臺灣博碩士論文加值系統

本文結合資料探勘與類神經網路的方法，實作一個股票預測系統。與其他研究不同的是，此系統是直接結合兩個方法，分別處理技術指標及總體經濟指標。同時，我們相信技術指標並不是每個時間點都是有效的，而技術指標的有效性會受其他的技術指標以及總體經濟指標的影響。因此，我們提出的系統結合這兩種方法進行了兩個實驗，驗證此系統在不同產業之間的預測能力。實證結果顯示，此系統能輔助投資者判斷正確的進出場時機，因而避免額外的損失，提高獲利能力，同時也能降低交易成本。

In this study, we develop a new hybrid stock prediction system by integrating data mining and artificial intelligence techniques. Different from other studies, this study proposes a system that does not predict stock price using these techniques directly. We posit that technical indicators are not always effective. Each indicator is affected by other indicators and fundamentalist factors. Consequently, the proposed system integrates these two techniques to optimize their advantages based on technical and fundamental indicators. We conduct two experiments to examine the prediction ability of the proposed system across different industries. The results reveal that the proposed system is capable of determining the right timing for an investor to avoid extra loss, increase profitability, and decrease trading cost.

Abstract i
Content ii
List of Table iii
List of Figure iv
1 Introduction 1
2 Literature review 3
3. Methodology 11
3.1 System construction 11
3.2 Data preprocess 11
3.3 System construction 17
4 Comparative analyses on returns and experiments 21
4.1 Dataset and model construction 21
4.2 Experiments 23
5 Conclusions 31
References 32
Appendix A 33

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