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題名 再探投資組合建立的變數選取
Revisit the variable selection for portfolio construction
作者 黃群翔
Huang, Chiun-Shiang
貢獻者 林士貴
黃群翔
Huang, Chiun-Shiang
關鍵詞 投資組合構建
變數選取
財務管理
市場預測
資產配置
Portfolio construction
Variable selection
Financial management
Market prediction
Asset allocation
日期 2024
上傳時間 4-Feb-2025 16:19:47 (UTC+8)
摘要 投資組合構建是財務管理中的一個核心問題，隨著市場條件的不斷變化和金融工具的多樣化，如何選取合適的變數來優化投資組合成為了一個持續挑戰。有效的投資組合變數選取方法可以顯著提高投資績效，降低風險，並幫助投資者在複雜且動態的市場中做出明智的決策。本研究旨在回顧並重新探討投資組合構建中的變數選取問題。Aït-Sahalia & Brandt (2001)在其開創性的論文中提出了一種系統化的方法來選擇影響投資組合績效的關鍵變數。這些方法在當時提供了有效的理論框架和實證支持，對投資組合管理實務產生了深遠的影響。然而，隨著市場環境的變化和數據分析技術的進步，有必要重新評估這些方法在當前市場環境中的適用性和有效性。本文將通過實證分析和理論研究，探討2001年提出的變數選取方法是否仍然具有優越性，並考察是否有更優的新方法可供選擇。研究結果顯示，收益和收益共變異數在較長期限內更具可預測性。股票收益率比債券收益率具有更高的可預測性，而收益變異數在本研究中無顯著可預測性。平均-變異數投資者在無條件投資組合選擇中的風險部位於不同投資期限內皆相同，與風險趨避程度無關，驗證了兩基金分離理論。條件預測最佳投資組合的能力相較以往減弱，近年來的回報模式顯示出不同於1954-1997年期間的變化。這些結果初步揭示了現代市場環境中變數選取的挑戰和機會，為未來研究探索更有效的變數選取策略提供了基礎，以幫助投資者在複雜的市場環境中做出更明智的投資決策。
Portfolio construction is a core issue in financial management. With the continuous changes in market conditions and the diversification of financial instruments, selecting appropriate variables to optimize a portfolio has become a persistent challenge. Effective variable selection methods for portfolio construction can significantly improve investment performance, reduce risk, and help investors make informed decisions in complex and dynamic markets. This study aims to review and re-examine the issue of variable selection in portfolio construction. Aït-Sahalia & Brandt (2001) proposed a systematic method in their groundbreaking paper to select key variables that impact portfolio performance. These methods provided an effective theoretical framework and empirical support at the time, having a profound impact on portfolio management practice. However, with changes in the market environment and advancements in data analysis techniques, it is necessary to reassess the applicability and effectiveness of these methods in the current market environment. This paper will explore through empirical analysis and theoretical research whether the variable selection methods proposed in 2001 still possess superiority and whether there are better new methods available. The research results show that returns and return covariances are more predictable over longer horizons. Stock returns are more predictable than bond returns, while return variances are not significantly predictable in this study. Mean-variance investors have the same risk positions in unconditional portfolio choices across different investment horizons, regardless of risk aversion, confirming the two-fund separation theory. The ability to conditionally predict optimal portfolios has weakened compared to the past, and recent return patterns differ from those in the 1954-1997 period. These results initially reveal the challenges and opportunities in variable selection in the modern market environment, providing a foundation for future research to explore more effective variable selection strategies to help investors make wiser investment decisions in complex market environments.
參考文獻 Aït-Sahalia, Y., & Brandt, M. W. (2001). Variable selection for portfolio choice. Journal of Finance, 56(4), 1297-1351. Avramov, D. (1999). Stock return predictability and the implied equity premium. Journal of Financial Economics, 52(2), 277-306. Balduzzi, P., & Lynch, A. W. (1999). Transaction costs and predictability: Some utility cost calculations. Journal of Financial Economics, 52(1), 47-78. Barberis, N. (2000). Investing for the long run when returns are predictable. Journal of Finance, 55(1), 225-264. Bekaert, G., & Harvey, C. R. (2003). Emerging markets finance. Journal of Empirical Finance, 10(1-2), 3-55. Brandt, M. W. (1999). Estimating portfolio and consumption choice: A conditional euler equations approach. Journal of Finance, 54(5), 1609-1645. Brandt, M. W., & Santa-Clara, P. (2006). Dynamic portfolio selection by augmenting the asset space. Journal of Finance, 61(5), 2187-2217. Campbell, J. Y., & Viceira, L. M. (1999). Consumption and portfolio decisions when expected returns are time varying. Quarterly Journal of Economics, 114(2), 433-495. Campbell, J. Y., & Viceira, L. M. (2002). Strategic asset allocation: Portfolio choice for long-term investors. Oxford University Press. De Bondt, W. F. M., & Thaler, R. (1985). Does the stock market overreact? Journal of Finance, 40(3), 793-805. Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. Journal of Finance, 25(2), 383-417. Fama, E. F., & French, K. R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3-25. Fama, E. F., & French, K. R. (1989). Business conditions and expected returns on stocks and bonds. Journal of Financial Economics, 25(1), 23-49. Fan, J., & Li, R. (2006). Statistical challenges with high dimensionality: Feature selection in knowledge discovery. Proceedings of the International Congress of Mathematicians, 3, 595-622. Fung, W., & Hsieh, D. A. (2001). The risk in hedge fund strategies: Theory and evidence from trend followers. Review of Financial Studies, 14(2), 313-341. Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. Econometrica, 50(4), 1029-1054. Hodrick, R. J. (1992). Dividend yields and expected stock returns: Alternative procedures for inference and measurement. Review of Financial Studies, 5(3), 357-386. Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance, 48(1), 65-91. Kandel, S., & Stambaugh, R. F. (1996). On the predictability of stock returns: An asset allocation perspective. Journal of Finance, 51(2), 385-424. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291. Keim, D. B., & Stambaugh, R. F. (1986). Predicting returns in the stock and bond markets. Journal of Financial Economics, 17(2), 357-390. Kolanovic, M., & Krishnamachari, R. (2017). Big data and AI strategies. J.P. Morgan Global Quantitative and Derivatives Strategy Report. Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91. Ross, S. A. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13(3), 341-360. Schneeweis, T., & Spurgin, R. B. (1998). Multifactor models in managed futures, hedge fund and mutual fund return estimation. Journal of Alternative Investments, 1(2), 1-24. Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425-442. Shiller, R. J. (2003). From efficient markets theory to behavioral finance. Journal of Economic Perspectives, 17(1), 83-104.
描述 碩士
國立政治大學
金融學系
111352037
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111352037
資料類型 thesis
dc.contributor.advisor 林士貴zh_TW
dc.contributor.author (Authors) 黃群翔zh_TW
dc.contributor.author (Authors) Huang, Chiun-Shiangen_US
dc.creator (作者) 黃群翔zh_TW
dc.creator (作者) Huang, Chiun-Shiangen_US
dc.date (日期) 2024en_US
dc.date.accessioned 4-Feb-2025 16:19:47 (UTC+8)-
dc.date.available 4-Feb-2025 16:19:47 (UTC+8)-
dc.date.issued (上傳時間) 4-Feb-2025 16:19:47 (UTC+8)-
