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題名 利用隨機森林模型建構台灣指數期貨交易策略
Constructing a TAIEX Futures Trading Strategy Using Random Forest作者 鄭仁杰
Cheng, Jen-Chieh貢獻者 江彌修
Chiang, Mi-Hsiu
鄭仁杰
Cheng, Jen-Chieh關鍵詞 機器學習
多數決學習
隨機森林
交易策略
台灣加權股價指數期貨
袋外錯誤率
卡馬比率
Machine learning
Ensemble learning
Random forest
Trading strategy
TAIEX futures
OOB error rate
Calmar ratio日期 2018 上傳時間 19-Jul-2018 17:26:12 (UTC+8) 摘要 過去幾十年以來，預測金融商品價格走勢一直都是被熱烈討論的研究領域，但由於各種不同面向的因素交互影響，導致市場特性總是複雜且波動，價格走勢預測更加困難。普遍而言，錯誤率可被視為交易策略風險的指標，因此必須極小化錯誤率才能讓每單位風險享有更高獲利。為了使問題更單純，本研究將價格走勢預測視為分類問題，而本篇文章會使用機器學習（Machine Learning）來預測類別。在眾多演算法中，本研究選用多數決學習（Ensemble Learning）中具有許多良好特性的隨機森林（Random Forest）為本次交易策略建構的基礎架構。 本研究選用技術面與籌碼面指標作為訓練模型的特徵，建構兩個交易策略，而分析預測結果的方法除了隨機森林的袋外錯誤率（OOB Error Rate）以外，本研究會更著重在績效表現，以更接近交易策略的本質。由於台股期貨報酬不符合常態，本研究引入一種更為直覺的指標－卡馬比率（Calmar Ratio）作為評估績效的主要標準，另外再加入多種績效指標來提升績效評估的穩健性。 本研究透過不同切入角度測試策略績效與穩健性，結果也一再顯示，扣除手續費後兩個策略的績效確實遠遠勝過大盤，且擊敗大盤的情況不僅存在於訓練區間，更能延伸到測試區間。除此之外，本研究透過種種數據驗證測試區間屬於單一景氣循環的上漲區段，而處於這樣的背景間接降低了測試區間內兩個交易策略績效的鑒別度，使交易策略雖然勝過大盤績效，但差異性不大，而從袋外錯誤率的角度可以發現，交易策略確實具有足夠的穩健性。
Over the past decades, predicting trends in financial products prices has been an area of interest, but due to the interaction effects of different factors from all sides, the nature of market is always complex and dynamic. In general, error rate is seen as a proxy of risk of trading strategy, and it needs to be minimized to improve strategy effectiveness. To simplify the problem, the forecasting problem in our research is treated as a classification problem, and Machine Learning is used to solve it. Because of some attractive characteristics, our research used one of Ensemble Learning, which is Random Forest, to construct trading strategies.Our research selected technical and chip indicators as the features to train model, and the ways to analyze predictions contained OOB error rate, which derived from Random Forest, and the performance indicators. Because TAIEX Futures historical returns are non-normal distribution, our research introduced an intuitive performance indicator- Calmar Ratio as the evaluation criteria, and the other performance indicators have been added to improve the robustness.Our research have tested the performance of strategies and the robustness from different angle, and the result shows that our strategies truly beat the benchmark in whole period, not just training period. Besides, there is a lot of evidence that testing period in our research was in recovery to the peak, and this will lower the discrimination between strategies and benchmark performance. However, from the point of view of OOB error rate, our strategies are truly sufficiently robust.參考文獻 1. Ballings, M., Van den Poel, D., Hespeels, N. and Gryp, R. (2015), “Evaluating Multiple Classifiers for Stock Price Direction Prediction,” Expert Systems with Applications, Vol. 42, No. 20, pp.7046-7056.2. Blume, L., Easley, D. and O′Hara, M. (1994), ”Market Statistics and Technical Analysis: The Role of Volume,” Journal of Finance, Vol. 49, No. 1, pp.153-181.3. Brieman, L. (2001), “Random Forests”, Machine Learning, Vol. 45, No. 1, pp.5-32.4. Brieman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J. (1984), “Classification and Regression Tree”, Wadsworth.5. Brooks, C., A. Rew, and S. Ritson (2001), “A Trading Strategy Based on the Lead-Lag Relationship between the Spot Index and Futures Contract