1. NCCU Academic Hub
  2. Publications
  3. College of Commerce
  4. Theses
  5. Item

Publications-Theses

Article View/Open

Publication Export

Google ScholarTM

NCCU Library

Citation Infomation

Related Publications in TAIR

題名 更多限制或更佳估計？投資組合理論在台灣股票市場之應用
More Restrictions or Better Estimates? Application of Portfolio Theory in the Taiwan Stock Market
作者 鄭緒紳
Cheng, Hsu-Shen
貢獻者 鍾令德
鄭緒紳
Cheng, Hsu-Shen
關鍵詞 投資組合最佳化
權重約束
估計誤差
協方差估計
低波動異象
Portfolio optimization
Weight constraints
Estimation error
Covariance estimation
Low-volatility anomaly
日期 2025
上傳時間 1-Sep-2025 14:55:20 (UTC+8)
摘要 投資組合最佳化在實務中面臨估計誤差放大問題，導致極端的資產權重配 置與樣本外投資績效不佳。在台灣市場已有學術研究分別探討懲罰函數、收縮 估計、多因子模型等解決方法，然而尚無將權重約束與上述方法進行整合比較 來評估修正措施的相對效果。本文以台灣證券交易所 2000 年至 2025 年之掛牌 普通股為樣本，運用重複抽樣設計並結合雙重統計檢定，從中比較三種權重約 束條件與七種協方差估計方法組成不同全域最小變異數與最小追蹤誤差投資組 合的樣本外表現。實證結果顯示，採用樣本協方差並施行禁止放空約束能將全 域最小變異投資組合年化標準差從 17.57% 降至 13.23%，將最小追蹤誤差投資 組合追蹤誤差從 13.24% 降至 8.06%，單純禁止放空在風險控制效果上與收縮估 計及多因子模型等複雜方法達到統計等效水準。再進一步運用六因子模型迴歸 分析後，我們發現台灣市場存在低波動異象，全域最小變異投資組合呈現正向 超額報酬與低於一的市場風險曝險特徵，與風險最小化策略的優異表現一致。 本研究證實權重約束在台灣市場具備協方差估計修正功能，為投資組合風險管 理提供簡約且有效的實務方案。
Portfolio optimization is prone to estimation errors in practice, leading to extreme weight allocations and suboptimal out-of-sample performance. While existing studies in Taiwan have already considered penalty functions, shrinkage estimation, and factor models as potential solutions, it remains unclear how well these alternative approaches perform when incorporating practical weight constraints. This study utilizes data from the Taiwan Stock Exchange, which includes listed stocks spanning the period from 2000 to 2025. By utilizing a repeated random sampling design combined with dual statistical testing frameworks, we systematically compare three weight constraint specifications against seven covariance estimation methodologies. Our analysis examines the out-of- sample performance of global minimum variance and minimum tracking error portfolios across sample covariance, Ledoit-Wolf shrinkage estimation, and five different factor model specifications. Our empirical findings demonstrate that implementing no-short- selling constraints with sample covariance estimation reduces the annualized standard deviation of the global minimum variance portfolios from 17.57% to 13.23%, while simultaneously decreasing the minimum tracking error portfolios’ tracking error from 13.24% to 8.06%. This parsimonious constraint achieves risk control effectiveness com- parable to that of more sophisticated shrinkage and factor model approaches. Our six- factor model regression analysis confirms the presence of the low-volatility anomaly in Taiwan, with global minimum variance portfolios exhibiting positive abnormal returns and market beta coefficients significantly that are below one. This study indicates that weight constraints serve as effective covariance matrix correction mechanisms in Tai- wan, offering a practical yet simple solution to managing portfolio risk.
參考文獻 Baker, Malcolm, Brendan Bradley, and Jeffrey Wurgler, 2011, Benchmarks as limits to arbitrage: Understanding the low-volatility anomaly, Financial Analysts Journal 67, 40–54. Banz, Rolf W, 1981, The relationship between return and market value of common stocks, Journal of Financial Economics 9, 3–18. Blitz, David, and Pim van Vliet, 2007, The volatility effect: Lower risk without lower return, Journal of Portfolio Management 34, 102–113. Carhart, Mark M, 1997, On persistence in mutual fund performance, Journal of Finance 52, 