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| Title: | 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 |
| Authors: | 吳永樂
Wu, Yong Le |
| Contributors: | 劉文卿
Liou, Wen Qing
吳永樂
Wu, Yong Le |
| Keywords: | 支持向量機
決策樹
台指期
預測模型
SVM
Decision Tree
Global Indices
Taiwan Stock Market |
| Date: | 2015 |
| Issue Date: | 2015-08-17 14:08:36 (UTC+8) |
| Abstract: | 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. |
| Reference: | 1. Aase, K.-G. (2011). Text Mining of News Articles for Stock Price Predictions, Norwegian University of Science and Technology.
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| Description: | 碩士
國立政治大學
資訊管理研究所
102356048 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G1023560482 |
| Data Type: | thesis |
| Appears in Collections: | [資訊管理學系] 學位論文
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| 048201.pdf | 1063Kb | Adobe PDF2 | 90 | View/Open |
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