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題名 LPPL交易策略應用-探討亞洲主要股市指數
Applications of LPPL(Log-Periodic Power Law) trading strategies on major Asian stock market indices作者 黃健寧
Huang, Jian-Ning貢獻者 岳夢蘭
Yueh, Meng-Lan
黃健寧
Huang, Jian-Ning關鍵詞 LPPL交易策略
泡沫
亞洲股市
短週期
初始時間窗口
LPPL
Bubble
Asian markets
Short periodic
Initial time window日期 2021 上傳時間 2-九月-2021 15:46:15 (UTC+8) 摘要 本篇論文的模型主要建構於LPPL(Log-Periodic Power Law) (Johansen et al., 2000)。先前的研究者已將LPPL交易策略在全球股市上進行試驗，然而在亞洲市場，尤其是台灣、日本、中國股市上表現不突出或不一致，並認為主因-出在於亞洲股市主要屬於短循環週期(Pham Huu, 2018; Mamageishvili, 2019)。因此本篇論文將調整Mamageishvili的方法，以找出適合亞洲股市的LPPL初始時間窗口(initial time window)。首先利用LPPL在兩種過濾條件:早期預警(Early Warning), 結束標誌(End Flag)下來偵測各自的LPPL泡沫信心指標( LPPL Bubble Confidence Indicator)數值，再取100日移動平均，進而轉換成不同等級的交易訊號，以此作為交易。本研究結果發現台灣、日本、南韓股市指數避開2008年金融海嘯，並在2009年金融海嘯結束後低點準確進場，使得最終結果的各項報酬指標上均優於市場；另一方面，香港、中國上證、中國深圳指數除了準確避開2008年金融海嘯和進場於2009金融海嘯落底之時，並在2018年中美貿易戰中股市重挫時進場，而兩大中國指數更是再次避開2015年中國股災。本篇應證亞洲主要股市指數屬於短循環週期。在亞洲主要股市上，以LPPL短期初始時間窗口(initial time window)方式建構交易策略，可以在大型正(負)向泡沫發生時，抓對時機進出場，勝過買進持有策略並產生alpha。
The model of this thesis is mainly constructed from LPPL(Log-periodic Power Law) (Johansen et al., 2000). Previous researchers have tested LPPL trading strategies on global stock markets, but the performance of Asian markets, especially Taiwan, Japan , and China, is not remarkable or consistent, and the main reason is that Asian stock markets are mainly short cycle (Pham Huu, 2018; Mamageishvili, 2019). Therefore, this thesis will adjust Mamageishvili`s method to find the LPPL initial time window suitable for Asian stock markets. First, LPPL is used to detect LPPL Bubble Confidence Indicator values under two filter conditions: Early Warning and End Flag, and then adjust with a 100-day moving average. This translates into different levels of trading signals, which are used as real trade. The results of this study show that Taiwan, Japan, and South Korea stock market indexes avoid the financial crisis of 2008 and enter the bottom of the market after the ending of the financial crisis in 2009 accurately, which makes the final results of all the return and risk/reward ratios are better than the market’s corresponding ones; On the other hand, in addition, to accurately avoiding the financial crisis of 2008 and entering the bottom of the market after the ending of the financial crisis in 2009, Hong Kong, Shanghai, and Shenzhen indexes also entered the market when the stock market plunged in the China–United States trade war in 2018, while the two major Chinese indexes also avoided the Chinese stock market turbulence in 2015. This article should confirm that major Asian stock indexes belong to a short cycle. In major Asian stock markets, a trading strategy constructed with a short-term LPPL initial time window can outperform a buy-and-hold strategy and produce alpha in the event of a large positive (negative) bubble.參考文獻 Filimonov, V., and Sornette, D. (2013). “A Stable and Robust Calibration Scheme of the Log-Periodic Power Law Model.” Physica A: Statistical Mechanics and its Applications, Vol. 392, Issues. 17, pp. 3698–3707.Forró, Z., Woodard, R., and Sornette, D. (2015). “Using trading strategies to detect phase transitions in financial markets.” Physical Review E, Vol. 91, Issues. 4 . 042803Jacobsson E. (2009). “How to predict crashes in financial markets with the log-periodic power Law” (Master’s Thesis, Department of Mathematical Statistics,Stockholm University, Sweden). Retrieved from https://www2.math.su.se/matstat/reports/serieb/2009/rep7/report.pdfJiang, Z.-Q., Zou, W.