57–82. Clarke, Roger G, Harindra de Silva, and Steven Thorley, 2006, Minimum-variance port- folios in the US equity market, Journal of Portfolio Management 33, 10–24. DeMiguel, Victor, Lorenzo Garlappi, and Raman Uppal, 2009, Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy?, Review of Financial Studies 22, 1915–1953. Fama, Eugene F, and Kenneth R French, 1992, The cross-section of expected stock returns, Journal of Finance 47, 427–465. Fama, Eugene F, and Kenneth R French, 1993, Common risk factors in the returns on stocks and bonds, Journal of Financial Economics 33, 3–56. Fama, Eugene F, and Kenneth R French, 2015, A five-factor asset pricing model, Jour- nal of Financial Economics 116, 1–22. Fama, Eugene F, and Kenneth R French, 2018, Choosing factors, Journal of Financial Economics 128, 234–252. Frahm, Gabriel, 2015, A theoretical foundation of portfolio resampling, Theory and Decision 79, 107–132. Green, Richard C, and Burton Hollifield, 1992, When will mean-variance efficient port- folios be well diversified?, Journal of Finance 47, 1785–1809. Jagannathan, Ravi, and Tongshu Ma, 2003, Risk reduction in large portfolios: Why imposing the wrong constraints helps, Journal of Finance 58, 1651–1683. Jegadeesh, Narasimhan, and Sheridan Titman, 1993, Returns to buying winners and selling losers: Implications for stock market efficiency, Journal of Finance 48, 65– 91. Ledoit, Olivier, and Michael Wolf, 2003, Improved estimation of the covariance ma- trix of stock returns with an application to portfolio selection, Journal of Empirical Finance 10, 603–621. Ledoit, Olivier, and Michael Wolf, 2004a, Honey, I shrunk the sample covariance ma- trix, Journal of Portfolio Management 30, 110–119. Ledoit, Olivier, and Michael Wolf, 2004b, A well-conditioned estimator for large- dimensional covariance matrices, Journal of Multivariate Analysis 88, 365–411. Ledoit, Olivier, and Michael Wolf, 2008, Robust performance hypothesis testing with the sharpe ratio, Journal of Empirical Finance 15, 850–859. Ledoit, Olivier, and Michael Wolf, 2011, Robust performances hypothesis testing with the variance, Wilmott 2011, 86–89. Lintner, John, 1965, Security Prices, Risk, and Maximal Gains From Diversification, Journal of Finance 20, 587–615. Markowitz, Harry, 1952, Portfolio selection, Journal of Finance 7, 77–91. Merton, Robert C, 1972, An analytic derivation of the efficient portfolio frontier, Jour-nal of Financial and Quantitative Analysis 7, 1851–1872. Michaud, Richard O, 1989, The Markowitz optimization enigma: Is “optimized” opti-mal?, Financial Analysts Journal 45, 31–42. Michaud, Richard O, and Robert Michaud, 2007, Estimation error and portfolio opti-mization: A resampling solution, Available at SSRN 2658657. Mossin, Jan, 1966, Equilibrium in a capital asset market, Econometrica 34, 768–783. Newey, Whitney K., and Kenneth D. West, 1987, A simple, positive semi-definite, het- eroskedasticity and autocorrelation consistent covariance matrix, Econometrica 55, 703–708. Newey, Whitney K., and Kenneth D. West, 1994, Automatic lag selection in covariance matrix estimation, Review of Economic Studies 61, 631–653. Scherer, Bernd, 2002, Portfolio resampling: Review and critique, Financial Analysts Journal 58, 98–109. Sharpe, William F., 1964, Capital asset prices: A theory of market equilibrium under conditions of risk, Journal of Finance 19, 425–442. 張芷涵, 2021, 最佳資產配置法與多因子模型探討:以台灣市場為例, 國立政治大 學金融學系碩士論文. 莊丹華, 2017, 加權範數最小變異數投資組合之實證應用:以台灣股市為例, 國立 政治大學國際經營與貿易學系碩士論文. 裘涵, 2017, 高維度平均-變異數最佳化之共變異數矩陣估計:以台灣資料為例, 國立臺灣大學統計碩士學位學程碩士論文.