for the FTSE 100,” International Journal of Forecasting, Vol. 17, pp.31-44.6. Chu, H. H., Chen, T. L., Cheng, C. H., Huang, C. C. (2009), “Fuzzy Dual-Factor Time-Series for Stock Index Forecasting,” Expert Systems with Applications, Vol. 36, No. 1, pp.165-171.7. Dutta, J., Bandopadhyay, G. and Sengupta, S. (2012), “Prediction of Stock Performance in the Indian Stock Market Using Logistic Regression,” International Journal of Business and information, Vol. 7, No. 1, pp.105-136.8. Grossman, S. J. and Stiglitz, J. E. (1980), “On the Impossibility of Informationally Efficient Markets,” The American Economic Review, Vol. 70, No. 3, pp.393-408.9. Ho, T. K. (1995), “Random Decision Forests”, Proceedings of 3rd International Conference on Document Analysis and Recognition, pp.278–282.10. Ho, T. K., Hull, J. J. and Srihari, S. N. (1994), “Decision Combination in Multiple Classifier Systems,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 16, No. 1, pp.66-75.11. Huang, C. J., Yang, D. X. and Chuang, Y. T. (2008), “Application of Wrapper Approach and Composite Classifier to the Stock Trend Prediction,” Expert Systems with Applications, Vol. 34, No. 4, pp.2870-2878.12. Hurst, H. E. (1951), “Long-Term Storage Capacity of Reservoirs”, Trans. Amer. Soc. Civil Engineers, Vol. 116, pp.770-799.13. Keating, C. and Shadwick, W. F. (2002), “A Universal Performance Measure”, Journal of Performance Measurement, Vol. 6, No. 3, pp.59-84.14. Khaidem, L., Saha, S. and Dey, S. R. (2016), “Predicting the Direction of Stock Market Prices Using Random Forest,” arXiv preprint arXiv:160500003.15. Kim, S. H. and Chun, S. H. (1998), “Graded Forecasting Using an Array of Bipolar Predictions: Application of Probabilistic Neural Networks to a Stock Market Index,” International Journal of Forecasting, Vol. 14, No. 3, pp.323-337.16. Kohavi, R. and John, G. (1997), “Wrappers for Feature Subset Selection,” Artificial Intelligence, Vol. 97, No. 12, pp.273-324.17. Kumar, M. and Thenmozhi, M. (2006), “Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest,” In Proceedings of Ninth Indian Institute of Capital Markets Conference, Mumbai, India.18. Lai, R. K., Fan, C. Y., Huang, W. H. and Chang, P. C. (2009), “Evolving and Clustering Fuzzy Decision Tree for Financial Time Series Data Forecasting,” Expert Systems with Applications, Vol. 36, No. 2, pp.3761-3773.19. Lo, A. W., Mamaysky, H. and Wang, J. (2000), “Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation,” Journal of Finance, Vol. 55, No. 4, pp.1705-1765.20. Malkiel, B. G. (2003), “The Efficient Market Hypothesis and Its Critics,” The Journal of Economic Perspectives, Vol. 17, No. 1, pp.59-82.21. Malkiel, B. G. and Fama, E. F. (1970), “Efficient Capital Markets: A Review of Theory and Empirical Work,” Journal of Finance, Vol. 25, No. 2, pp.383-417.22. Mandelbrot, B. B. and Wallis, J. (1968), “Noah, Joseph and Operational Hydrology”, Water Resources Research, Vol. 4, pp.909-918.23. Ren, N., Zargham, M. and Rahimi, S. (2006), “A Decision Tree-Based Classification Approach to Rule Extraction for Security Analysis,” International Journal of Information Technology and Decision Making, Vol. 5, No. 1, pp.227-240.24. Shannon, C. E. (1948), “A Mathematical Theory of Communication”, Bell System Technical Journal, Vol. 27, No. 3, pp.379-423 and 623-656.25. Sheu, H. J. and Wei, Y. C. (2011), “Options