-X., Sornette, D., Woodard, R., Bastiaensen, K., and Cauwels, P. (2010). “Bubble diagnosis and prediction of the 2005–2007 and 2008–2009 chinese stock market bubbles.” Journal of Economic Behavior & Organization, Vol. 74, Issues. 3, pp. 149–162.Johansen, A., Ledoit, O., and Sornette, D. (2000). “Crashes as critical points.” Int. J. Theor. Appl. Finance, Vol. 3, No. 2, pp. 219-255.Johansen, A., and Sornette, D. (2001). “Log-periodic power law bubbles in Latin-American and Asian markets and correlated anti-bubbles in Western stock markets: an empirical study” Int. J. Theor. Appl. Finance, Vol. 4, No. 6, pp. 853-920.Johansen, A. and Sornette, D. (2010). “Shocks, Crashes and Bubbles in FinancialMarkets.” Brussels Economic Review, Vol.53, Issues. 2, pp. 201–253.Pham Huu, N.-P. (2018). “Back-testing of trading strategies based on financial crisis observatory output.” (Master’s thesis, Swiss Federal Institute of Technology Zurich, Swiss). Retrived from https://ethz.ch/content/dam/ethz/special-interest/mtec/chair-of-entrepreneurial-risks-dam/documents/dissertation/master%20thesis/master_thesis_Tinatin%20_4March2019.pdfSornette, D., Johansen, A., and Bouchaud, J. (1996). “Stock market crashes, precursors and replicas.” Journal de Physique I, Vol. 6, No. 1, pp. 167-175.Sornette, D. and Johansen, A. (2001). “Significance of log-periodic precursors to financial crashes.” Quantitative Finance, Vol. 1, Issues. 4, pp. 452–471.Sornette, D. and Zhou, W.-X. (2006). “Predictability of Large Future Changes in major financial indices.” International Journal of Forecasting, Vol. 22, Issues. 1, pp. 153–168.Mamageishvili, T. (2019). “Back-testing of Trading Strategies Using Financial Crisis Observatory Output.” (Master’s thesis, Swiss Federal Institute of Technology Zurich, Swiss) Retrieved from https://ethz.ch/content/dam/ethz/special-interest/mtec/chair-of-entrepreneurial-risks-dam/documents/dissertation/master%20thesis/master_thesis_Tinatin%20_4March2019.pdfWheatley, S., Sornette, D., Huber, T., Reppen, M., and Gantner, R. N. (2018). “Are Bitcoin Bubbles Predictable? Combining a Generalized Metcalfe`s Law and the LPPLS Model.” Swiss Finance Institute Research, Paper No. 18-22.Zhang, Q., Sornette, D., Balcilar, M., Gupta, R., Ozdemir, Z. A., Yetkiner, H. (2016). “LPPLS bubble indicators over two centuries of the S&P 500 index.” Physica A: Statistical mechanics and its Applications, Vol 458, Issues C, pp. 126-139.Zhang, Q., Zhang, Q., and Sornette, D. (2015). “Early warning signals of financial crises with multi-scale quantile regressions of Log-Periodic Power Law Singularities.” Swiss Finance Institute Research, Paper No. 15-43.Zhou, W. X., and Sornette, D. (2003). “Renormalization group analysis of the 2000–2002 anti-bubble in the US S&P500 index: explanation of the hierarchy of five crashes and prediction.” Physica A: Statistical Mechanics and its Applications, Vol. 30, Issues 3–4, pp. 584-604. 描述 碩士
國立政治大學
財務管理學系
108357017資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108357017 資料類型 thesis dc.contributor.advisor 岳夢蘭 zh_TW dc.contributor.advisor Yueh, Meng-Lan en_US dc.contributor.author (作者) 黃健寧 zh_TW dc.contributor.author (作者) Huang, Jian-Ning en_US dc.creator (作者) 黃健寧 zh_TW dc.creator (作者) Huang, Jian-Ning en_US dc.date (日期) 2021 en_US dc.date.accessioned 2-九月-2021 15:46:15 (UTC+8) - dc.date.available 2-九月-2021 15:46:15 (UTC+8) - dc.date.issued (上傳時間) 2-九月-2021 15:46:15 (UTC+8) - dc.identifier (其他 識別碼) G0108357017 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/136835 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 財務管理學系 zh_TW dc.description (描述) 108357017 zh_TW dc.description.abstract (摘要) 本篇論文的模型主要建構於LPPL(Log-Periodic Power Law) (Johansen et al., 2000)。