描述 碩士
國立政治大學
國際經營與貿易學系
112351037
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112351037
資料類型 thesis
dc.contributor.advisor 鍾令德zh_TW
dc.contributor.author (Authors) 鄭緒紳zh_TW
dc.contributor.author (Authors) Cheng, Hsu-Shenen_US
dc.creator (作者) 鄭緒紳zh_TW
dc.creator (作者) Cheng, Hsu-Shenen_US
dc.date (日期) 2025en_US
dc.date.accessioned 1-Sep-2025 14:55:20 (UTC+8)-
dc.date.available 1-Sep-2025 14:55:20 (UTC+8)-
dc.date.issued (上傳時間) 1-Sep-2025 14:55:20 (UTC+8)-
dc.identifier (Other Identifiers) G0112351037en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/159061-
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) 112351037zh_TW
dc.description.abstract (摘要) 投資組合最佳化在實務中面臨估計誤差放大問題，導致極端的資產權重配 置與樣本外投資績效不佳。在台灣市場已有學術研究分別探討懲罰函數、收縮 估計、多因子模型等解決方法，然而尚無將權重約束與上述方法進行整合比較 來評估修正措施的相對效果。本文以台灣證券交易所 2000 年至 2025 年之掛牌 普通股為樣本，運用重複抽樣設計並結合雙重統計檢定，從中比較三種權重約 束條件與七種協方差估計方法組成不同全域最小變異數與最小追蹤誤差投資組 合的樣本外表現。實證結果顯示，採用樣本協方差並施行禁止放空約束能將全 域最小變異投資組合年化標準差從 17.57% 降至 13.23%，將最小追蹤誤差投資 組合追蹤誤差從 13.24% 降至 8.06%，單純禁止放空在風險控制效果上與收縮估 計及多因子模型等複雜方法達到統計等效水準。再進一步運用六因子模型迴歸 分析後，我們發現台灣市場存在低波動異象，全域最小變異投資組合呈現正向 超額報酬與低於一的市場風險曝險特徵，與風險最小化策略的優異表現一致。 本研究證實權重約束在台灣市場具備協方差估計修正功能，為投資組合風險管 理提供簡約且有效的實務方案。zh_TW
dc.description.abstract (摘要) Portfolio optimization is prone to estimation errors in practice, leading to extreme weight allocations and suboptimal out-of-sample performance. While existing studies in Taiwan have already considered penalty functions, shrinkage estimation, and factor models as potential solutions, it remains unclear how well these alternative approaches perform when incorporating practical weight constraints. This study utilizes data from the Taiwan Stock Exchange, which includes listed stocks spanning the period from 2000 to 2025. By utilizing a repeated random sampling design combined with dual statistical testing frameworks, we systematically compare three weight constraint specifications against seven covariance estimation methodologies. Our analysis examines the out-of- sample performance of global minimum variance and minimum tracking error portfolios across sample covariance, Ledoit-Wolf shrinkage estimation, and five different factor model specifications. Our empirical findings demonstrate that implementing no-short- selling constraints with sample covariance estimation reduces the annualized standard deviation of the global minimum variance portfolios from 17.57% to 13.23%, while simultaneously decreasing the minimum tracking error portfolios’ tracking error from 13.24% to 8.06%. This parsimonious constraint achieves risk control effectiveness com- parable to that of more sophisticated shrinkage and factor model approaches. Our six- factor model regression analysis confirms the presence of the low-volatility anomaly in Taiwan, with global minimum variance portfolios exhibiting positive abnormal returns and market beta coefficients significantly that are below one. This study indicates that weight constraints serve as effective covariance matrix correction mechanisms in Tai- wan, offering a practical yet simple solution to managing portfolio risk.en_US
dc.description.tableofcontents 中文摘要 i 英文摘要 ii 第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的 3 第三節 研究架構 3 第二章 文獻回顧與方法論 4 第一節 現代投資組合理論與估計誤差 4 第二節 降低估計誤差對投資組合影響之方法 6 第三節 低波動異常現象 13 第三章 研究資料與研究方法 14 第一節 資料來源 14 第二節 研究方法 14 第三節 實證流程 18 第四章 研究結果與分析 21 第一節 投資組合基本特徵分析 21 第二節 全域最小變異數投資組合之績效分析 27 第三節 最小追蹤誤差投資組合之績效分析 36 第四節 風險最小化策略之合理性 45 第五節 低波動異象之實證檢驗 48 第五章 結論與建議 51 第一節 結論 51 第二節 限制與建議 52 參考文獻 53zh_TW
