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Student Number 954203045
Author Yun-Cheng Wu(吳昀錚)
Author's Email Address 954203045@cc.ncu.edu.tw
Statistics This thesis had been viewed 1583 times. Download 1008 times.
Department Information Management
Year 2007
Semester 2
Degree Master
Type of Document Master's Thesis
Language English
Title Predicting the Trend of Taiwan Weighted Stock Index with Text Mining Techniques
Date of Defense 2008-06-26
Page Count 27
Keyword
  • Classification
  • Short-Term
  • Stock
  • Taiwan Weighted Stock Index
  • Text Mining
  • Abstract Stock price trend forecasting is an interesting topic. If investors can master stock price trend in advance, they will gain profit efficiently. However, no method can predict the trend accurately because human behavior is quite difficult to understand. In the past, many studies work on the topic by adopting fundamental and technical analysis. Nevertheless, both of the two trading analyses ignore the influence of short-term stock market movement caused by financial news, but only research into long-term forecasting.
     In this paper, we aim to predict the movement of whole Taiwan stock market by utilizing text mining. We develop a system to classify on-line financial news articles. The classification results can decide our trading strategies, and then the performance of our system is evaluated by investing Taiwan Weighted Stock Index (TWSI).
     The results reveal that our system can earn an average return of 5.4% per month, and additionally, the system has statistically the higher average return than the certificate of deposit (CD) rate (α = 0.05). Therefore, we argue that the trading strategies provide by our system are valuable for the short-term investors.
    Table of Content Contents
    List of Figures            ii
    List of Tables            iii
    1.Introduction           1
    2.Related Work           3
    2.1.Text Mining           3
    2.1.1.Preprocessing       3
    2.1.2.Feature Selection       4
    2.1.3.Word Weighting       6
    2.1.4.Classifying           7
    2.2.Stock Price Trend Forecasting with Text Mining Techniques           9
    3.System Design           13
    3.1.Training Phase           14
    3.2.Test Phase           16
    4.Experimental Design and Results  18
    4.1.Experimental Design      18
    4.2.Experimental Results      20
    5.Conclusions and Future Directions  24
    References           25
    Reference References
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    Advisor
  • Shih-Chieh Chou(周世傑)
  • Files
  • 954203045.pdf
  • approve immediately
    Date of Submission 2008-07-17

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