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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/124144


    Title: 以遺傳演算法優化JLS模型的台股崩盤預測
    TAIEX Crash Prediction based on JLS Model with Genetic Algorithm
    Authors: 郭力帆
    Kuo, Li-Fan
    Contributors: 江彌修
    Chiang, Mi-Hsiu
    郭力帆
    Kuo, Li-Fan
    Keywords: JLS模型
    對數週期冪次法則
    崩盤預測
    遺傳演算法
    加權指數
    JLS model
    Log-Periodic Power Law
    Crash prediction
    Genetic Algorithm
    TAIEX
    Date: 2019
    Issue Date: 2019-07-01 10:48:16 (UTC+8)
    Abstract: 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.
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    Description: 碩士
    國立政治大學
    金融學系
    106352031
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0106352031
    Data Type: thesis
    DOI: 10.6814/NCCU201900053
    Appears in Collections:[Department of Money and Banking] Theses

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