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題名 雙動能策略與權重平滑效果之應用
Application of Dual Momentum Strategy and Weight Smoothing Effect作者 李芷瑜
Li, Chih-Yu貢獻者 廖四郎
Liao, Szu-Lang
李芷瑜
Li, Chih-Yu關鍵詞 資產配置
雙動能投資策略
支援向量機
Black-Litterman模型
長時間短期記憶模型
動能效果
權重效果
Asset Allocation
Dual Momentum Strategy
SVM
Black-Litterman Model
LSTM
Momentum Effect
Weighted Effect日期 2022 上傳時間 1-Aug-2022 17:30:44 (UTC+8) 摘要 過去研究發現資產配置策略對投資組合的貢獻程度高達九成以上;而報酬預測是建構投資組合最核心的議題。本篇論文分析美國ETF市場資料,主要探討建構投資組合的策略及工具,創新動能投資策略並善用人工智慧科技的特性設計權重分配的規則,歸因四種投資組合的動能效果及權重效果,最後測試投資組合的穩健性及風險耐受性。戰略性資產配置(Strategic Asset Allocation)藉由動能的訊號產生的事件機率進行大類資產權重配置,能有效降低投資組合的整體風險,說明時間序列動能因子具有風險擇時的能力;戰術性資產配置(Tactical Asset Allocation)使用Black-Litterman模型結合長時間短期記憶神經網路轉換報酬分配來提高預測的準確度,其中長時間短期記憶神經網路預測準確率高達六成。研究結果發現,用以決定風險性資產權重的橫截面動能效果非常顯著,即便持有的資產屬於的投資組合類別(例如產業代表性ETF),仍有機會透過動能效果增加額外的報酬,其中規避突發風險性衝擊的效果則來自於風險性資產池中納入避險性資產,說明同時具報酬與風險擇時的能力。因此本文建議投資人可以動態方式調整股債的權重來規避風險的衝擊,並搭配橫截面動能策略追求最大化目標報酬。
There is evidence that asset allocation strategies contribute more than 90% to investment portfolios, and return prediction is the core issue in portfolio construction. We conducted a data analysis in US ETF market, and focused on portfolio construction strategies and methods, including innovating dual momentum strategies, designing the rules of weight allocation by using the characteristics of artificial intelligence technology, and attributing the momentum effect and weight effect of investment portfolios. Finally, Robustness test and t-student test were used for statistical analysis. Strategic Asset Allocation is weighted based on the probability of events generated by momentum signals, which can reduce the overall risk of the investment portfolio effectively. It shows that time series momentum factor has the ability to market timing. On the other hand, Black-Litterman model combined with LSTM in Tactical Asset Allocation can be used to transform the distribution of returns, and improve the accuracy of prediction. Among them, prediction accuracy of LSTM is about 60%. The empirical results show that cross-sectional momentum effect used to determine the weight of risky assets is very significant. Even though the assets belong to an investment portfolio category (such as ETFs), there is still an opportunity to increase excess returns through the momentum. The principal conclusion was that investors can avoid the impact of risks through allocating the weight of stocks and bonds dynamically, and maximize the target returns by cross-sectional momentum strategy.參考文獻 [1] Antonacci, G. (2011), “Optimal Momentum: A global cross asset approach,” Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1833722.[2]Antonacci, G. (2013), “Absolute momentum: A simple rule-based strategy and universal trend-following overlay,” Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2244633.[3] Antonacci, G. (2017), “Risk premia harvesting through dual momentum,” Journal of Management & Entrepreneurship, 2(1), 27-55.[4] Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013), “Value and momentum everywhere,” The Journal of Finance, 68(3), 929-985.[5] Bird, R., Gao, X., & Yeung, D. (2017), “Time-series and cross-sectional momentum strategies under alternative implementation strategies,” Australian Journal of Management, 42(2), 230-251.[6] Black, F., & Litterman, R. (1990), “Asset allocation: combining investor views with market equilibrium,” The Journal of Fixed Income, 1(2), 7-18.[7] Black, F., & Litterman, R. (1992), “Global portfolio optimization,” Financial Analysts Journal, 48(5), 28-43.