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題名 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作者 郭力帆
Kuo, Li-Fan貢獻者 江彌修
Chiang, Mi-Hsiu
郭力帆
Kuo, Li-Fan關鍵詞 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日期 2019 上傳時間 1-Jul-2019 10:48:16 (UTC+8) 摘要 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.參考文獻 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. 描述 碩士
國立政治大學
金融學系
106352031資料來源 http://thesis.lib.nccu.edu.tw/record/#G0106352031 資料類型 thesis dc.contributor.advisor 江彌修 zh_TW dc.contributor.advisor Chiang, Mi-Hsiu en_US dc.contributor.author (Authors) 郭力帆 zh_TW dc.contributor.author (Authors) Kuo, Li-Fan en_US dc.creator (作者) 郭力帆 zh_TW dc.creator (作者) Kuo, Li-Fan en_US dc.date (日期) 2019 en_US dc.date.accessioned 1-Jul-2019 10:48:16 (UTC+8) - dc.date.available 1-Jul-2019 10:48:16 (UTC+8) - dc.date.issued (上傳時間) 1-Jul-2019 10:48:16 (UTC+8) - dc.identifier (Other Identifiers) G0106352031 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/124144 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 金融學系 zh_TW dc.description (描述) 106352031 zh_TW dc.description.abstract (摘要) 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。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents 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 zh_TW dc.format.extent 2294970 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106352031 en_US dc.subject (關鍵詞) JLS模型 zh_TW dc.subject (關鍵詞) 對數週期冪次法則 zh_TW dc.subject (關鍵詞) 崩盤預測 zh_TW dc.subject (關鍵詞) 遺傳演算法 zh_TW dc.subject (關鍵詞) 加權指數 zh_TW dc.subject (關鍵詞) JLS model en_US dc.subject (關鍵詞) Log-Periodic Power Law en_US dc.subject (關鍵詞) Crash prediction en_US dc.subject (關鍵詞) Genetic Algorithm en_US dc.subject (關鍵詞) TAIEX en_US dc.title (題名) 以遺傳演算法優化JLS模型的台股崩盤預測 zh_TW dc.title (題名) TAIEX Crash Prediction based on JLS Model with Genetic Algorithm en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 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. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU201900053 en_US