dc.format.extent 2906670 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112351037en_US
dc.subject (關鍵詞) 投資組合最佳化zh_TW
dc.subject (關鍵詞) 權重約束zh_TW
dc.subject (關鍵詞) 估計誤差zh_TW
dc.subject (關鍵詞) 協方差估計zh_TW
dc.subject (關鍵詞) 低波動異象zh_TW
dc.subject (關鍵詞) Portfolio optimizationen_US
dc.subject (關鍵詞) Weight constraintsen_US
dc.subject (關鍵詞) Estimation erroren_US
dc.subject (關鍵詞) Covariance estimationen_US
dc.subject (關鍵詞) Low-volatility anomalyen_US
dc.title (題名) 更多限制或更佳估計？投資組合理論在台灣股票市場之應用zh_TW
dc.title (題名) More Restrictions or Better Estimates? Application of Portfolio Theory in the Taiwan Stock Marketen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Baker, Malcolm, Brendan Bradley, and Jeffrey Wurgler, 2011, Benchmarks as limits to arbitrage: Understanding the low-volatility anomaly, Financial Analysts Journal 67, 40–54. Banz, Rolf W, 1981, The relationship between return and market value of common stocks, Journal of Financial Economics 9, 3–18. Blitz, David, and Pim van Vliet, 2007, The volatility effect: Lower risk without lower return, Journal of Portfolio Management 34, 102–113. Carhart, Mark M, 1997, On persistence in mutual fund performance, Journal of Finance 52, 57–82. Clarke, Roger G, Harindra de Silva, and Steven Thorley, 2006, Minimum-variance port- folios in the US equity market, Journal of Portfolio Management 33, 10–24. DeMiguel, Victor, Lorenzo Garlappi, and Raman Uppal, 2009, Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy?, Review of Financial Studies 22, 1915–1953. Fama, Eugene F, and Kenneth R French, 1992, The cross-section of expected stock returns, Journal of Finance 47, 427–465. Fama, Eugene F, and Kenneth R French, 1993, Common risk factors in the returns on stocks and bonds, Journal of Financial Economics 33, 3–56. Fama, Eugene F, and Kenneth R French, 2015, A five-factor asset pricing model, Jour- nal of Financial Economics 116, 1–22. Fama, Eugene F, and Kenneth R French, 2018, Choosing factors, Journal of Financial Economics 128, 234–252. Frahm, Gabriel, 2015, A theoretical foundation of portfolio resampling, Theory and Decision 79, 107–132. Green, Richard C, and Burton Hollifield, 1992, When will mean-variance efficient port- folios be well diversified?, Journal of Finance 47, 1785–1809. Jagannathan, Ravi, and Tongshu Ma, 2003, Risk reduction in large portfolios: Why imposing the wrong constraints helps, Journal of Finance 58, 1651–1683. Jegadeesh, Narasimhan, and Sheridan Titman, 1993, Returns to buying winners and selling losers: Implications for stock market efficiency, Journal of Finance 48, 65– 91. Ledoit, Olivier, and Michael Wolf, 2003, Improved estimation of the covariance ma- trix of stock returns with an application to portfolio selection, Journal of Empirical Finance 10, 603–621. Ledoit, Olivier, and Michael Wolf, 2004a, Honey, I shrunk the sample covariance ma- trix, Journal of Portfolio Management 30, 110–119. Ledoit, Olivier, and Michael Wolf, 2004b, A well-conditioned estimator for large- dimensional covariance matrices, Journal of Multivariate Analysis 88, 365–411. Ledoit, Olivier, and Michael Wolf, 2008, Robust performance hypothesis testing with the sharpe ratio, Journal of Empirical Finance 15, 850–859. Ledoit, Olivier, and Michael Wolf, 2011, Robust performances hypothesis testing with the variance, Wilmott 2011, 86–89. Lintner, John, 1965, Security Prices, Risk, and Maximal Gains From Diversification, