong-Yuh;Wu, Mu-En
貢獻者 風管系
關鍵詞 Quantitative trading; Options trading strategy; Winning rate forecasting; Machine learning; Heatmap analysis
日期 2024-12
上傳時間 30-Apr-2025 15:03:04 (UTC+8)
摘要 In recent years, many studies have explored artificial intelligence (AI) in quantitative trading and financial prediction. Among financial products, options are highlighted for their risk management capabilities and defined trading cycles, yet they pose challenges due to their complexity. This paper proposes HAPPY (Heatmap Analysis via PON/POD Yield), an options trading system designed to predict expected winning rates (EW) by integrating actual profits, losses, and risk factors, thus enhancing traditional winning rate metrics. HAPPY employs heatmap analysis to address the risk of overfitting by smoothing isolated low EW values and incorporates machine learning (ML) models like random forest, extreme gradient boosting (XGBoost), and light gradient boosted machine (LGBM) for improved prediction accuracy. Employing TAIEX weekly options, the study evaluates EW and backtests trading performance, comparing empirical statistics and ML models. Findings indicate that ML models excel in accuracy and precision, though empirical statistics perform better in backtesting, especially as options near expiration. This research offers a robust options trading system that can be applied to other options markets or predictive models.
關聯 Expert Systems with Applications, Vol.257, 124948
資料類型 article
DOI https://doi.org/10.1016/j.eswa.2024.124948
dc.contributor 風管系
dc.creator (作者) 謝明華
dc.creator (作者) Hsieh, Ming-Hua;Chen, Min-Kuan;Yang, Dong-Yuh;Wu, Mu-En
dc.date (日期) 2024-12
dc.date.accessioned 30-Apr-2025 15:03:04 (UTC+8)-
dc.date.available 30-Apr-2025 15:03:04 (UTC+8)-
dc.date.issued (上傳時間) 30-Apr-2025 15:03:04 (UTC+8)-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/156765-
dc.description.abstract (摘要) In recent years, many studies have explored artificial intelligence (AI) in quantitative trading and financial prediction. Among financial products, options are highlighted for their risk management capabilities and defined trading cycles, yet they pose challenges due to their complexity. This paper proposes HAPPY (Heatmap Analysis via PON/POD Yield), an options trading system designed to predict expected winning rates (EW) by integrating actual profits, losses, and risk factors, thus enhancing traditional winning rate metrics. HAPPY employs heatmap analysis to address the risk of overfitting by smoothing isolated low EW values and incorporates machine learning (ML) models like random forest, extreme gradient boosting (XGBoost), and light gradient boosted machine (LGBM) for improved prediction accuracy. Employing TAIEX weekly options, the study evaluates EW and backtests trading performance, comparing empirical statistics and ML models. Findings indicate that ML models excel in accuracy and precision, though empirical statistics perform better in backtesting, especially as options near expiration. This research offers a robust options trading system that can be applied to other options markets or predictive models.
dc.format.extent 106 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Expert Systems with Applications, Vol.257, 124948
dc.subject (關鍵詞) Quantitative trading; Options trading strategy; Winning rate forecasting; Machine learning; Heatmap analysis
dc.title (題名) An intelligent option trading system based on heatmap analysis via PON/POD yields
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1016/j.eswa.2024.124948
dc.doi.uri (DOI) https://doi.org/10.1016/j.eswa.2024.124948