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題名 基於分位數迴歸森林在選擇權結算價 報酬率預測及交易策略應用
Based on Quantile Regression Forests for Predicting Option Settlement Price Returns and Trading Strategy Applications
作者 陳旻寬
Chen, Min-Kuan
貢獻者 廖四郎
Liao, Szu-Lang
陳旻寬
Chen, Min-Kuan
關鍵詞 分位數迴歸森林
分位數迴歸
選擇權投資組合
選擇權結算價報酬率預測
選擇權交易
Quantile Regression Forests
Quantile Regression
Options portfolios
Options settlement price return forecasting
Options trading
日期 2024
上傳時間 5-Aug-2024 12:18:11 (UTC+8)
摘要 選擇權透過投資組合以應對市場預期或避險需求，顯示出其獨特優勢。然而，過往的選擇權研究多聚焦於定價和避險策略，對於確定投資組合履約價的探討相對缺乏。為解決此問題，本研究採用基於隨機森林的分位數迴歸森林(Quantile Regression Forests, QRF)，預測選擇權結算價的報酬率分位數並建立信賴區間。本文採用了蝴蝶價差策略和兀鷹價差策略，這兩種適用於預期市場波動範圍內的選擇權投資組合策略。透過對臺灣加權股價指數選擇權(臺指選)的週選擇權進行實證分析和回測交易，比較了QRF與傳統分位數迴歸(Quantile Regression, QR)的效能。結果顯示，QRF在預測準確度、勝率及報酬率方面均顯著優於QR，並在統計上達到顯著的正報酬率。這些發現強調了分位數預測與選擇權交易策略相結合在市場不確定性中的盈利潛力，並突出了機器學習在捕捉金融市場特徵方面的有效性。未來研究將探索結合深度學習以提高預測準確性，進行資金管理以優化風險控制，並將這些策略擴展到更多金融產品及選擇權投資組合。
Options demonstrate unique advantages through portfolio adjustments to meet market expectations or hedging needs. However, past research on options has predominantly focused on pricing and hedging strategies, with less discussion on determining strike prices for portfolios. To address this issue, this study employs Quantile Regression Forests (QRF), based on random forest algorithms, to predict the quantile of option settlement price returns and establish confidence intervals. This paper utilizes strategies such as the butterfly spread and condor spread, which are suited to expected market volatility ranges. Empirical analysis and backtesting trades were conducted using weekly options on the Taiwan Capitalization Weighted Stock Index (TAIEX options), comparing the efficacy of QRF with traditional Quantile Regression (QR). The results show that QRF significantly outperforms QR in terms of prediction accuracy, win rate, and return, achieving statistically significant positive returns. These findings highlight the profit potential of combining quantile forecasting with options trading strategies amidst market uncertainties and underscore the effectiveness of machine learning in capturing financial market characteristics. Future research will explore integrating deep learning to enhance predictive accuracy, optimize risk control through capital management, and extend these strategies to a broader range of financial products and options portfolio strategies.