Trading Based on the Forecasting of Volatility Direction with the Incorporation of Investor Sentiment”, Emerging Markets Finance and Trade, Vol. 47, No. 2, pp.31-47.26. Simon, D. P. and Wiggins, R. A. (2001), “S&P Futures Returns and Contrary Sentiment Indicators,” Journal of Futures Markets, Vol. 21, No. 5, pp.447-462.27. Sortino, F. A. and Van der Meer, R. (1991), “Downside Risk”, The Journal of Portfolio Management, Vol. 17, No. 4, pp.27-31.28. Sortino, F. A., Van der Meer, R. and Plantinga, A. (1999), “The Dutch Triangle”, The Journal of Portfolio Management, Vol. 26, No. 1, pp.50-57.29. Wang, L. R. and Shen, C. H. (1999), “Do Foreign Investments Affect Foreign Exchange and Stock Markets – The Case of Taiwan,” Applied Economics, Vol. 31, No. 11, pp.1303-1314.30. Wilder, J. W. Jr. (1978), “New Concepts in Technical Trading Systems,” Trend Research.31. 陳彥碩 （2011），外資現貨買賣超、期貨與選擇權多空交易與大盤指數之關係：台灣證券市場實證研究，碩士論文，政治大學金融研究所。 描述 碩士
國立政治大學
金融學系
105352022資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105352022 資料類型 thesis dc.contributor.advisor 江彌修 zh_TW dc.contributor.advisor Chiang, Mi-Hsiu en_US dc.contributor.author (Authors) 鄭仁杰 zh_TW dc.contributor.author (Authors) Cheng, Jen-Chieh en_US dc.creator (作者) 鄭仁杰 zh_TW dc.creator (作者) Cheng, Jen-Chieh en_US dc.date (日期) 2018 en_US dc.date.accessioned 19-Jul-2018 17:26:12 (UTC+8) - dc.date.available 19-Jul-2018 17:26:12 (UTC+8) - dc.date.issued (上傳時間) 19-Jul-2018 17:26:12 (UTC+8) - dc.identifier (Other Identifiers) G0105352022 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/118755 - 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) 105352022 zh_TW dc.description.abstract (摘要) 過去幾十年以來，預測金融商品價格走勢一直都是被熱烈討論的研究領域，但由於各種不同面向的因素交互影響，導致市場特性總是複雜且波動，價格走勢預測更加困難。普遍而言，錯誤率可被視為交易策略風險的指標，因此必須極小化錯誤率才能讓每單位風險享有更高獲利。為了使問題更單純，本研究將價格走勢預測視為分類問題，而本篇文章會使用機器學習（Machine Learning）來預測類別。在眾多演算法中，本研究選用多數決學習（Ensemble Learning）中具有許多良好特性的隨機森林（Random Forest）為本次交易策略建構的基礎架構。 本研究選用技術面與籌碼面指標作為訓練模型的特徵，建構兩個交易策略，而分析預測結果的方法除了隨機森林的袋外錯誤率（OOB Error Rate）以外，本研究會更著重在績效表現，以更接近交易策略的本質。由於台股期貨報酬不符合常態，本研究引入一種更為直覺的指標－卡馬比率（Calmar Ratio）作為評估績效的主要標準，另外再加入多種績效指標來提升績效評估的穩健性。 本研究透過不同切入角度測試策略績效與穩健性，結果也一再顯示，扣除手續費後兩個策略的績效確實遠遠勝過大盤，且擊敗大盤的情況不僅存在於訓練區間，更能延伸到測試區間。除此之外，本研究透過種種數據驗證測試區間屬於單一景氣循環的上漲區段，而處於這樣的背景間接降低了測試區間內兩個交易策略績效的鑒別度，使交易策略雖然勝過大盤績效，但差異性不大，而從袋外錯誤率的角度可以發現，交易策略確實具有足夠的穩健性。 zh_TW dc.description.abstract (摘要) Over the past decades, predicting trends in financial products prices has been an area of interest, but due to the interaction effects of different factors from all sides, the nature of market is always complex and dynamic. In general, error rate is seen as a proxy of risk of trading strategy, and it needs to be minimized to improve strategy effectiveness. To simplify the problem, the forecasting problem in our research is treated as a classification problem, and Machine Learning is used to solve it. Because of some attractive characteristics, our research used one of Ensemble Learning, which is Random Forest, to construct trading strategies.Our research selected technical and chip indicators as the features to train model, and the ways to analyze predictions contained OOB error rate, which derived from Random Forest, and the performance indicators. Because TAIEX Futures historical returns are non-normal distribution, our research introduced an intuitive performance indicator- Calmar Ratio as the