先前的研究者已將LPPL交易策略在全球股市上進行試驗，然而在亞洲市場，尤其是台灣、日本、中國股市上表現不突出或不一致，並認為主因-出在於亞洲股市主要屬於短循環週期(Pham Huu, 2018; Mamageishvili, 2019)。因此本篇論文將調整Mamageishvili的方法，以找出適合亞洲股市的LPPL初始時間窗口(initial time window)。首先利用LPPL在兩種過濾條件:早期預警(Early Warning), 結束標誌(End Flag)下來偵測各自的LPPL泡沫信心指標( LPPL Bubble Confidence Indicator)數值，再取100日移動平均，進而轉換成不同等級的交易訊號，以此作為交易。本研究結果發現台灣、日本、南韓股市指數避開2008年金融海嘯，並在2009年金融海嘯結束後低點準確進場，使得最終結果的各項報酬指標上均優於市場；另一方面，香港、中國上證、中國深圳指數除了準確避開2008年金融海嘯和進場於2009金融海嘯落底之時，並在2018年中美貿易戰中股市重挫時進場，而兩大中國指數更是再次避開2015年中國股災。本篇應證亞洲主要股市指數屬於短循環週期。在亞洲主要股市上，以LPPL短期初始時間窗口(initial time window)方式建構交易策略，可以在大型正(負)向泡沫發生時，抓對時機進出場，勝過買進持有策略並產生alpha。 zh_TW dc.description.abstract (摘要) The model of this thesis is mainly constructed from LPPL(Log-periodic Power Law) (Johansen et al., 2000). Previous researchers have tested LPPL trading strategies on global stock markets, but the performance of Asian markets, especially Taiwan, Japan , and China, is not remarkable or consistent, and the main reason is that Asian stock markets are mainly short cycle (Pham Huu, 2018; Mamageishvili, 2019). Therefore, this thesis will adjust Mamageishvili`s method to find the LPPL initial time window suitable for Asian stock markets. First, LPPL is used to detect LPPL Bubble Confidence Indicator values under two filter conditions: Early Warning and End Flag, and then adjust with a 100-day moving average. This translates into different levels of trading signals, which are used as real trade. The results of this study show that Taiwan, Japan, and South Korea stock market indexes avoid the financial crisis of 2008 and enter the bottom of the market after the ending of the financial crisis in 2009 accurately, which makes the final results of all the return and risk/reward ratios are better than the market’s corresponding ones; On the other hand, in addition, to accurately avoiding the financial crisis of 2008 and entering the bottom of the market after the ending of the financial crisis in 2009, Hong Kong, Shanghai, and Shenzhen indexes also entered the market when the stock market plunged in the China–United States trade war in 2018, while the two major Chinese indexes also avoided the Chinese stock market turbulence in 2015. This article should confirm that major Asian stock indexes belong to a short cycle. In major Asian stock markets, a trading strategy constructed with a short-term LPPL initial time window can outperform a buy-and-hold strategy and produce alpha in the event of a large positive (negative) bubble. en_US dc.description.tableofcontents 摘要 4Abstract 5第一章 緒論 6第二章 文獻回顧 7第三章 研究方法 9第一節LPPL模型推導 10第二節 LPPL參數估計式 13第三節 LPPL泡沫信心指標建立 13第四章 交易策略 16第一節 資料 16第二節 交易策略建構 17第五章 結果 19第一節 各組合表現彙整結果 20第二節 早期預警 22第三節 結束標誌 34第六章 結論 46參考文獻 48 zh_TW dc.format.extent 6807093 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108357017 en_US dc.subject (關鍵詞) LPPL交易策略 zh_TW dc.subject (關鍵詞) 泡沫 zh_TW dc.subject (關鍵詞) 亞洲股市 zh_TW dc.subject (關鍵詞) 短週期 zh_TW dc.subject (關鍵詞) 初始時間窗口 zh_TW dc.subject (關鍵詞) LPPL en_US dc.subject (關鍵詞) Bubble en_US dc.subject (關鍵詞) Asian markets en_US dc.subject (關鍵詞) Short periodic en_US dc.subject (關鍵詞) Initial time window en_US dc.title (題名) LPPL交易策略應用-探討亞洲主要股市指數 zh_TW dc.title (題名) Applications of LPPL(Log-Periodic Power Law) trading strategies on major Asian stock market indices en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Filimonov, V., and Sornette, D. (2013). “A Stable and Robust Calibration Scheme of the Log-Periodic Power Law Model.” Physica A: Statistical Mechanics and its Applications, Vol. 392, Issues. 