參考文獻 Ayala, Jordan, García-Torres, Miguel, Noguera, José Luis Vázquez, Gómez-Vela, Francisco, & Divina, Federico. (2021). Technical analysis strategy optimization using a machine learning approach in stock market indices. Knowledge-Based Systems, 225, 107119. Bali, Turan G., Beckmeyer, Heiner, Mörke, Mathis, & Weigert, Florian. (2023). Option Return Predictability with Machine Learning and Big Data. The Review of Financial Studies, 36(9), 3548-3602. Baruník, Jozef, & Čech, František. (2021). Measurement of common risks in tails: A panel quantile regression model for financial returns. Journal of Financial Markets, 52, 100562. Basak, Suryoday, Kar, Saibal, Saha, Snehanshu, Khaidem, Luckyson, & Dey, Sudeepa Roy. (2019). Predicting the direction of stock market prices using tree-based classifiers. The North American Journal of Economics and Finance, 47, 552-567. Baur, Dirk G., & Dimpfl, Thomas. (2019). A Quantile Regression Approach to Estimate the Variance of Financial Returns. Journal of Financial Econometrics, 17(4), 616-644. Baur, Dirk, & Schulze, Niels. (2005). Coexceedances in financial markets—a quantile regression analysis of contagion. Emerging Markets Review, 6(1), 21-43. Breiman, Leo. (2001). Random Forests. Machine Learning, 45(1), 5-32. Breiman, Leo , Friedman, Jerome , Olshen, R.A. , & Stone, Charles J. . (1984). Classification and Regression Trees (1st ed.). Chapman and Hall/CRC. Cannon, Alex J. (2011). Quantile regression neural networks: Implementation in R and application to precipitation downscaling. Computers & Geosciences, 37(9), 1277-1284. Chronopoulos, Ilias, Raftapostolos, Aristeidis, & Kapetanios, George. (2023). Forecasting Value-at-Risk Using Deep Neural Network Quantile Regression. Journal of Financial Econometrics, 1-34. Cutler, Adele, Cutler, D. Richard, & Stevens, John R. (2012). Random Forests. In Cha Zhang & Yunqian Ma (Eds.), Ensemble Machine Learning: Methods and Applications (pp. 157-175). Springer New York. Dawid, A. P. (1984). Present Position and Potential Developments: Some Personal Views: Statistical Theory: The Prequential Approach. Journal of the Royal Statistical Society. Series A (General), 147(2), 278-292. Galit, Shmueli. (2010). To Explain or to Predict? Statistical Science, 25(3), 289-310. Gneiting, Tilmann, & Raftery, Adrian E. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association, 102(477), 359-378. Gupta, Rangan, Ji, Qiang, Pierdzioch, Christian, & Plakandaras, Vasilios. (2023). Forecasting the conditional distribution of realized volatility of oil price returns: The role of skewness over 1859 to 2023. Finance Research Letters, 58, 104501. Gyamerah, Samuel Asante, & Moyo, Edwin. (2020). Long-Term Exchange Rate Probability Density Forecasting Using Gaussian Kernel and Quantile Random Forest. Complexity, 2020, 1972962. Henrique, Bruno Miranda, Sobreiro, Vinicius Amorim, & Kimura, Herbert. (2019). Literature review: Machine learning techniques applied to financial market prediction. Expert Systems with Applications, 124, 226-251. Hull, John. (2018). Options, Futures, and Other Derivatives (10th ed.). Pearson India. Ivașcu, Codruț-Florin. (2021). Option pricing using Machine Learning. Expert Systems with Applications, 163, 113799. Johnson, Reid A. (2024). quantile-forest: A Python Package for Quantile Regression Forests. Journal of Open Source Software, 9(93), 5976. Koenker, Roger, & Bassett, Gilbert. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. Kolmogorov, An. (1933). Sulla determinazione empirica di una legge didistribuzione. Giorn Dell'inst Ital Degli Att, 4, 89-91. Kumbure, Mahinda Mailagaha, Lohrmann, Christoph, Luukka, Pasi, & Porras, Jari. (2022). Machine learning techniques and data for stock market forecasting: A literature review. Expert Systems with Applications, 197, 116659. Liu, Dehong, Liang, Yucong, Zhang, Lili, Lung, Peter, & Ullah, Rizwan. (2021). Implied volatility forecast and option trading strategy. International Review of Economics & Finance, 71, 943-954. Lohrmann, Christoph, & Luukka, Pasi. (2019). Classification of intraday S&P500 returns with a Random Forest. International Journal of Forecasting, 35(1), 390-407. Madhu, Biplab, Rahman, Md Azizur, Mukherjee, Arnab, Islam, Md Zahidul, Roy, Raju, & Ali, Lasker Ershad. (2021). A comparative study of support vector machine and artificial neural network for option price prediction. Journal of Computer and Communications, 9(05), 78-91. Mann, H. B., & Whitney, D. R. (1947). On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other. The Annals of Mathematical Statistics, 18(1), 50-60. Meinshausen, Nicolai, & Ridgeway, Greg. (2006). Quantile regression forests. Journal of machine learning research, 7(6). Meligkotsidou, Loukia, Panopoulou, Ekaterini, Vrontos, Ioannis D., & Vrontos, Spyridon D. (2014). A Quantile Regression Approach to Equity Premium Prediction. Journal of Forecasting, 33(7), 558-576. Nazareth, Noella, & Ramana Reddy, Yeruva Venkata. (2023). Financial applications of machine learning: A literature review. Expert Systems with Applications, 219, 119640. Ozbayoglu, Ahmet Murat, Gudelek, Mehmet Ugur, & Sezer, Omer Berat. (2020). Deep learning for financial applications : A survey. Applied Soft Computing, 93, 106384. Pedregosa, Fabian, Varoquaux, Gaël, Gramfort, Alexandre, Michel, Vincent, Thirion, Bertrand, Grisel, Olivier, Blondel, Mathieu, Prettenhofer, Peter, Weiss, Ron, & Dubourg, Vincent. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830. Pradeepkumar, Dadabada, & Ravi, Vadlamani. (2017). Forecasting financial time series volatility using Particle Swarm Optimization trained Quantile Regression Neural Network. Applied Soft Computing, 58, 35-52. Probst, Philipp, Wright, Marvin N., & Boulesteix, Anne-Laure. (2019). Hyperparameters and tuning strategies for random forest. WIREs Data Mining and Knowledge Discovery, 9(3), e1301. Sezer, Omer Berat, Gudelek, Mehmet Ugur, & Ozbayoglu, Ahmet Murat. (2020). Financial time series forecasting with deep learning : A systematic literature review: 2005–2019. Applied Soft Computing, 90, 106181. Smirnov, Nikolai V. (1939). On the estimation of the discrepancy between empirical curves of distribution for two independent samples. Bull. Math. Univ. Moscou, 2(2), 3-14. Taylor, James W. (2000). A quantile regression neural network approach to estimating the conditional density of multiperiod returns. Journal of Forecasting, 19(4), 299-311. Tyralis, Hristos, & Papacharalampous, Georgia. (2024). A review of predictive uncertainty estimation with machine learning. Artificial Intelligence Review, 57(4), 94. Waldmann, Elisabeth. (2018). Quantile regression: A short story on how and why. Statistical Modelling, 18(3-4), 203-218. Wilcoxon, Frank. (1992). Individual Comparisons by Ranking Methods. In Samuel Kotz & Norman L. Johnson (Eds.), Breakthroughs in Statistics: Methodology and Distribution (pp. 196-202). Springer New York. Wu, Jimmy Ming-Tai, Wu, Mu-En, Hung, Pang-Jen, Hassan, Mohammad Mehedi, & Fortino, Giancarlo. (2020). Convert index trading to option strategies via LSTM architecture. Neural Computing and Applications. Wu, M. E., & Chung, W. H. (2018). A Novel Approach of Option Portfolio Construction Using the Kelly Criterion. IEEE Access, 6, 53044-53052. Wu, Mu-En, Syu, Jia-Hao, & Chen, Chien-Ming. (2022). Kelly-Based Options Trading Strategies on Settlement Date via Supervised Learning Algorithms. Computational Economics, 59(4), 1627-1644. Yuan, X., Yuan, J., Jiang, T., & Ain, Q. U. (2020). Integrated Long-Term Stock Selection Models Based on Feature Selection and Machine Learning Algorithms for China Stock Market. IEEE Access, 8, 22672-22685.
