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題名 運用因子投資與懲罰範數建構投資組合 : 以美國股票市場為例
Constructing portfolios with factor investment using Norm Penalty Functions: a case study of US stock markets作者 侯至玹
Hou, Jhih-Syuan貢獻者 顏佑銘
Yen, Yu-Min
侯至玹
Hou, Jhih-Syuan關鍵詞 因子投資
最小變異數投資組合
加權懲罰範數
Factor Investment
Minimum Variance Portfolio
Weighted-Norm Penalty日期 2021 上傳時間 1-Mar-2022 16:37:52 (UTC+8) 摘要 根據 Singer and Beebower (1991)的研究,資產配置策略對於投資組合的報酬 績效貢獻高達 90%,因此藉由不同之建構投資組合的方法尋找資產的最適權重 分配一直是投資人所關心之重要課題。在過往的資產配置中,股票和債券等資 產一直是多元化投資組合的主要基石,然而自從 2008 年美國次貸危機爆發後, 許多金融資產遭到投資者嚴重的拋售壓力,導致各類資產普遍下跌的現象,金 融資產之間的相關係數上升,學界開始研究是否可以使用因子類比金融資產作 為投資組合的建構,降低投資組合內標的間的相關性。本文研究亦從此出發,從權威性金融期刊中挑選出因子,使用懲罰函數結合 平均數-變異數投資組合法,形成加權範數最小變異數投資組合;並運用十個績效指標來衡量加權範數最小變異數投資組合與其他三種標竿投資組合的表現
According to the research of Singer and Beebower (1991), asset allocation strategy contributes about 90% to the return performance of the investment portfolio. Therefore, finding the optimal weight distribution of assets through different methods of constructing investment portfolios has always been an important issue for investors. In the past asset allocation, stocks and bonds have always been the main points of diversified investment portfolios. However, since the outbreak of the U.S. subprime mortgage crisis in 2008, many financial assets have been under severe selling pressure from investors. With the phenomenon that assets are generally falling, and the correlation between financial assets has risen, researching whether factors can be used as financial assets as the construction of investment portfolios to reduce the correlation between investment portfolio internal assets.The essay selected factors from authoritative financial journals, using norm penalty function combined with mean-variance portfolio method to form the Weighted-Norm Minimum Variance Portfolio (WNMVP) portfolio, then using ten performance indicators to measure the performance of Weighted-Norm Minimum Variance Portfolio and the other three benchmark portfolios.參考文獻 1. 李振婷(2015)。最小變異數投資組合在台灣股市之運用。未出版之碩士 論文, 國立政治大學,國際經營與貿易學系,碩士論文。2. 吳孟臻(2009)。投資人情緒、動能與公司治理對股價的影響。國立政治 大學,財務管理學系,碩士論文。3. 洪茂蔚、林宜勉、劉志諒(2007)。「動能投資策略之獲利性與影響因 素」,中山管理評論,第 15 卷第 3 期,第 515-546 頁。4. 劉修銘(2018)。因子投資在資產配置上的應用。國立交通大學,資訊管 理與財務金融學系,碩士論文,第 3-15 頁。5. 劉清標、吳佩紋、林筱鳳(2019)。企業創新效率之六因子資產定價模 型。商管科技季刊第二十卷第一期,第 69-79 頁。6. 賀蘭芝(2015),《Risk Parity 投資組合配置分析》,JP Morgan 資產管理 公司「2015 計量模型投資訓練課程」,行政院所屬各機關因公出國人員 出國報告書。第 4-9 頁。7. 顏佑銘(2015)。資產數目過大時,投資人該如何建構投資組合策略,政 大商業評論,第 2015 期。8. Black, F., & Litterman, R. (1992). Global portfolio optimization. Financial analysts journal, 48(5), 28-43.9. Benartzi, S., & Thaler, R. H. (2001). Naive diversification strategies in defined contribution saving plans. American economic review, 91(1), 79-98.10. Brinson, G. P., Singer, B. D., & Beebower, G. L. (1991). Determinants of portfolio performance II: An update. Financial Analysts Journal, 47(3), 40-48.11. Chaves, D., Hsu, J., Li, F., & Shakernia, O. (2011). Risk parity portfolio vs. other asset allocation heuristic portfolios. The Journal of Investing, 20(1), 108- 118.12. Chen, A. Y., & Zimmermann, T. (2020). Publication bias and the cross-section of stock returns. The Review of Asset Pricing Studies, 10(2), 249-289.13. Chow, T. M., Hsu, J., Kalesnik, V., & Little, B. (2011). A survey of alternative equity index strategies. Financial Analysts Journal, 67(5), 37-57.14. Clarke, R. G., de Silva, H., & Murdock, R. (2005). A factor approach to asset allocation. The Journal of Portfolio Management, 32(1), 10-21.15. De Bondt, W. F., & Thaler, R. (1985). Does the stock market overreact?. The Journal of finance, 40(3), 793-805.16. De Bondt, W. F., & Thaler, R. H. (1987). Further evidence on investor overreaction and stock market seasonality. The Journal of finance, 42(3), 557- 581.17. DeMiguel, V., Garlappi, L., & Uppal, R. (2009). Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy?. The review of Financial studies, 22(5), 1915-1953.18. Fama, E.F. and K.R. French(1993),Common Risk Factors in the Returns on Stocks and Bonds, Journal of Financial Economics 33,3-56.19. 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. Kenneth French data library. Retrieved March 13 2020, from:https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html21. Markowitz, H. (1952) Portfolio Selection, The Journal of Finance, 7, 77–91.22. Merton, R. C. (1980). On estimating the expected return on the market: An exploratory investigation. Journal of financial economics, 8(4), 323-361.23. Michaud, R. O. (1989). The Markowitz optimization enigma: Is ‘optimized’optimal?. Financial analysts journal, 45(1), 31-42.24. Page, S., & Taborsky, M. A. (2011). Invited Editorial Comment: The Myth of Diversification: Risk Factors versus Asset Classes.25. Tobin, J. (1958). Liquidity preference as behavior towards risk. The review of economic studies, 25(2), 65-86.26. Windcliff, H., & Boyle, P. P. (2004). The 1/n pension investment puzzle. North American Actuarial Journal, 8(3), 32-45.27. Yen, Y. M., & Yen, T. J. (2014). Solving norm constrained portfolio optimization via coordinate-wise descent algorithms. Computational Statistics & Data Analysis, 76, 737-759.28. Yen, Y. M. (2015). Sparse weighted-norm minimum variance portfolios. Review of Finance, 20(3), 1259-1287. 描述 碩士
國立政治大學
國際經營與貿易學系
108351040資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108351040 資料類型 thesis dc.contributor.advisor 顏佑銘 zh_TW dc.contributor.advisor Yen, Yu-Min en_US dc.contributor.author (Authors) 侯至玹 zh_TW dc.contributor.author (Authors) Hou, Jhih-Syuan en_US dc.creator (作者) 侯至玹 zh_TW dc.creator (作者) Hou, Jhih-Syuan en_US dc.date (日期) 2021 en_US dc.date.accessioned 1-Mar-2022 16:37:52 (UTC+8) - dc.date.available 1-Mar-2022 16:37:52 (UTC+8) - dc.date.issued (上傳時間) 1-Mar-2022 16:37:52 (UTC+8) - dc.identifier (Other Identifiers) G0108351040 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/139138 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 國際經營與貿易學系 zh_TW dc.description (描述) 108351040 zh_TW dc.description.abstract (摘要) 根據 Singer and Beebower (1991)的研究,資產配置策略對於投資組合的報酬 績效貢獻高達 90%,因此藉由不同之建構投資組合的方法尋找資產的最適權重 分配一直是投資人所關心之重要課題。在過往的資產配置中,股票和債券等資 產一直是多元化投資組合的主要基石,然而自從 2008 年美國次貸危機爆發後, 許多金融資產遭到投資者嚴重的拋售壓力,導致各類資產普遍下跌的現象,金 融資產之間的相關係數上升,學界開始研究是否可以使用因子類比金融資產作 為投資組合的建構,降低投資組合內標的間的相關性。本文研究亦從此出發,從權威性金融期刊中挑選出因子,使用懲罰函數結合 平均數-變異數投資組合法,形成加權範數最小變異數投資組合;並運用十個績效指標來衡量加權範數最小變異數投資組合與其他三種標竿投資組合的表現 zh_TW dc.description.abstract (摘要) According to the research of Singer and Beebower (1991), asset allocation strategy contributes about 90% to the return performance of the investment portfolio. Therefore, finding the optimal weight distribution of assets through different methods of constructing investment portfolios has always been an important issue for investors. In the past asset allocation, stocks and bonds have always been the main points of diversified investment portfolios. However, since the outbreak of the U.S. subprime mortgage crisis in 2008, many financial assets have been under severe selling pressure from investors. With the phenomenon that assets are generally falling, and the correlation between financial assets has risen, researching whether factors can be used as financial assets as the construction of investment portfolios to reduce the correlation between investment portfolio internal assets.The essay selected factors from authoritative financial journals, using norm penalty function combined with mean-variance portfolio method to form the Weighted-Norm Minimum Variance Portfolio (WNMVP) portfolio, then using ten performance indicators to measure the performance of Weighted-Norm Minimum Variance Portfolio and the other three benchmark portfolios. en_US dc.description.tableofcontents 第一章 緒論 1第一節 研究動機 1第二節 研究目的 2第三節 研究架構 3第二章 文獻探討 4第一節 因子投資理論回顧 4第二節 資產配置理論回顧 6第三章 研究方法 8第一節 因子的篩選 8第二節 加權範數最小變異數投資組合 11第三節 替代懲罰範數 14第四章 實證資料 16第一節 樣本的資料與描述 16第二節 績效評估方法 17第三節 實證結果與分析 20第四節 加入限制報酬條件 23第五節 替代懲罰範數之表現 25第五章 結論與建議 29第一節 研究結論與建議 29參考文獻 32 zh_TW dc.format.extent 1066285 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108351040 en_US dc.subject (關鍵詞) 因子投資 zh_TW dc.subject (關鍵詞) 最小變異數投資組合 zh_TW dc.subject (關鍵詞) 加權懲罰範數 zh_TW dc.subject (關鍵詞) Factor Investment en_US dc.subject (關鍵詞) Minimum Variance Portfolio en_US dc.subject (關鍵詞) Weighted-Norm Penalty en_US dc.title (題名) 運用因子投資與懲罰範數建構投資組合 : 以美國股票市場為例 zh_TW dc.title (題名) Constructing portfolios with factor investment using Norm Penalty Functions: a case study of US stock markets en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 1. 李振婷(2015)。最小變異數投資組合在台灣股市之運用。未出版之碩士 論文, 國立政治大學,國際經營與貿易學系,碩士論文。2. 吳孟臻(2009)。投資人情緒、動能與公司治理對股價的影響。國立政治 大學,財務管理學系,碩士論文。3. 洪茂蔚、林宜勉、劉志諒(2007)。「動能投資策略之獲利性與影響因 素」,中山管理評論,第 15 卷第 3 期,第 515-546 頁。4. 劉修銘(2018)。因子投資在資產配置上的應用。國立交通大學,資訊管 理與財務金融學系,碩士論文,第 3-15 頁。5. 劉清標、吳佩紋、林筱鳳(2019)。企業創新效率之六因子資產定價模 型。商管科技季刊第二十卷第一期,第 69-79 頁。6. 賀蘭芝(2015),《Risk Parity 投資組合配置分析》,JP Morgan 資產管理 公司「2015 計量模型投資訓練課程」,行政院所屬各機關因公出國人員 出國報告書。第 4-9 頁。7. 顏佑銘(2015)。資產數目過大時,投資人該如何建構投資組合策略,政 大商業評論,第 2015 期。8. Black, F., & Litterman, R. (1992). Global portfolio optimization. Financial analysts journal, 48(5), 28-43.9. Benartzi, S., & Thaler, R. H. (2001). Naive diversification strategies in defined contribution saving plans. American economic review, 91(1), 79-98.10. Brinson, G. P., Singer, B. D., & Beebower, G. L. (1991). Determinants of portfolio performance II: An update. Financial Analysts Journal, 47(3), 40-48.11. Chaves, D., Hsu, J., Li, F., & Shakernia, O. (2011). Risk parity portfolio vs. other asset allocation heuristic portfolios. The Journal of Investing, 20(1), 108- 118.12. Chen, A. Y., & Zimmermann, T. (2020). Publication bias and the cross-section of stock returns. The Review of Asset Pricing Studies, 10(2), 249-289.13. Chow, T. M., Hsu, J., Kalesnik, V., & Little, B. (2011). A survey of alternative equity index strategies. Financial Analysts Journal, 67(5), 37-57.14. Clarke, R. G., de Silva, H., & Murdock, R. (2005). A factor approach to asset allocation. The Journal of Portfolio Management, 32(1), 10-21.15. De Bondt, W. F., & Thaler, R. (1985). Does the stock market overreact?. The Journal of finance, 40(3), 793-805.16. De Bondt, W. F., & Thaler, R. H. (1987). Further evidence on investor overreaction and stock market seasonality. The Journal of finance, 42(3), 557- 581.17. DeMiguel, V., Garlappi, L., & Uppal, R. (2009). Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy?. The review of Financial studies, 22(5), 1915-1953.18. Fama, E.F. and K.R. French(1993),Common Risk Factors in the Returns on Stocks and Bonds, Journal of Financial Economics 33,3-56.19. 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. Kenneth French data library. Retrieved March 13 2020, from:https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html21. Markowitz, H. (1952) Portfolio Selection, The Journal of Finance, 7, 77–91.22. Merton, R. C. (1980). On estimating the expected return on the market: An exploratory investigation. Journal of financial economics, 8(4), 323-361.23. Michaud, R. O. (1989). The Markowitz optimization enigma: Is ‘optimized’optimal?. Financial analysts journal, 45(1), 31-42.24. Page, S., & Taborsky, M. A. (2011). Invited Editorial Comment: The Myth of Diversification: Risk Factors versus Asset Classes.25. Tobin, J. (1958). Liquidity preference as behavior towards risk. The review of economic studies, 25(2), 65-86.26. Windcliff, H., & Boyle, P. P. (2004). The 1/n pension investment puzzle. North American Actuarial Journal, 8(3), 32-45.27. Yen, Y. M., & Yen, T. J. (2014). Solving norm constrained portfolio optimization via coordinate-wise descent algorithms. Computational Statistics & Data Analysis, 76, 737-759.28. Yen, Y. M. (2015). Sparse weighted-norm minimum variance portfolios. Review of Finance, 20(3), 1259-1287. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202200219 en_US