English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113318/144297 (79%)
Visitors : 50953062      Online Users : 960
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
    •  Adv. Search
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version
    政大機構典藏 > 商學院 > 金融學系 > 學位論文 >  Item 140.119/124144


    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.
    Reference: An, B. -J., Ang, A., Bali, G., & Cakici, N. (2014). The joint cross section of stocks and options. Journal of Finance, 69, 2279-2337.
    Bachelier, L. (1900). Théorie de la spéculation. Annales de l’Ecole Normale Supérieure, 3(17), 21-86.
    Banerjee, P. J., Doran, J. S., & Peterson, D. R. (2007). Implied volatility and future portfolio returns. Journal of Banking and Finance, 31, 3183-3199.
    Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81(3), 637-654.
    Brée, D. S., & Joseph, N. L. (2013). Testing for financial crashes using the Log Periodic Power Law model. International Review of Financial Analysis, 30, 287-297.
    Brown, C., & Abraham, F. (2012). Sum of perpetuities method for valuing stock prices. Journal of Economics, 38, 59-72.
    Chou, R. -Y., Lin, J., & Wu, C. -S. (1999). Modeling the Taiwan stock market and international linkages. Pacific Economic Review, 4, 305–320.
    Eun, C. S., & Shim, S. (1989). International transmission of stock market movements. Journal of Financial and Quantitative Analysis, 24 (2), 241-256.
    Fama, E. F., & French., K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3-56.
    Feigenbaum, J. A., & Freund, P. G. O. (1996). Discrete scale invariance in stock markets before crashes, International Journal of Modern Physics B, 10(27), 3737-3745.
    Gordon, M. J. (1959). Dividends, earnings and stock prices. Review of Economics and Statistics, 41(2), 99-105.
    Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor, MI: University of Michigan Press.
    Jiang, Z. -Q., Zhou, W. -X., Sornette, D., Woodard, R., Bastiaensen, K., & Cauwels, P. (2010). Bubble diagnosis and prediction of the 2005–2007 and 2008–2009 Chinese stock market bubbles. Journal of Economic Behavior & Organization, 74, 149-162.
    Johansen, A. (2003). Characterization of large price variations in financial markets. Physica A, 324, 157-166.
    Johansen, A., Ledoit, O., & Sornette, D. (2000). Crashes as critical points. International Journal of Theoretical and Applied Finance, 13(2), 19-255.
    Johansen, A., & Sornette, D. (1999a). Critical crash. Risk, 12, 91-94.
    Johansen, A., & Sornette, D. (1999b). Modeling the stock market prior to large crashes. The European Physical Journal B, 9, 167-174.
    Lintner, J. (1965). The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. Review of Economics and Statistics, 47, 13-37.
    Merton, R. C. (1976). Option pricing when underlying stock returns are discontinuous. Journal of Financial Economics, 3, 125-144.
    Rappaport, A. (1986). Creating shareholder value: The new standard for business performance. New York, NY: Simon and Schuster Publishing Group.
    Ross, S. A. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13, 341-360.
    Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425-442.
    Sornette, D., Johansen, A., & Bouchaud, J. -P. (1996). Stock market crashes, precursors and replicas. Journal de Physique I, 6(1), 167-175.
    Sornette, D., Takayasu, H., & Zhou, W. -X. (2003). Finite-time singularity signature of hyperinflation. Physica A, 325, 492-506.
    Sornette, D., & Zhou, W. -X. (2006). Predictability of large future changes in major financial indices. International Journal of Forecasting, 22, 153-168.
    Sprenkle, C. M. (1961). Warrant prices as indicators of expectations and preferences. Yale Economic Essays, 1, 178-231.
    Vandewalle, N., Ausloos, M., Boveroux, P., & Minguet, A. (1999). Visualizing the log-periodic pattern before crashes. The European Physical Journal B, 9, 355-359.
    Yan, W., Woodard, R., & Sornette, D. (2010). Diagnosis and prediction of tipping points in financial markets: crashes and rebounds. Physics Procedia, 3(5), 1641-1657.
    Zhou, W. -X., & Sornette, D. (2006a). Fundamental factors versus herding in the 2000–2005 US stock market and prediction. Physica A, 360, 459-482.
    Zhou, W. -X., & Sornette, D. (2006b). Is there a real-estate bubble in the US? Physica A, 361, 297-308.
    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:[金融學系] 學位論文

    Files in This Item:

    File SizeFormat
    203101.pdf2241KbAdobe PDF2144View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback