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題名 擁擠交易：臺灣股市類股輪動策略
Crowded Trades: Sector Rotation Strategy in Taiwan Stock Market
作者 林雅琪
貢獻者 廖四郎
Liao, Szu-Lang
林雅琪
關鍵詞 擁擠交易
產業輪動
主成分分析
Crowded Trades
Sector Rotation
PCA
日期 2024
上傳時間 5-Aug-2024 12:19:19 (UTC+8)
摘要 本研究旨在探討擁擠交易現象與資產價格泡沫化的關聯，並提供一種發現泡沫擴 張及泡沫破裂階段的方法，使投資人能夠在價格上漲過程中獲利、在價格下降前出售。 研究中採用了兩種方法來判斷資產價格的階段：資產中心度和相對價值。前者判斷資 金是否流入或流出該資產，即資金在少數資產上流動的程度。當資產中心度高時，表 示大量資金流入或流出該資產，推升其價格上漲或驟降之可能性。後者判斷價格是否 已偏離其實際價值，當相對價值高於某一標準時，表示價格可能已偏離其實際價值， 存在泡沫破裂風險。 此外，本研究將這些方法結合 Black-Litterman 模型，利用此模型結合資產中心度 和相對價值，進行動態的資產配置，從而達到優於其餘資產配置模型的效果。
This study explores the relationship between crowded trading and asset bubbles, providing a method to identify the stages of bubble expansion and burst. This method enables investors to profit during the price increase and sell before the price declines. The study employs two methods to determine the stages of asset prices: centrality and relative value. The former assesses whether capital flows into or out of the asset, indicating the degree of capital movement in a few assets. When centrality is high, it suggests significant capital inflows or outflows. That increases the probability of price rises or sudden drops. The latter assesses whether the price has deviated from its actual value. When the relative value exceeds a certain threshold, the price may have deviated from its actual value, posing a risk of a bubble burst. Furthermore, this study integrates these methods with the Black-Litterman model. Combining centrality and relative value using this model performs dynamic asset allocation, achieving results superior to other models.
參考文獻 [1] 邊宇濤 (2019), 〈大中華區的行業輪動策略——基於擁擠交易〉, 國立 政治大學金融學系碩士論文。. [2] C. Alexiou and A. Tyagi, “Gauging the effectiveness of sector rotation strategies: evidence from the usa and europe,” Journal of Asset Man- agement, vol. 21, no. 3, pp. 239–260, 2020. [3] F. Black and R. Litterman, “Global portfolio optimization,” Financial analysts journal, vol. 48, no. 5, pp. 28–43, 1992. [4] M. Billio, M. Getmansky, A. W. Lo, and L. Pelizzon, “Econometric measures of connectedness and systemic risk in the finance and insurance sectors,” Journal of financial economics, vol. 104, no. 3, pp. 535–559, 2012. [5] M. Billio, M. Getmansky Sherman, and L. Pelizzon, “Crises and hedge fund risk,” UMASS-Amherst Working Paper, Yale ICF Working Paper, no. 07-14, pp. 10–08, 2010. [6] S. Benner, “Benner’s prophecies,” 1876. 33 [7] A.-C. Díaz-Mendoza and A. Pardo, “North american journal of eco- nomics and finance,” North American Journal of Economics and Fi- nance, vol. 52, p. 101124, 2020. [8] G. Connor and R. A. Korajczyk, “Risk and return in an equilibrium apt: Application of a new test methodology,” Journal of financial economics, vol. 21, no. 2, pp. 255–289, 1988. [9] C. Cao, Y. Chen, B. Liang, and A. W. Lo, “Can hedge funds time market liquidity?” Journal of Financial Economics, vol. 109, no. 2, pp. 493–516, 2013. [10] C. M. Conover, G. R. Jensen, R. R. Johnson, and J. M. Mercer, “Sector rotation and monetary conditions,” vol. 17, pp. 34 – 46, 2008. [11] E. F. Fama, “Two pillars of asset pricing,” American Economic Review, vol. 104, no. 6, pp. 1467–1485, 2014. [12] G. J. Feeney and D. D. Hester, “Stock market indices: A principal com- ponents analysis,” 1964. [13] W. Fung and D. A. Hsieh, “Empirical characteristics of dynamic trading strategies: The case of hedge funds,” The review of financial studies, vol. 10, no. 2, pp. 275–302, 1997. [14] R. Greenwood and D. Thesmar, “Stock price fragility,” Journal of Fi- nancial Economics, vol. 102, no. 3, pp. 471–490, 2011. 