17, pp. 3698–3707.Forró, Z., Woodard, R., and Sornette, D. (2015). “Using trading strategies to detect phase transitions in financial markets.” Physical Review E, Vol. 91, Issues. 4 . 042803Jacobsson E. (2009). “How to predict crashes in financial markets with the log-periodic power Law” (Master’s Thesis, Department of Mathematical Statistics,Stockholm University, Sweden). Retrieved from https://www2.math.su.se/matstat/reports/serieb/2009/rep7/report.pdfJiang, Z.-Q., Zou, W.-X., Sornette, D., Woodard, R., Bastiaensen, K., and Cauwels, P. (2010). “Bubble diagnosis and prediction of the 2005–2007 and 2008–2009 chinese stock market bubbles.” Journal of Economic Behavior & Organization, Vol. 74, Issues. 3, pp. 149–162.Johansen, A., Ledoit, O., and Sornette, D. (2000). “Crashes as critical points.” Int. J. Theor. Appl. Finance, Vol. 3, No. 2, pp. 219-255.Johansen, A., and Sornette, D. (2001). “Log-periodic power law bubbles in Latin-American and Asian markets and correlated anti-bubbles in Western stock markets: an empirical study” Int. J. Theor. Appl. Finance, Vol. 4, No. 6, pp. 853-920.Johansen, A. and Sornette, D. (2010). “Shocks, Crashes and Bubbles in FinancialMarkets.” Brussels Economic Review, Vol.53, Issues. 2, pp. 201–253.Pham Huu, N.-P. (2018). “Back-testing of trading strategies based on financial crisis observatory output.” (Master’s thesis, Swiss Federal Institute of Technology Zurich, Swiss). Retrived from https://ethz.ch/content/dam/ethz/special-interest/mtec/chair-of-entrepreneurial-risks-dam/documents/dissertation/master%20thesis/master_thesis_Tinatin%20_4March2019.pdfSornette, D., Johansen, A., and Bouchaud, J. (1996). “Stock market crashes, precursors and replicas.” Journal de Physique I, Vol. 6, No. 1, pp. 167-175.Sornette, D. and Johansen, A. (2001). “Significance of log-periodic precursors to financial crashes.” Quantitative Finance, Vol. 1, Issues. 4, pp. 452–471.Sornette, D. and Zhou, W.-X. (2006). “Predictability of Large Future Changes in major financial indices.” International Journal of Forecasting, Vol. 22, Issues. 1, pp. 153–168.Mamageishvili, T. (2019). “Back-testing of Trading Strategies Using Financial Crisis Observatory Output.” (Master’s thesis, Swiss Federal Institute of Technology Zurich, Swiss) Retrieved from https://ethz.ch/content/dam/ethz/special-interest/mtec/chair-of-entrepreneurial-risks-dam/documents/dissertation/master%20thesis/master_thesis_Tinatin%20_4March2019.pdfWheatley, S., Sornette, D., Huber, T., Reppen, M., and Gantner, R. N. (2018). “Are Bitcoin Bubbles Predictable? Combining a Generalized Metcalfe`s Law and the LPPLS Model.” Swiss Finance Institute Research, Paper No. 18-22.Zhang, Q., Sornette, D., Balcilar, M., Gupta, R., Ozdemir, Z. A., Yetkiner, H. (2016). “LPPLS bubble indicators over two centuries of the S&P 500 index.” Physica A: Statistical mechanics and its Applications, Vol 458, Issues C, pp. 126-139.Zhang, Q., Zhang, Q., and Sornette, D. (2015). “Early warning signals of financial crises with multi-scale quantile regressions of Log-Periodic Power Law Singularities.” Swiss Finance Institute Research, Paper No. 15-43.Zhou, W. X., and Sornette, D. (2003). “Renormalization group analysis of the 2000–2002 anti-bubble in the US S&P500 index: explanation of the hierarchy of five crashes and prediction.” Physica A: Statistical Mechanics and its Applications, Vol. 30, Issues 3–4, pp. 584-604. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202101135 en_US