描述 碩士
國立政治大學
金融學系
111352021
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111352021
資料類型 thesis
dc.contributor.advisor 廖四郎zh_TW
dc.contributor.advisor Liao, Szu-Langen_US
dc.contributor.author (Authors) 陳旻寬zh_TW
dc.contributor.author (Authors) Chen, Min-Kuanen_US
dc.creator (作者) 陳旻寬zh_TW
dc.creator (作者) Chen, Min-Kuanen_US
dc.date (日期) 2024en_US
dc.date.accessioned 5-Aug-2024 12:18:11 (UTC+8)-
dc.date.available 5-Aug-2024 12:18:11 (UTC+8)-
dc.date.issued (上傳時間) 5-Aug-2024 12:18:11 (UTC+8)-
dc.identifier (Other Identifiers) G0111352021en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/152469-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 金融學系zh_TW
dc.description (描述) 111352021zh_TW
dc.description.abstract (摘要) 選擇權透過投資組合以應對市場預期或避險需求，顯示出其獨特優勢。然而，過往的選擇權研究多聚焦於定價和避險策略，對於確定投資組合履約價的探討相對缺乏。為解決此問題，本研究採用基於隨機森林的分位數迴歸森林(Quantile Regression Forests, QRF)，預測選擇權結算價的報酬率分位數並建立信賴區間。本文採用了蝴蝶價差策略和兀鷹價差策略，這兩種適用於預期市場波動範圍內的選擇權投資組合策略。透過對臺灣加權股價指數選擇權(臺指選)的週選擇權進行實證分析和回測交易，比較了QRF與傳統分位數迴歸(Quantile Regression, QR)的效能。結果顯示，QRF在預測準確度、勝率及報酬率方面均顯著優於QR，並在統計上達到顯著的正報酬率。這些發現強調了分位數預測與選擇權交易策略相結合在市場不確定性中的盈利潛力，並突出了機器學習在捕捉金融市場特徵方面的有效性。未來研究將探索結合深度學習以提高預測準確性，進行資金管理以優化風險控制，並將這些策略擴展到更多金融產品及選擇權投資組合。zh_TW
dc.description.abstract (摘要) Options demonstrate unique advantages through portfolio adjustments to meet market expectations or hedging needs. However, past research on options has predominantly focused on pricing and hedging strategies, with less discussion on determining strike prices for portfolios. To address this issue, this study employs Quantile Regression Forests (QRF), based on random forest algorithms, to predict the quantile of option settlement price returns and establish confidence intervals. This paper utilizes strategies such as the butterfly spread and condor spread, which are suited to expected market volatility ranges. Empirical analysis and backtesting trades were conducted using weekly options on the Taiwan Capitalization Weighted Stock Index (TAIEX options), comparing the efficacy of QRF with traditional Quantile Regression (QR). The results show that QRF significantly outperforms QR in terms of prediction accuracy, win rate, and return, achieving statistically significant positive returns. These findings highlight the profit potential of combining quantile forecasting with options trading strategies amidst market uncertainties and underscore the effectiveness of machine learning in capturing financial market characteristics. Future research will explore integrating deep learning to enhance predictive accuracy, optimize risk control through capital management, and extend these strategies to a broader range of financial products and options portfolio strategies.en_US
dc.description.tableofcontents 第一章 緒論 1 第一節 研究背景及動機 1 第二節 研究目的 2 第二章 文獻回顧 4 第一節 選擇權交易 4 第二節 QR於金融預測 6 第三節 QRF於金融預測 7 第三章 研究方法 10 第一節 QR預測分位數 10 第二節 QRF預測分位數 11 第三節 選擇權投資組合應用 13 第四章 實證分析 16 第一節 資料描述 16 第二節 預測結算價報酬率結果 19 一、 預測分位數結果 19 二、 預測表現評估 24 第三節 回測交易績效評估 29 一、 回測交易績效評估 29 二、 回測報酬率統計檢定 32 第五章 結論與建議 37 第一節 結論 37 第二節 未來展望 37 參考文獻 39 附錄 43 第一節 技術指標定義 43 第二節 模型參數設定 44zh_TW
dc.format.extent 2930083 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111352021en_US
dc.subject (關鍵詞) 分位數迴歸森林zh_TW
dc.subject (關鍵詞) 分位數迴歸zh_TW
dc.subject (關鍵詞) 選擇權投資組合zh_TW
dc.subject (關鍵詞) 選擇權結算價報酬率預測zh_TW
dc.subject (關鍵詞) 選擇權交易zh_TW
dc.subject (關鍵詞) Quantile Regression Forestsen_US
dc.subject (關鍵詞) Quantile Regressionen_US
dc.subject (關鍵詞) Options portfoliosen_US
dc.subject (關鍵詞) Options settlement price return forecastingen_US
dc.subject (關鍵詞) Options tradingen_US
dc.title (題名) 基於分位數迴歸森林在選擇權結算價 報酬率預測及交易策略應用zh_TW