34 [15] J. Gastwirth, Y. Gel, and W. Miao, “The impact of levene’s test of equality of variances on statistical theory and practice,” Quality Engi- neering, vol. 24, p. 343, 2009. [16] S. Gu, B. Kelly, and D. Xiu, “Empirical asset pricing via machine learn- ing,” The Review of Financial Studies, vol. 33, no. 5, pp. 2223–2273, 2020. [17] H. Hotelling, “Analysis of a complex of statistical variables into principal components.” Journal of educational psychology, vol. 24, no. 6, p. 417, 1933. [18] W. Kinlaw, M. Kritzman, and D. Turkington, “Crowded trades: Impli- cations for sector rotation and factor timing,” The Journal of Portfolio Management, vol. 45, no. 5, pp. 46–57, 2019. [19] L. KPFRS, “On lines and planes of closest fit to systems of points in space,” in Proceedings of the 17th ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems (SIGMOD), 1901, p. 19. [20] D. Kai-Ineman and A. Tversky, “Prospect theory: An analysis of decision under risk,” Econometrica, vol. 47, no. 2, pp. 363–391, 1979. [21] X. Lu and Y. Shen, “The investment strategies based on sector rotation effect,” 2013 International Conference on Information Technology and Applications, pp. 489–492, 2013. [22] M. Lynch, “The investment clock,” Special report, 2004. 35 [23] R. Litterman, “Common factors affecting bond returns,” Journal of fixed income, pp. 54–61, 1991. [24] H. Markowitz, “Portfolio Selection,” Journal of Finance, vol. 7, no. 1, pp. 77–91, March 1952. [Online]. Available: https://ideas.repec.org/a/ bla/jfinan/v7y1952i1p77-91.html [25] R. C. Merton, “Lifetime portfolio selection under uncertainty: The continuous-time case,” The review of Economics and Statistics, pp. 247– 257, 1969. [26] S. Maillard, T. Roncalli, and J. Teïletche, “The properties of equally weighted risk contribution portfolios,” Journal of Portfolio Management, vol. 36, no. 4, p. 60, 2010. [27] A. Moghar and M. Hamiche, “Stock market prediction using lstm recur- rent neural network,” pp. 1168–1173, 2020. [28] J. Von Neumann and O. Morgenstern, “Theory of games and economic behavior, 2nd rev,” 1947. [29] M. Pericoli and M. Sbracia, “Crowded trades among hedge funds,” Banca d ’Italia working paper, 2010. [30] A. Rehman, A. K. Malik, B. Raza, and W. Ali, “A hybrid cnn-lstm model for improving accuracy of movie reviews sentiment analysis,” Multimedia Tools and Applications, vol. 78, pp. 26 597 – 26 613, 2019. 36 [31] M. Sauer, “Sector rotation through the business cycle: A machine learn- ing regime approach,” Econometric Modeling: Capital Markets - Fore- casting eJournal, 2019. [32] W. F. Sharpe, “Capital asset prices: A theory of market equilibrium under conditions of risk,” The journal of finance, vol. 19, no. 3, pp. 425–442, 1964. [33] D. Song, “Portfolio optimization by lstm with a selection of six stocks,” Advances in Economics, Management and Political Sciences, 2023. [34] W. Sharpe, “The sharpe ratio,” vol. 21, pp. 49 – 58, 1994. [35] Student, “The probable error of a mean,” Biometrika, vol. 6, no. 1, pp. 1–25, 1908. [Online]. Available: http://www.jstor.org/stable/2331554 [36] F. Zhang, Y. Ma, and S. Yu, “Investment model based on lstm network forecasting and portfolio investment,” Proceedings of the 2022 Interna- tional Conference on E-business and Mobile Commerce, 2022.
