臺灣博碩士論文加值系統

過去許多文獻指出投資者具有從眾心理，以及追漲殺跌的投資心態，所以投資人的情緒對於股市變化存在重大影響，其投資人情緒上揚，指數亦上漲；反之，投資人情緒下降，指數亦走跌，顯示投資人情緒的確會造成市場的衝擊，因而引發市場報酬的波動。除此之外，過去許多研究也常以迴歸模型檢定這些情緒指標對市場報酬的影響，但由於運用在迴歸模型之資料受限於必需服從古典五大假設，而運用於類神經網路之樣本資料並不受限於上述之假設，也無限制輸入變數是否具有共線性的問題，因此本研究使用倒傳遞類神經網路來學習投資人於現貨、期貨、選擇權的佈局，以預測台股期貨隔日的收盤指數。
本研究以台股期貨指數為研究對象，區分「全體市場投資人情緒指標」與「法人情緒指標」兩組模型，以倒傳遞類神經網路進行學習並預測台股期貨隔日之收盤指數。研究結果指出，投資人情緒指標可用來預測台股期貨指數；另外，於本研究模型中，「全體市場投資人情緒指標」在預測隔日台股期貨收盤指數較「法人情緒指標」準確。

Prior research shows investors have herding behavior and momentum strategy attitude. It shows that investors’ emotion will impact stock market and cause the fluctuation of market returns. Recent researches have examined how the emotional index influence on market returns, but found multi-collinearity among sample which indicate the relationship is nonlinear. This study predicts Taiwan Future Index closing price next day by using back propagation neural network to simulate investors'' behaviors in stocks, futures and options operating.
We conduct both whole market investors’ sentiment and Institutional Investors’ sentiment as model variables. Back propagation neural network was utilized to predict Taiwan Future Index closing price next day. The result shows that we can predict Taiwan Future Index closing price by whole Investor’s Sentiment. Furthermore, whole market investors’ sentiment is more accurate than Institutional Investors’ Sentiment when predicting Taiwan Future Index closing.

摘　要 i
ABSTRACT ii
誌　謝 iii
目　錄 iv
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 6
1.3 研究流程 6
1.4 論文架構 8
第二章 文獻回顧 9
2.1 投資人行為 9
2.1.1 從眾行為(Herding behavior) 10
2.1.2 動能策略(Momentum) 11
2.2 投資人情緒與投資報酬之相關性 12
2.2.1 投資人情緒指標 13
2.3類神經網路(Neural Network) 18
2.3.1類神經網路的源起 18
2.3.2類神經網路發展的現況 20
2.3.3倒傳遞類神經網路簡介及實證 21
第三章 研究方法 24
3.1 實驗架構 24
3.2 資料來源與研究期間 25
3.2.1 資料來源 25
3.2.2 研究期間 26
3.3研究變數之選取 27
3.4倒傳遞類神經網路演算法 32
3.4.1 資料正規化 32
3.4.2倒傳遞類神經網路之參數設定 33
3.4.3演算法流程 36
3.5 交易策略的制定 37
3.6 績效評估之方法 39
3.7獨立樣本t檢定 41
3.8型Ⅰ誤差與型Ⅱ誤差 43
第四章 實證分析 44
4.1 敘述性統計 44
4.2實驗結果與分析 49
4.3 統計檢定 61
4.3.1準確率檢定 62
4.3.2平均獲利檢定 64
4.4型Ⅰ誤差與型Ⅱ誤差 66
第五章 結論與建議 77
5.1結論 77
5.2研究貢獻 78
5.3來研究建議 78
參考文獻 80

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