[8] Breiman, L. (2001), “Statistical modeling: The two cultures (with comments and a rejoinder by the author),” Statistical Science, 16(3), 199-231.[9] Brinson, G. P., Hood, L. R., & Beebower, G. L. (1986), “Determinants of portfolio performance,” Financial Analysts Journal, 42(4), 39-44.[10] Chopra, V. K., & Ziemba, W. T. (2013), The effect of errors in means, variances, and covariances on optimal portfolio choice, In Handbook of the fundamentals of financial decision making: Part I (pp. 365-373).[11] Cortes, C., & Vapnik, V. (1995), “Support-vector networks,” Machine Learning, 20(3), 273-297.[12] Donthireddy, P. (2018), “Black-Litterman Portfolios with Machine Learning derived Views,” ResearchGate, Retrieved March 12, 2022, form https://www.researchgate.net/publication/326489143_Black-Litterman_Portfolios_with_Machine_Learning_derived_Views.[13] Eichhorn, D., Gupta, F., & Stubbs, E. (1998), “Using constraints to improve the robustness of asset allocation,” Journal of Portfolio Management, 24(3), 41-48.[14] Fama, E. F., & French, K. R. (2015), “A five-factor asset pricing model,” Journal of Financial Economics, 116(1), 1-22.[15] Frost, P. A., & Savarino, J. E. (1988), “For better performance: Constrain portfolio weights,” Journal of Portfolio Management, 15(1), 29-34.[16] Ha, S., & Fabozzi, F. J. (2022), “Dual Momentum: Testing the Dual Momentum Strategy and Implications for Lifetime Allocations,” The Journal of Portfolio Management, 48(4), 282-301.[17] Idzorek, T. (2007), A step-by-step guide to the Black-Litterman model: Incorporating user-specified confidence levels, In Forecasting expected returns in the financial markets (pp. 17-38). Academic Press.[18] Israel, R., Kelly, B. T., & Moskowitz, T. J. (2020), “Can Machines ` Learn` Finance?”, Journal of Investment Management, 18(2), 23-26.[19] Jegadeesh, N., & Titman, S. (1993), “Returns to buying winners and selling losers: Implications for stock market efficiency,” The Journal of finance, 48(1), 65-91.[20] Litterman, R., & He, G. (2002), “The intuition behind black-litterman model portfolios,” Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=334304.[21] Markowitz, H. (1952), “Portfolio selection,” The Journal of Finance, 7(1), 77-91.[22] Menkhoff, L., Sarno, L., Schmeling, M., & Schrimpf, A. (2012), “Currency momentum strategies,” Journal of Financial Economics, 106(3), 660-684.[23] Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012), “Time series momentum,” Journal of Financial Economics, 104(2), 228-250.[24] Scherer, B. (2002), “Portfolio resampling: Review and critique,” Financial Analysts Journal, 58(6), 98-109.[25] Sharpe, W. F. (1964), “Capital asset prices: A theory of market equilibrium under conditions of risk,” The Journal of Finance, 19(3), 425-442.[26] Sharpe, W. F. (1974). “Imputing expected security returns from portfolio composition,” Journal of Financial and Quantitative Analysis, 9(3), 463-472. 描述 碩士
國立政治大學
金融學系
109352030資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109352030 資料類型 thesis dc.contributor.advisor 廖四郎 zh_TW dc.contributor.advisor Liao, Szu-Lang en_US dc.contributor.author (Authors) 李芷瑜 zh_TW dc.contributor.author (Authors) Li, Chih-Yu en_US dc.creator (作者) 李芷瑜 zh_TW dc.creator (作者) Li, Chih-Yu en_US dc.date (日期) 2022 en_US dc.date.accessioned 1-Aug-2022 17:30:44 (UTC+8) - dc.date.available 1-Aug-2022 17:30:44 (UTC+8) - dc.date.issued (上傳時間) 1-Aug-2022 17:30:44 (UTC+8) - dc.identifier (Other Identifiers) G0109352030 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/141069 - 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) 109352030 zh_TW dc.description.abstract (摘要) 過去研究發現資產配置策略對投資組合的貢獻程度高達九成以上;而報酬預測是建構投資組合最核心的議題。