evaluation criteria, and the other performance indicators have been added to improve the robustness.Our research have tested the performance of strategies and the robustness from different angle, and the result shows that our strategies truly beat the benchmark in whole period, not just training period. Besides, there is a lot of evidence that testing period in our research was in recovery to the peak, and this will lower the discrimination between strategies and benchmark performance. However, from the point of view of OOB error rate, our strategies are truly sufficiently robust. en_US dc.description.tableofcontents 第一章、研究動機 1第二章、文獻回顧 4第三章、研究方法 8一、隨機森林交易策略架構流程 9（一）交易策略建構與探討 9（二）交易策略執行 10二、訊號生成 11三、特徵生成 12（一）技術面指標 13（二）籌碼面指標 16四、隨機森林算法流程 18五、隨機森林及決策樹 20（一）決策樹 20（二）隨機森林 29六、模型衡量 32七、績效計算與衡量 35八、模型穩定度檢驗 43第四章、實證結果 45一、資料描述與敘述統計 45二、模型建構 48三、模型測試 65四、模型穩定度 82第五章、結論與未來展望 85參考文獻 89 zh_TW dc.format.extent 3577087 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105352022 en_US dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) 多數決學習 zh_TW dc.subject (關鍵詞) 隨機森林 zh_TW dc.subject (關鍵詞) 交易策略 zh_TW dc.subject (關鍵詞) 台灣加權股價指數期貨 zh_TW dc.subject (關鍵詞) 袋外錯誤率 zh_TW dc.subject (關鍵詞) 卡馬比率 zh_TW dc.subject (關鍵詞) Machine learning en_US dc.subject (關鍵詞) Ensemble learning en_US dc.subject (關鍵詞) Random forest en_US dc.subject (關鍵詞) Trading strategy en_US dc.subject (關鍵詞) TAIEX futures en_US dc.subject (關鍵詞) OOB error rate en_US dc.subject (關鍵詞) Calmar ratio en_US dc.title (題名) 利用隨機森林模型建構台灣指數期貨交易策略 zh_TW dc.title (題名) Constructing a TAIEX Futures Trading Strategy Using Random Forest en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 1. Ballings, M., Van den Poel, D., Hespeels, N. and Gryp, R. (2015), “Evaluating Multiple Classifiers for Stock Price Direction Prediction,” Expert Systems with Applications, Vol. 42, No. 20, pp.7046-7056.2. Blume, L., Easley, D. and O′Hara, M. (1994), ”Market Statistics and Technical Analysis: The Role of Volume,” Journal of Finance, Vol. 49, No. 1, pp.153-181.3. Brieman, L. (2001), “Random Forests”, Machine Learning, Vol. 45, No. 1, pp.5-32.4. Brieman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J. (1984), “Classification and Regression Tree”, Wadsworth.5. Brooks, C., A. Rew, and S. Ritson (2001), “A Trading Strategy Based on the Lead-Lag Relationship between the Spot Index and Futures Contract for the FTSE 100,” International Journal of Forecasting, Vol. 17, pp.31-44.6. Chu, H. H., Chen, T. L., Cheng, C. H., Huang, C. C. (2009), “Fuzzy Dual-Factor Time-Series for Stock Index Forecasting,” Expert Systems with Applications, Vol. 36, No. 1, pp.165-171.7. Dutta, J., Bandopadhyay, G. and Sengupta, S. (2012), “Prediction of Stock Performance in the Indian Stock Market Using Logistic Regression,” International Journal of Business and information, Vol. 7, No. 1, pp.105-136.8. Grossman, S. J. and Stiglitz, J. E. (1980), “On the Impossibility of Informationally Efficient Markets,” The American Economic Review, Vol. 70, No. 3, pp.393-408.9. Ho, T. K. (1995), “Random Decision Forests”, Proceedings of 3rd International Conference on Document Analysis and Recognition, pp.278–282.10. Ho, T. K., Hull, J. J. and Srihari, S. N. (1994), “Decision Combination in Multiple Classifier Systems,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 16, No. 1, pp.66-75.11. Huang, C. J., Yang, D. X. and