描述 碩士
國立政治大學
金融學系
111352036
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111352036
資料類型 thesis
dc.contributor.advisor 廖四郎zh_TW
dc.contributor.advisor Liao, Szu-Langen_US
dc.contributor.author (Authors) 林雅琪zh_TW
dc.creator (作者) 林雅琪zh_TW
dc.date (日期) 2024en_US
dc.date.accessioned 5-Aug-2024 12:19:19 (UTC+8)-
dc.date.available 5-Aug-2024 12:19:19 (UTC+8)-
dc.date.issued (上傳時間) 5-Aug-2024 12:19:19 (UTC+8)-
dc.identifier (Other Identifiers) G0111352036en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/152475-
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) 111352036zh_TW
dc.description.abstract (摘要) 本研究旨在探討擁擠交易現象與資產價格泡沫化的關聯，並提供一種發現泡沫擴 張及泡沫破裂階段的方法，使投資人能夠在價格上漲過程中獲利、在價格下降前出售。 研究中採用了兩種方法來判斷資產價格的階段：資產中心度和相對價值。前者判斷資 金是否流入或流出該資產，即資金在少數資產上流動的程度。當資產中心度高時，表 示大量資金流入或流出該資產，推升其價格上漲或驟降之可能性。後者判斷價格是否 已偏離其實際價值，當相對價值高於某一標準時，表示價格可能已偏離其實際價值， 存在泡沫破裂風險。 此外，本研究將這些方法結合 Black-Litterman 模型，利用此模型結合資產中心度 和相對價值，進行動態的資產配置，從而達到優於其餘資產配置模型的效果。zh_TW
dc.description.abstract (摘要) This study explores the relationship between crowded trading and asset bubbles, providing a method to identify the stages of bubble expansion and burst. This method enables investors to profit during the price increase and sell before the price declines. The study employs two methods to determine the stages of asset prices: centrality and relative value. The former assesses whether capital flows into or out of the asset, indicating the degree of capital movement in a few assets. When centrality is high, it suggests significant capital inflows or outflows. That increases the probability of price rises or sudden drops. The latter assesses whether the price has deviated from its actual value. When the relative value exceeds a certain threshold, the price may have deviated from its actual value, posing a risk of a bubble burst. Furthermore, this study integrates these methods with the Black-Litterman model. Combining centrality and relative value using this model performs dynamic asset allocation, achieving results superior to other models.en_US
dc.description.tableofcontents 誌謝 i 摘要 ii Abstract iii 目錄 iv 表次 vi 圖次 vii 第一章 緒論 1 第一節 研究背景 1 第二節 研究架構 2 第二章 文獻回顧 3 第一節 類股輪動策略 3 第二節 擁擠交易 4 第三節 主成份分析 (PCA) 5 第四節 資產配置方法 6 第三章 研究方法 7 第一節 資產中心度 7 第二節 資產相對價值 10 第三節 各條件結果 11 第四章 實證結果 13 第一節 實驗設定 13 第二節 類股輪動策略回測 21 第五章 結論 32 參考文獻 33zh_TW
dc.format.extent 3239779 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111352036en_US
dc.subject (關鍵詞) 擁擠交易zh_TW
dc.subject (關鍵詞) 產業輪動zh_TW
dc.subject (關鍵詞) 主成分分析zh_TW
dc.subject (關鍵詞) Crowded Tradesen_US
dc.subject (關鍵詞) Sector Rotationen_US
dc.subject (關鍵詞) PCAen_US
dc.title (題名) 擁擠交易：臺灣股市類股輪動策略zh_TW
dc.title (題名) Crowded Trades: Sector Rotation Strategy in Taiwan Stock Marketen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] 邊宇濤 (2019), 〈大中華區的行業輪動策略——基於擁擠交易〉, 國立 政治大學金融學系碩士論文。. [2] C. Alexiou and A. Tyagi, “Gauging the effectiveness of sector rotation strategies: evidence from the usa and europe,” Journal of Asset Man- agement, vol. 21, no. 3, pp. 239–260, 2020. [3] F. Black and R. Litterman, “Global portfolio optimization,” Financial analysts journal, vol. 48, no. 5, pp. 28–43, 1992. [4] M. Billio, M. Getmansky, A. W. Lo, and L. Pelizzon, “Econometric measures of connectedness and systemic risk in the finance and insurance sectors,” Journal of financial economics, vol. 104, no. 3, pp. 535–559, 2012. [5] M. Billio, M. Getmansky Sherman, and L. Pelizzon, “Crises and hedge fund risk,” UMASS-Amherst Working Paper, Yale ICF Working Paper, no. 07-14, pp. 10–08, 2010. [6] S. Benner, “Benner’s prophecies,” 1876. 33 [7] A.