dc.identifier (Other Identifiers) G0111352037en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/155528-
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) 111352037zh_TW
dc.description.abstract (摘要) 投資組合構建是財務管理中的一個核心問題，隨著市場條件的不斷變化和金融工具的多樣化，如何選取合適的變數來優化投資組合成為了一個持續挑戰。有效的投資組合變數選取方法可以顯著提高投資績效，降低風險，並幫助投資者在複雜且動態的市場中做出明智的決策。本研究旨在回顧並重新探討投資組合構建中的變數選取問題。Aït-Sahalia & Brandt (2001)在其開創性的論文中提出了一種系統化的方法來選擇影響投資組合績效的關鍵變數。這些方法在當時提供了有效的理論框架和實證支持，對投資組合管理實務產生了深遠的影響。然而，隨著市場環境的變化和數據分析技術的進步，有必要重新評估這些方法在當前市場環境中的適用性和有效性。本文將通過實證分析和理論研究，探討2001年提出的變數選取方法是否仍然具有優越性，並考察是否有更優的新方法可供選擇。研究結果顯示，收益和收益共變異數在較長期限內更具可預測性。股票收益率比債券收益率具有更高的可預測性，而收益變異數在本研究中無顯著可預測性。平均-變異數投資者在無條件投資組合選擇中的風險部位於不同投資期限內皆相同，與風險趨避程度無關，驗證了兩基金分離理論。條件預測最佳投資組合的能力相較以往減弱，近年來的回報模式顯示出不同於1954-1997年期間的變化。這些結果初步揭示了現代市場環境中變數選取的挑戰和機會，為未來研究探索更有效的變數選取策略提供了基礎，以幫助投資者在複雜的市場環境中做出更明智的投資決策。zh_TW
dc.description.abstract (摘要) Portfolio construction is a core issue in financial management. With the continuous changes in market conditions and the diversification of financial instruments, selecting appropriate variables to optimize a portfolio has become a persistent challenge. Effective variable selection methods for portfolio construction can significantly improve investment performance, reduce risk, and help investors make informed decisions in complex and dynamic markets. This study aims to review and re-examine the issue of variable selection in portfolio construction. Aït-Sahalia & Brandt (2001) proposed a systematic method in their groundbreaking paper to select key variables that impact portfolio performance. These methods provided an effective theoretical framework and empirical support at the time, having a profound impact on portfolio management practice. However, with changes in the market environment and advancements in data analysis techniques, it is necessary to reassess the applicability and effectiveness of these methods in the current market environment. This paper will explore through empirical analysis and theoretical research whether the variable selection methods proposed in 2001 still possess superiority and whether there are better new methods available. The research results show that returns and return covariances are more predictable over longer horizons. Stock returns are more predictable than bond returns, while return variances are not significantly predictable in this study. Mean-variance investors have the same risk positions in unconditional portfolio choices across different investment horizons, regardless of risk aversion, confirming the two-fund separation theory. The ability to conditionally predict optimal portfolios has weakened compared to the past, and recent return patterns differ from those in the 1954-1997 period. These results initially reveal the challenges and opportunities in variable selection in the modern market environment, providing a foundation for future research to explore more effective variable selection strategies to help investors make wiser investment decisions in complex market environments.en_US
dc.description.tableofcontents 第一章 緒論 1 第一節 研究背景與研究動機 1 第二節 研究目的 3 一、研究目的 3 二、研究貢獻 4 第三節 研究架構 5 第二章 文獻回顧 6 第一節 投資組合理論的發展 6 一、現代投資組合理論 6 二、投資組合建立變數選取難題 7 第二節 投資組合變數選取的相關研究 7 一、重要預測變數的早期研究 7 二、Aït-Sahalia 和 Brandt (2001)的研究 8 三、近期的投資組合建立變數選取方法與應用 9 第三章 研究方法 11 第一節 預測個別動差 11 第二節 預測最佳投資組合權重 12 一、投資者的問題 (Investor's Problem) 13 二、條件投資組合選擇的指數 (Indices for the Conditional Portfolio Choice) 14 第四章 實證分析 18 第一節 資料描述 18 第二節 個別動差的可預測性(Individual Moment Predictability) 20 第三節 無條件投資組合選擇(Unconditional Portfolio Choice) 25 第四節 最優指數組合(Optimal Index Composition) 28 第五章 結論與建議 31 第一節 結論 31 第二節 未來展望 32 參考文獻 33zh_TW
dc.format.extent 1571687 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111352037en_US
dc.subject (關鍵詞) 投資組合構建zh_TW
