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題名 運用支持向量機和決策樹預測台指期走勢
Predicting Taiwan Stock Index Future Trend Using SVM and Decision Tree作者 吳永樂
Wu, Yong Le貢獻者 劉文卿
Liou, Wen Qing
吳永樂
Wu, Yong Le關鍵詞 支持向量機
決策樹
台指期
預測模型
SVM
Decision Tree
Global Indices
Taiwan Stock Market日期 2015 上傳時間 17-八月-2015 14:08:36 (UTC+8) 摘要 本研究利用479個全球指標對台指期建立預測模型。該模型可以預測台指期在未來K天的漲跌走勢。我們使用了兩種演算法（支持向量機和決策樹）以及兩種取樣方式（交叉驗證和移動視窗）進行預測。在交叉驗證的建模過程中，決策樹展現了較高的預測力，最高準確度達到了93.4%。在移動視窗的建模過程中，支持向量機表現較好，達到了79.97%的預測准確度。於此同時，不管是哪一種條件設定都表明當我們預測的週期拉長時，預測的效果相對較好。這說明全球市場對台灣市場的影響很大，但是需要一定的市場反應時間。該研究結果對投資人有一定的參考作用。在未來方向裡，可以嘗試使用改進的決策樹演算法，也可以結合回歸預測進行深入研究。
In this research, we build a stock price direction forecasting model with Taiwan Stock Index Future (TXF). The input data we used is 479 global indices. The classification algorithms we used are SVM and Decision Tree. This model can predict the up and down trend in the next k days. In the model building process, both cross validation and moving window are taking into account. As for the time period, both short term prediction (i.e. 1 day) and long term prediction (i.e. 100 days) are tested for comparison. The results showed that cross validation performs best with 93.4% in precision, and moving window reached 79.97% in precision when we use the last 60 days historical data to predict the up and down trend in the next 20 days. The results imply Taiwan stock market is significantly influenced by the global market in the long run. This finding could be further used by investors and also be studied with regression algorithms as a combination model to enhance its performance.參考文獻 1. Aase, K.-G. (2011). Text Mining of News Articles for Stock Price Predictions, Norwegian University of Science and Technology.2. Campbell, C., & Ying, Y. (2011). Learning with support vector machines. Synthesis Lectures on Artificial Intelligence and Machine Learning, 5(1), 1-95.3. Chen, A. S., Leung, M. T., & Daouk, H. (2003). Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index. Computers & Operations Research, 30(6), 901-923.4. Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge university press.5. Lin, S., Patel, S., Duncan, A., & Goodwin, L. (2003). Using decision trees and support vector machines to classify genes by names. In Proceeding of the Europen workshop on data mining and text mining for bioinformatics (pp. 35-41).6. Lu, Y. C., Fang, H., & Nieh, C. C. (2012). The price impact of foreign institutional herding on large-size stocks in the Taiwan stock market. Review of Quantitative Finance and Accounting, 39(2), 189-208.7. Mingers, J. (1989). An empirical comparison of selection measures for decision-tree induction. Machine learning, 3(4), 319-342.8. Ou, P., & Wang, H. (2009). Prediction of stock market index movement by ten data mining techniques. Modern Applied Science, 3(12), p28.9. Quinlan, J. R. (2014). C4. 5: programs for machine learning. Elsevier.10. Shen, S., Jiang, H., & Zhang, T. (2012). Stock market forecasting using machine learning algorithms. url: http://cs229. stanford. edu/proj2012/ShenJiangZhang-StockMarketForecastingusingMachineLearningAlgorithms. pdf (visited on 05/08/2015).11. Wu, M. C., Lin, S. Y., & Lin, C. H. (2006). An effective application of decision tree to stock trading. Expert Systems with Applications, 31(2), 270-274.12. Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., ... & Steinberg, D. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1-37.13. Yang, Y., & Liu, X. (1999, August). A re-examination of text categorization methods. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval (pp. 42-49). ACM. 描述 碩士
國立政治大學
資訊管理研究所
102356048資料來源 http://thesis.lib.nccu.edu.tw/record/#G1023560482 資料類型 thesis dc.contributor.advisor 劉文卿 zh_TW dc.contributor.advisor Liou, Wen Qing en_US dc.contributor.author (作者) 吳永樂 zh_TW dc.contributor.author (作者) Wu, Yong Le en_US dc.creator (作者) 吳永樂 zh_TW dc.creator (作者) Wu, Yong Le en_US dc.date (日期) 2015 en_US dc.date.accessioned 17-八月-2015 14:08:36 (UTC+8) - dc.date.available 17-八月-2015 14:08:36 (UTC+8) - dc.date.issued (上傳時間) 17-八月-2015 14:08:36 (UTC+8) - dc.identifier (其他 識別碼) G1023560482 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/77557 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理研究所 zh_TW dc.description (描述) 102356048 zh_TW dc.description.abstract (摘要) 本研究利用479個全球指標對台指期建立預測模型。該模型可以預測台指期在未來K天的漲跌走勢。我們使用了兩種演算法（支持向量機和決策樹）以及兩種取樣方式（交叉驗證和移動視窗）進行預測。在交叉驗證的建模過程中，決策樹展現了較高的預測力，最高準確度達到了93.4%。在移動視窗的建模過程中，支持向量機表現較好，達到了79.97%的預測准確度。於此同時，不管是哪一種條件設定都表明當我們預測的週期拉長時，預測的效果相對較好。