臺灣博碩士論文加值系統

本研究使用JLS模型分析2005年至2018年間，回測台股加權指數的崩盤事件與預測發生時間點，並透過納入長短期修正模型之經濟因子，提高模型的預測能力。在透過遺傳演算法的優化參數結果後，我們發現納入因子能有效提高模型擬合真實股價指數的能力，並且對於模型預測崩盤的準確性有顯著的提升。在分析預測誤差與股價特徵的關係中，區間天數與股價增長速度和模型預測日誤差呈現明顯相關性。而透過模型RMSE與非線性函數參數之敏感度分析中，我們發現參數多數都能落在全域最佳解附近，顯示遺傳演算法的優化結果相當良好。最終在比較納入短期衝擊因子對於模型預測能力亦有所提升，股價走勢也更具彈性。

This paper analyzes and predicts TAIEX crash events from 2005 to 2018 with JLS model, and increases the predictability by modifying JLS model by including economic factors. After we use the genetic algorithm to optimize the model with economic factors, the result shows that the predictability to a crash and fitting ability are both significantly increased. When analyzing the correlation between error days and stock price features, we find that the length of a period and the growth rate of a stock price are both correlated with the error days. We also find that most nonlinear parameters are close to the global optima through sensitivity analysis of RMSE between nonlinear parameters. Finally, our research shows that when including specific factors related with certain crash event, the predictability and the flexibility of JLS model increases further.

謝辭 II
目錄 VI
表目錄 VII
圖目錄 VIII
第一章 緒論 1
第一節 研究動機 1
第二節 研究目的 3
第三節 研究架構 5
第二章 文獻回顧 6
第一節 股價模型之文獻回顧 6
第二節 經濟因子之文獻回顧 8
第三章 研究方法 9
第一節 資料來源 10
第二節 JLS模型 11
第三節 遺傳演算法 14
第四章 實證分析 19
第一節 資料描述與模型建立 19
第二節 崩盤日預測與誤差分析 25
第三節 其他非線性函數參數分析 29
第四節 納入特定因素之模擬結果 36
第五章 結論與未來展望 43
參考文獻 46

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