dc.subject (關鍵詞) 變數選取zh_TW
dc.subject (關鍵詞) 財務管理zh_TW
dc.subject (關鍵詞) 市場預測zh_TW
dc.subject (關鍵詞) 資產配置zh_TW
dc.subject (關鍵詞) Portfolio constructionen_US
dc.subject (關鍵詞) Variable selectionen_US
dc.subject (關鍵詞) Financial managementen_US
dc.subject (關鍵詞) Market predictionen_US
dc.subject (關鍵詞) Asset allocationen_US
dc.title (題名) 再探投資組合建立的變數選取zh_TW
dc.title (題名) Revisit the variable selection for portfolio constructionen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Aït-Sahalia, Y., & Brandt, M. W. (2001). Variable selection for portfolio choice. Journal of Finance, 56(4), 1297-1351. Avramov, D. (1999). Stock return predictability and the implied equity premium. Journal of Financial Economics, 52(2), 277-306. Balduzzi, P., & Lynch, A. W. (1999). Transaction costs and predictability: Some utility cost calculations. Journal of Financial Economics, 52(1), 47-78. Barberis, N. (2000). Investing for the long run when returns are predictable. Journal of Finance, 55(1), 225-264. Bekaert, G., & Harvey, C. R. (2003). Emerging markets finance. Journal of Empirical Finance, 10(1-2), 3-55. Brandt, M. W. (1999). Estimating portfolio and consumption choice: A conditional euler equations approach. Journal of Finance, 54(5), 1609-1645. Brandt, M. W., & Santa-Clara, P. (2006). Dynamic portfolio selection by augmenting the asset space. Journal of Finance, 61(5), 2187-2217. Campbell, J. Y., & Viceira, L. M. (1999). Consumption and portfolio decisions when expected returns are time varying. Quarterly Journal of Economics, 114(2), 433-495. Campbell, J. Y., & Viceira, L. M. (2002). Strategic asset allocation: Portfolio choice for long-term investors. Oxford University Press. De Bondt, W. F. M., & Thaler, R. (1985). Does the stock market overreact? Journal of Finance, 40(3), 793-805. Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. Journal of Finance, 25(2), 383-417. Fama, E. F., & French, K. R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3-25. Fama, E. F., & French, K. R. (1989). Business conditions and expected returns on stocks and bonds. Journal of Financial Economics, 25(1), 23-49. Fan, J., & Li, R. (2006). Statistical challenges with high dimensionality: Feature selection in knowledge discovery. Proceedings of the International Congress of Mathematicians, 3, 595-622. Fung, W., & Hsieh, D. A. (2001). The risk in hedge fund strategies: Theory and evidence from trend followers. Review of Financial Studies, 14(2), 313-341. Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. Econometrica, 50(4), 1029-1054. Hodrick, R. J. (1992). Dividend yields and expected stock returns: Alternative procedures for inference and measurement. Review of Financial Studies, 5(3), 357-386. Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance, 48(1), 65-91. Kandel, S., & Stambaugh, R. F. (1996). On the predictability of stock returns: An asset allocation perspective. Journal of Finance, 51(2), 385-424. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291. Keim, D. B., & Stambaugh, R. F. (1986). Predicting returns in the stock and bond markets. Journal of Financial Economics, 17(2), 357-390. Kolanovic, M., & Krishnamachari, R. (2017). Big data and AI strategies. J.P. Morgan Global Quantitative and Derivatives Strategy Report. Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91. Ross, S. A. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13(3), 341-360. Schneeweis, T., & Spurgin, R. B. (1998). Multifactor models in managed futures, hedge fund and mutual fund return estimation. Journal of Alternative Investments, 1(2), 1-24. Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425-442. Shiller, R. J. (2003). From efficient markets theory to behavioral finance. Journal of Economic Perspectives, 17(1), 83-104.zh_TW