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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-Aug-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 Qingen_US
dc.contributor.author (Authors) 吳永樂zh_TW
dc.contributor.author (Authors) Wu, Yong Leen_US
dc.creator (作者) 吳永樂zh_TW
dc.creator (作者) Wu, Yong Leen_US
dc.date (日期) 2015en_US
dc.date.accessioned 17-Aug-2015 14:08:36 (UTC+8)-
dc.date.available 17-Aug-2015 14:08:36 (UTC+8)-
dc.date.issued (上傳時間) 17-Aug-2015 14:08:36 (UTC+8)-
dc.identifier (Other Identifiers) G1023560482en_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) 102356048zh_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 i
Abstract ii
Content Index iii
Table Index v
Figure Index vi
Chapter 1 Introduction 1
1.1 Background 1
1.1.1 Taiwan and Global Stock Market 1
1.1.2 Machine Learning Techniques 1
1.2 Research Motivation 2
Chapter 2 Literature Review 3
2.1 Stock Market Forecasting 3
2.2 Decision Trees 5
2.3 Support Vector Machines 9
Chapter 3 Methodology 15
3.1 Data Preparation 16
3.1.1 up or down: LAP-LAM Strategy 16
3.2 Prediction Model 17
3.2.1 Cross Validation 18
3.2.2 Moving Window 18
3.2.3 Model Evaluation 19
Chapter 4 Experiment Design & Results 22
4.1 Preprocessing 22
4.2 Parameter Setting 23
4.3 Experiment 1: Cross Validation with SVM 23
4.4 Experiment 2: Cross Validation with Decision Tree 25
4.5 Cross Validation Results Comparison 26
4.6 Experiment 3: Moving Window (Pilot) 29
4.7 Experiment 4: Moving Window 30
Chapter 5 Conclusion & Future Work 31
Reference 34
Appendix 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/#G1023560482en_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) SVMen_US
dc.subject (關鍵詞) Decision Treeen_US
dc.subject (關鍵詞) Global Indicesen_US
dc.subject (關鍵詞) Taiwan Stock Marketen_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 Treeen_US
dc.type (資料類型) thesisen
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