本篇論文分析美國ETF市場資料,主要探討建構投資組合的策略及工具,創新動能投資策略並善用人工智慧科技的特性設計權重分配的規則,歸因四種投資組合的動能效果及權重效果,最後測試投資組合的穩健性及風險耐受性。戰略性資產配置(Strategic Asset Allocation)藉由動能的訊號產生的事件機率進行大類資產權重配置,能有效降低投資組合的整體風險,說明時間序列動能因子具有風險擇時的能力;戰術性資產配置(Tactical Asset Allocation)使用Black-Litterman模型結合長時間短期記憶神經網路轉換報酬分配來提高預測的準確度,其中長時間短期記憶神經網路預測準確率高達六成。研究結果發現,用以決定風險性資產權重的橫截面動能效果非常顯著,即便持有的資產屬於的投資組合類別(例如產業代表性ETF),仍有機會透過動能效果增加額外的報酬,其中規避突發風險性衝擊的效果則來自於風險性資產池中納入避險性資產,說明同時具報酬與風險擇時的能力。因此本文建議投資人可以動態方式調整股債的權重來規避風險的衝擊,並搭配橫截面動能策略追求最大化目標報酬。 zh_TW dc.description.abstract (摘要) There is evidence that asset allocation strategies contribute more than 90% to investment portfolios, and return prediction is the core issue in portfolio construction. We conducted a data analysis in US ETF market, and focused on portfolio construction strategies and methods, including innovating dual momentum strategies, designing the rules of weight allocation by using the characteristics of artificial intelligence technology, and attributing the momentum effect and weight effect of investment portfolios. Finally, Robustness test and t-student test were used for statistical analysis. Strategic Asset Allocation is weighted based on the probability of events generated by momentum signals, which can reduce the overall risk of the investment portfolio effectively. It shows that time series momentum factor has the ability to market timing. On the other hand, Black-Litterman model combined with LSTM in Tactical Asset Allocation can be used to transform the distribution of returns, and improve the accuracy of prediction. Among them, prediction accuracy of LSTM is about 60%. The empirical results show that cross-sectional momentum effect used to determine the weight of risky assets is very significant. Even though the assets belong to an investment portfolio category (such as ETFs), there is still an opportunity to increase excess returns through the momentum. The principal conclusion was that investors can avoid the impact of risks through allocating the weight of stocks and bonds dynamically, and maximize the target returns by cross-sectional momentum strategy. en_US dc.description.tableofcontents 第一章 緒論 1第一節 研究動機 1第二節 研究架構 2第二章 文獻探討 3第一節 動能因子投資策略 3第二節 現代投資組合理論 4第三章 研究方法 6第一節 研究流程 7第二節 資料來源及樣本敘述統計 8第三節 雙動能投資策略 9第四節 支援向量機分類模型 10第五節 Black-Litterman模型 12第六節 投資組合建構及績效衡量 19第四章 實證結果 23第一節 機器學習實證效果 23第二節 投資組合績效評估 27第三節 動能因子擇時效果 32第五章 結論 34參考文獻 35 zh_TW dc.format.extent 2032040 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109352030 en_US dc.subject (關鍵詞) 資產配置 zh_TW dc.subject (關鍵詞) 雙動能投資策略 zh_TW dc.subject (關鍵詞) 支援向量機 zh_TW dc.subject (關鍵詞) Black-Litterman模型 zh_TW dc.subject (關鍵詞) 長時間短期記憶模型 zh_TW dc.subject (關鍵詞) 動能效果 zh_TW dc.subject (關鍵詞) 權重效果 zh_TW dc.subject (關鍵詞) Asset Allocation en_US dc.subject (關鍵詞) Dual Momentum Strategy en_US dc.subject (關鍵詞) SVM en_US dc.subject (關鍵詞) Black-Litterman Model en_US dc.subject (關鍵詞) LSTM en_US dc.subject (關鍵詞) Momentum Effect en_US dc.subject (關鍵詞) Weighted Effect en_US dc.title (題名) 雙動能策略與權重平滑效果之應用 zh_TW dc.title (題名) Application of Dual Momentum Strategy and Weight Smoothing Effect en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] Antonacci, G. (2011), “Optimal Momentum: A global cross asset approach,” Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1833722.[2]Antonacci, G. (2013), “Absolute momentum: A simple rule-based strategy and universal trend-following overlay,” Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2244633.[3] Antonacci, G. (2017), “Risk premia harvesting through dual momentum,” Journal of Management & Entrepreneurship, 2(1), 27-55.