-C. Díaz-Mendoza and A. Pardo, “North american journal of eco- nomics and finance,” North American Journal of Economics and Fi- nance, vol. 52, p. 101124, 2020. [8] G. Connor and R. A. Korajczyk, “Risk and return in an equilibrium apt: Application of a new test methodology,” Journal of financial economics, vol. 21, no. 2, pp. 255–289, 1988. [9] C. Cao, Y. Chen, B. Liang, and A. W. Lo, “Can hedge funds time market liquidity?” Journal of Financial Economics, vol. 109, no. 2, pp. 493–516, 2013. [10] C. M. Conover, G. R. Jensen, R. R. Johnson, and J. M. Mercer, “Sector rotation and monetary conditions,” vol. 17, pp. 34 – 46, 2008. [11] E. F. Fama, “Two pillars of asset pricing,” American Economic Review, vol. 104, no. 6, pp. 1467–1485, 2014. [12] G. J. Feeney and D. D. Hester, “Stock market indices: A principal com- ponents analysis,” 1964. [13] W. Fung and D. A. Hsieh, “Empirical characteristics of dynamic trading strategies: The case of hedge funds,” The review of financial studies, vol. 10, no. 2, pp. 275–302, 1997. [14] R. Greenwood and D. Thesmar, “Stock price fragility,” Journal of Fi- nancial Economics, vol. 102, no. 3, pp. 471–490, 2011. 34 [15] J. Gastwirth, Y. Gel, and W. Miao, “The impact of levene’s test of equality of variances on statistical theory and practice,” Quality Engi- neering, vol. 24, p. 343, 2009. [16] S. Gu, B. Kelly, and D. Xiu, “Empirical asset pricing via machine learn- ing,” The Review of Financial Studies, vol. 33, no. 5, pp. 2223–2273, 2020. [17] H. Hotelling, “Analysis of a complex of statistical variables into principal components.” Journal of educational psychology, vol. 24, no. 6, p. 417, 1933. [18] W. Kinlaw, M. Kritzman, and D. Turkington, “Crowded trades: Impli- cations for sector rotation and factor timing,” The Journal of Portfolio Management, vol. 45, no. 5, pp. 46–57, 2019. [19] L. KPFRS, “On lines and planes of closest fit to systems of points in space,” in Proceedings of the 17th ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems (SIGMOD), 1901, p. 19. [20] D. Kai-Ineman and A. Tversky, “Prospect theory: An analysis of decision under risk,” Econometrica, vol. 47, no. 2, pp. 363–391, 1979. [21] X. Lu and Y. Shen, “The investment strategies based on sector rotation effect,” 2013 International Conference on Information Technology and Applications, pp. 489–492, 2013. [22] M. Lynch, “The investment clock,” Special report, 2004. 35 [23] R. Litterman, “Common factors affecting bond returns,” Journal of fixed income, pp. 54–61, 1991. [24] H. Markowitz, “Portfolio Selection,” Journal of Finance, vol. 7, no. 1, pp. 77–91, March 1952. [Online]. Available: https://ideas.repec.org/a/ bla/jfinan/v7y1952i1p77-91.html [25] R. C. Merton, “Lifetime portfolio selection under uncertainty: The continuous-time case,” The review of Economics and Statistics, pp. 247– 257, 1969. [26] S. Maillard, T. Roncalli, and J. Teïletche, “The properties of equally weighted risk contribution portfolios,” Journal of Portfolio Management, vol. 36, no. 4, p. 60, 2010. [27] A. Moghar and M. Hamiche, “Stock market prediction using lstm recur- rent neural network,” pp. 1168–1173, 2020. [28] J. Von Neumann and O. Morgenstern, “Theory of games and economic behavior, 2nd rev,” 1947. [29] M. Pericoli and M. Sbracia, “Crowded trades among hedge funds,” Banca d ’Italia working paper, 2010. [30] A. Rehman, A. K. Malik, B. Raza, and W. Ali, “A hybrid cnn-lstm model for improving accuracy of movie reviews sentiment analysis,” Multimedia Tools and Applications, vol. 78, pp. 26 597 – 26 613, 2019. 36 [31] M. Sauer, “Sector rotation through the business cycle: A machine learn- ing regime approach,” Econometric Modeling: Capital Markets - Fore- casting eJournal, 2019. [32] W. F. Sharpe, “Capital asset prices: A theory of market equilibrium under conditions of risk,” The journal of finance, vol. 19, no. 3, pp. 425–442, 1964. [33] D. Song, “Portfolio optimization by lstm with a selection of six stocks,” Advances in Economics, Management and Political Sciences, 2023. [34] W. Sharpe, “The sharpe ratio,” vol. 21, pp. 49 – 58, 1994. [35] Student, “The probable error of a mean,” Biometrika, vol. 6, no. 1, pp. 1–25, 1908. [Online]. Available: http://www.jstor.org/stable/2331554 [36] F. Zhang, Y. Ma, and S. Yu, “Investment model based on lstm network forecasting and portfolio investment,” Proceedings of the 2022 Interna- tional Conference on E-business and Mobile Commerce, 2022.zh_TW