這說明全球市場對台灣市場的影響很大，但是需要一定的市場反應時間。該研究結果對投資人有一定的參考作用。在未來方向裡，可以嘗試使用改進的決策樹演算法，也可以結合回歸預測進行深入研究。 zh_TW dc.description.abstract (摘要) In this research, we build a stock price direction forecasting model with Taiwan Stock Index Future (TXF). The input data we used is 479 global indices. The classification algorithms we used are SVM and Decision Tree. This model can predict the up and down trend in the next k days. In the model building process, both cross validation and moving window are taking into account. As for the time period, both short term prediction (i.e. 1 day) and long term prediction (i.e. 100 days) are tested for comparison. The results showed that cross validation performs best with 93.4% in precision, and moving window reached 79.97% in precision when we use the last 60 days historical data to predict the up and down trend in the next 20 days. The results imply Taiwan stock market is significantly influenced by the global market in the long run. This finding could be further used by investors and also be studied with regression algorithms as a combination model to enhance its performance. en_US dc.description.tableofcontents 摘要 iAbstract iiContent Index iiiTable Index vFigure Index viChapter 1 Introduction 11.1 Background 11.1.1 Taiwan and Global Stock Market 11.1.2 Machine Learning Techniques 11.2 Research Motivation 2Chapter 2 Literature Review 32.1 Stock Market Forecasting 32.2 Decision Trees 52.3 Support Vector Machines 9Chapter 3 Methodology 153.1 Data Preparation 163.1.1 up or down: LAP-LAM Strategy 163.2 Prediction Model 173.2.1 Cross Validation 183.2.2 Moving Window 183.2.3 Model Evaluation 19Chapter 4 Experiment Design & Results 224.1 Preprocessing 224.2 Parameter Setting 234.3 Experiment 1: Cross Validation with SVM 234.4 Experiment 2: Cross Validation with Decision Tree 254.5 Cross Validation Results Comparison 264.6 Experiment 3: Moving Window (Pilot) 294.7 Experiment 4: Moving Window 30Chapter 5 Conclusion & Future Work 31Reference 34Appendix A 36 zh_TW dc.format.extent 1089113 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G1023560482 en_US dc.subject (關鍵詞) 支持向量機 zh_TW dc.subject (關鍵詞) 決策樹 zh_TW dc.subject (關鍵詞) 台指期 zh_TW dc.subject (關鍵詞) 預測模型 zh_TW dc.subject (關鍵詞) SVM en_US dc.subject (關鍵詞) Decision Tree en_US dc.subject (關鍵詞) Global Indices en_US dc.subject (關鍵詞) Taiwan Stock Market en_US dc.title (題名) 運用支持向量機和決策樹預測台指期走勢 zh_TW dc.title (題名) Predicting Taiwan Stock Index Future Trend Using SVM and Decision Tree en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) 1. Aase, K.-G. (2011). Text Mining of News Articles for Stock Price Predictions, Norwegian University of Science and Technology.2. Campbell, C., & Ying, Y. (2011). Learning with support vector machines. Synthesis Lectures on Artificial Intelligence and Machine Learning, 5(1), 1-95.3. Chen, A. S., Leung, M. T., & Daouk, H. (2003). Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index. Computers & Operations Research, 30(6), 901-923.4. Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge university press.5. Lin, S., Patel, S., Duncan, A., & Goodwin, L. (2003). Using decision trees and support vector machines to classify genes by names. In Proceeding of the Europen workshop on data mining and text mining for bioinformatics (pp. 35-41).6. Lu, Y. C., Fang, H., & Nieh, C. C. (2012). The price impact of foreign institutional herding on large-size stocks in the Taiwan stock market. Review of Quantitative Finance and Accounting, 39(2), 189-208.7. Mingers, J. (1989). An empirical comparison of selection measures for decision-tree induction. Machine learning, 3(4), 319-342.8. Ou, P., & Wang, H. (2009). Prediction of stock market index movement by ten data mining techniques. Modern Applied Science, 3(12), p28.9. Quinlan, J. R. (2014). C4. 5: programs for machine learning. Elsevier.10. Shen, S., Jiang, H., & Zhang, T. (2012). Stock market forecasting using machine learning algorithms. url: http://cs229. stanford. edu/proj2012/ShenJiangZhang-StockMarketForecastingusingMachineLearningAlgorithms. pdf (visited on 05/08/2015).11. Wu, M. C., Lin, S. Y., & Lin, C. H. (2006). An effective application of decision tree to stock trading. Expert Systems with Applications, 31(2), 270-274.12. Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., ... & Steinberg, D. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1-37.13. Yang, Y., & Liu, X. (1999, August). A re-examination of text categorization methods. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval (pp. 42-49). ACM. zh_TW