[4] Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013), “Value and momentum everywhere,” The Journal of Finance, 68(3), 929-985.[5] Bird, R., Gao, X., & Yeung, D. (2017), “Time-series and cross-sectional momentum strategies under alternative implementation strategies,” Australian Journal of Management, 42(2), 230-251.[6] Black, F., & Litterman, R. (1990), “Asset allocation: combining investor views with market equilibrium,” The Journal of Fixed Income, 1(2), 7-18.[7] Black, F., & Litterman, R. (1992), “Global portfolio optimization,” Financial Analysts Journal, 48(5), 28-43.[8] Breiman, L. (2001), “Statistical modeling: The two cultures (with comments and a rejoinder by the author),” Statistical Science, 16(3), 199-231.[9] Brinson, G. P., Hood, L. R., & Beebower, G. L. (1986), “Determinants of portfolio performance,” Financial Analysts Journal, 42(4), 39-44.[10] Chopra, V. K., & Ziemba, W. T. (2013), The effect of errors in means, variances, and covariances on optimal portfolio choice, In Handbook of the fundamentals of financial decision making: Part I (pp. 365-373).[11] Cortes, C., & Vapnik, V. (1995), “Support-vector networks,” Machine Learning, 20(3), 273-297.[12] Donthireddy, P. (2018), “Black-Litterman Portfolios with Machine Learning derived Views,” ResearchGate, Retrieved March 12, 2022, form https://www.researchgate.net/publication/326489143_Black-Litterman_Portfolios_with_Machine_Learning_derived_Views.[13] Eichhorn, D., Gupta, F., & Stubbs, E. (1998), “Using constraints to improve the robustness of asset allocation,” Journal of Portfolio Management, 24(3), 41-48.[14] Fama, E. F., & French, K. R. (2015), “A five-factor asset pricing model,” Journal of Financial Economics, 116(1), 1-22.[15] Frost, P. A., & Savarino, J. E. (1988), “For better performance: Constrain portfolio weights,” Journal of Portfolio Management, 15(1), 29-34.[16] Ha, S., & Fabozzi, F. J. (2022), “Dual Momentum: Testing the Dual Momentum Strategy and Implications for Lifetime Allocations,” The Journal of Portfolio Management, 48(4), 282-301.[17] Idzorek, T. (2007), A step-by-step guide to the Black-Litterman model: Incorporating user-specified confidence levels, In Forecasting expected returns in the financial markets (pp. 17-38). Academic Press.[18] Israel, R., Kelly, B. T., & Moskowitz, T. J. (2020), “Can Machines ` Learn` Finance?”, Journal of Investment Management, 18(2), 23-26.[19] Jegadeesh, N., & Titman, S. (1993), “Returns to buying winners and selling losers: Implications for stock market efficiency,” The Journal of finance, 48(1), 65-91.[20] Litterman, R., & He, G. (2002), “The intuition behind black-litterman model portfolios,” Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=334304.[21] Markowitz, H. (1952), “Portfolio selection,” The Journal of Finance, 7(1), 77-91.[22] Menkhoff, L., Sarno, L., Schmeling, M., & Schrimpf, A. (2012), “Currency momentum strategies,” Journal of Financial Economics, 106(3), 660-684.[23] Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012), “Time series momentum,” Journal of Financial Economics, 104(2), 228-250.[24] Scherer, B. (2002), “Portfolio resampling: Review and critique,” Financial Analysts Journal, 58(6), 98-109.[25] Sharpe, W. F. (1964), “Capital asset prices: A theory of market equilibrium under conditions of risk,” The Journal of Finance, 19(3), 425-442.[26] Sharpe, W. F. (1974). “Imputing expected security returns from portfolio composition,” Journal of Financial and Quantitative Analysis, 9(3), 463-472. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202200883 en_US