Chuang, Y. T. (2008), “Application of Wrapper Approach and Composite Classifier to the Stock Trend Prediction,” Expert Systems with Applications, Vol. 34, No. 4, pp.2870-2878.12. Hurst, H. E. (1951), “Long-Term Storage Capacity of Reservoirs”, Trans. Amer. Soc. Civil Engineers, Vol. 116, pp.770-799.13. Keating, C. and Shadwick, W. F. (2002), “A Universal Performance Measure”, Journal of Performance Measurement, Vol. 6, No. 3, pp.59-84.14. Khaidem, L., Saha, S. and Dey, S. R. (2016), “Predicting the Direction of Stock Market Prices Using Random Forest,” arXiv preprint arXiv:160500003.15. Kim, S. H. and Chun, S. H. (1998), “Graded Forecasting Using an Array of Bipolar Predictions: Application of Probabilistic Neural Networks to a Stock Market Index,” International Journal of Forecasting, Vol. 14, No. 3, pp.323-337.16. Kohavi, R. and John, G. (1997), “Wrappers for Feature Subset Selection,” Artificial Intelligence, Vol. 97, No. 12, pp.273-324.17. Kumar, M. and Thenmozhi, M. (2006), “Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest,” In Proceedings of Ninth Indian Institute of Capital Markets Conference, Mumbai, India.18. Lai, R. K., Fan, C. Y., Huang, W. H. and Chang, P. C. (2009), “Evolving and Clustering Fuzzy Decision Tree for Financial Time Series Data Forecasting,” Expert Systems with Applications, Vol. 36, No. 2, pp.3761-3773.19. Lo, A. W., Mamaysky, H. and Wang, J. (2000), “Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation,” Journal of Finance, Vol. 55, No. 4, pp.1705-1765.20. Malkiel, B. G. (2003), “The Efficient Market Hypothesis and Its Critics,” The Journal of Economic Perspectives, Vol. 17, No. 1, pp.59-82.21. Malkiel, B. G. and Fama, E. F. (1970), “Efficient Capital Markets: A Review of Theory and Empirical Work,” Journal of Finance, Vol. 25, No. 2, pp.383-417.22. Mandelbrot, B. B. and Wallis, J. (1968), “Noah, Joseph and Operational Hydrology”, Water Resources Research, Vol. 4, pp.909-918.23. Ren, N., Zargham, M. and Rahimi, S. (2006), “A Decision Tree-Based Classification Approach to Rule Extraction for Security Analysis,” International Journal of Information Technology and Decision Making, Vol. 5, No. 1, pp.227-240.24. Shannon, C. E. (1948), “A Mathematical Theory of Communication”, Bell System Technical Journal, Vol. 27, No. 3, pp.379-423 and 623-656.25. Sheu, H. J. and Wei, Y. C. (2011), “Options Trading Based on the Forecasting of Volatility Direction with the Incorporation of Investor Sentiment”, Emerging Markets Finance and Trade, Vol. 47, No. 2, pp.31-47.26. Simon, D. P. and Wiggins, R. A. (2001), “S&P Futures Returns and Contrary Sentiment Indicators,” Journal of Futures Markets, Vol. 21, No. 5, pp.447-462.27. Sortino, F. A. and Van der Meer, R. (1991), “Downside Risk”, The Journal of Portfolio Management, Vol. 17, No. 4, pp.27-31.28. Sortino, F. A., Van der Meer, R. and Plantinga, A. (1999), “The Dutch Triangle”, The Journal of Portfolio Management, Vol. 26, No. 1, pp.50-57.29. Wang, L. R. and Shen, C. H. (1999), “Do Foreign Investments Affect Foreign Exchange and Stock Markets – The Case of Taiwan,” Applied Economics, Vol. 31, No. 11, pp.1303-1314.30. Wilder, J. W. Jr. (1978), “New Concepts in Technical Trading Systems,” Trend Research.31. 陳彥碩 （2011），外資現貨買賣超、期貨與選擇權多空交易與大盤指數之關係：台灣證券市場實證研究，碩士論文，政治大學金融研究所。 zh_TW dc.identifier.doi (DOI) 10.6814/THE.NCCU.MB.017.2018.F06 -