dc.title (題名) Based on Quantile Regression Forests for Predicting Option Settlement Price Returns and Trading Strategy Applicationsen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Ayala, Jordan, García-Torres, Miguel, Noguera, José Luis Vázquez, Gómez-Vela, Francisco, & Divina, Federico. (2021). Technical analysis strategy optimization using a machine learning approach in stock market indices. Knowledge-Based Systems, 225, 107119. Bali, Turan G., Beckmeyer, Heiner, Mörke, Mathis, & Weigert, Florian. (2023). Option Return Predictability with Machine Learning and Big Data. The Review of Financial Studies, 36(9), 3548-3602. Baruník, Jozef, & Čech, František. (2021). Measurement of common risks in tails: A panel quantile regression model for financial returns. Journal of Financial Markets, 52, 100562. Basak, Suryoday, Kar, Saibal, Saha, Snehanshu, Khaidem, Luckyson, & Dey, Sudeepa Roy. (2019). Predicting the direction of stock market prices using tree-based classifiers. The North American Journal of Economics and Finance, 47, 552-567. Baur, Dirk G., & Dimpfl, Thomas. (2019). A Quantile Regression Approach to Estimate the Variance of Financial Returns. Journal of Financial Econometrics, 17(4), 616-644. Baur, Dirk, & Schulze, Niels. (2005). Coexceedances in financial markets—a quantile regression analysis of contagion. Emerging Markets Review, 6(1), 21-43. Breiman, Leo. (2001). Random Forests. Machine Learning, 45(1), 5-32. Breiman, Leo , Friedman, Jerome , Olshen, R.A. , & Stone, Charles J. . (1984). Classification and Regression Trees (1st ed.). Chapman and Hall/CRC. Cannon, Alex J. (2011). Quantile regression neural networks: Implementation in R and application to precipitation downscaling. Computers & Geosciences, 37(9), 1277-1284. Chronopoulos, Ilias, Raftapostolos, Aristeidis, & Kapetanios, George. (2023). Forecasting Value-at-Risk Using Deep Neural Network Quantile Regression. Journal of Financial Econometrics, 1-34. Cutler, Adele, Cutler, D. Richard, & Stevens, John R. (2012). Random Forests. In Cha Zhang & Yunqian Ma (Eds.), Ensemble Machine Learning: Methods and Applications (pp. 157-175). Springer New York. Dawid, A. P. (1984). Present Position and Potential Developments: Some Personal Views: Statistical Theory: The Prequential Approach. Journal of the Royal Statistical Society. Series A (General), 147(2), 278-292. Galit, Shmueli. (2010). To Explain or to Predict? Statistical Science, 25(3), 289-310. Gneiting, Tilmann, & Raftery, Adrian E. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association, 102(477), 359-378. Gupta, Rangan, Ji, Qiang, Pierdzioch, Christian, & Plakandaras, Vasilios. (2023). Forecasting the conditional distribution of realized volatility of oil price returns: The role of skewness over 1859 to 2023. Finance Research Letters, 58, 104501. Gyamerah, Samuel Asante, & Moyo, Edwin. (2020). Long-Term Exchange Rate Probability Density Forecasting Using Gaussian Kernel and Quantile Random Forest. Complexity, 2020, 1972962. Henrique, Bruno Miranda, Sobreiro, Vinicius Amorim, & Kimura, Herbert. (2019). Literature review: Machine learning techniques applied to financial market prediction. Expert Systems with Applications, 124, 226-251. Hull, John. (2018). Options, Futures, and Other Derivatives (10th ed.). Pearson India. Ivașcu, Codruț-Florin. (2021). Option pricing using Machine Learning. Expert Systems with Applications, 163, 113799. Johnson, Reid A. (2024). quantile-forest: A Python Package for Quantile Regression Forests. Journal of Open Source Software, 9(93), 5976. Koenker, Roger, & Bassett, Gilbert. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. Kolmogorov, An. (1933). Sulla determinazione empirica di una legge didistribuzione. Giorn Dell'inst Ital Degli Att, 4, 89-91. Kumbure, Mahinda Mailagaha, Lohrmann, Christoph, Luukka, Pasi, & Porras, Jari. (2022). Machine learning techniques and data for stock market forecasting: A literature review. Expert Systems with Applications, 197, 116659. Liu, Dehong, Liang, Yucong, Zhang, Lili, Lung, Peter, & Ullah, Rizwan. (2021). Implied volatility forecast and option trading strategy. International Review of Economics & Finance, 71, 943-954. Lohrmann, Christoph, & Luukka, Pasi. (2019). Classification of intraday S&P500 returns with a Random Forest. International Journal of Forecasting, 35(1), 390-407. Madhu, Biplab, Rahman, Md Azizur, Mukherjee, Arnab, Islam, Md Zahidul, Roy, Raju, & Ali, Lasker Ershad. (2021). A comparative study of support vector machine and artificial neural network for option price prediction. Journal of Computer and Communications, 9(05), 78-91. Mann, H. B., & Whitney, D. R. (1947). On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other. The Annals of Mathematical Statistics, 18(1), 50-60. Meinshausen, Nicolai, & Ridgeway, Greg. (2006). Quantile regression forests. Journal of machine learning research, 7(6). Meligkotsidou, Loukia, Panopoulou, Ekaterini, Vrontos, Ioannis D., & Vrontos, Spyridon D. (2014). A Quantile Regression Approach to Equity Premium Prediction. Journal of Forecasting, 33(7), 558-576. Nazareth, Noella, & Ramana Reddy, Yeruva Venkata. (2023). Financial applications of machine learning: A literature review. Expert Systems with Applications, 219, 119640. Ozbayoglu, Ahmet Murat, Gudelek, Mehmet Ugur, & Sezer, Omer Berat. (2020). Deep learning for financial applications : A survey. Applied Soft Computing, 93, 106384. Pedregosa, Fabian, Varoquaux, Gaël, Gramfort, Alexandre, Michel, Vincent, Thirion, Bertrand, Grisel, Olivier, Blondel, Mathieu, Prettenhofer, Peter, Weiss, Ron, & Dubourg, Vincent. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830. Pradeepkumar, Dadabada, & Ravi, Vadlamani. (2017). Forecasting financial time series volatility using Particle Swarm Optimization trained Quantile Regression Neural Network. Applied Soft Computing, 58, 35-52. Probst, Philipp, Wright, Marvin N., & Boulesteix, Anne-Laure. (2019). Hyperparameters and tuning strategies for random forest. WIREs Data Mining and Knowledge Discovery, 9(3), e1301. Sezer, Omer Berat, Gudelek, Mehmet Ugur, & Ozbayoglu, Ahmet Murat. (2020). Financial time series forecasting with deep learning : A systematic literature review: 2005–2019. Applied Soft Computing, 90, 106181. Smirnov, Nikolai V. (1939). On the estimation of the discrepancy between empirical curves of distribution for two independent samples. Bull. Math. Univ. Moscou, 2(2), 3-14. Taylor, James W. (2000). A quantile regression neural network approach to estimating the conditional density of multiperiod returns. Journal of Forecasting, 19(4), 299-311. Tyralis, Hristos, & Papacharalampous, Georgia. (2024). A review of predictive uncertainty estimation with machine learning. Artificial Intelligence Review, 57(4), 94. Waldmann, Elisabeth. (2018). Quantile regression: A short story on how and why. Statistical Modelling, 18(3-4), 203-218. Wilcoxon, Frank. (1992). Individual Comparisons by Ranking Methods. In Samuel Kotz & Norman L. Johnson (Eds.), Breakthroughs in Statistics: Methodology and Distribution (pp. 196-202). Springer New York. Wu, Jimmy Ming-Tai, Wu, Mu-En, Hung, Pang-Jen, Hassan, Mohammad Mehedi, & Fortino, Giancarlo. (2020). Convert index trading to option strategies via LSTM architecture. Neural Computing and Applications. Wu, M. E., & Chung, W. H. (2018). A Novel Approach of Option Portfolio Construction Using the Kelly Criterion. IEEE Access, 6, 53044-53052. Wu, Mu-En, Syu, Jia-Hao, & Chen, Chien-Ming. (2022). Kelly-Based Options Trading Strategies on Settlement Date via Supervised Learning Algorithms. Computational Economics, 59(4), 1627-1644. Yuan, X., Yuan, J., Jiang, T., & Ain, Q. U. (2020). Integrated Long-Term Stock Selection Models Based on Feature Selection and Machine Learning Algorithms for China Stock Market. IEEE Access, 8, 22672-22685.zh_TW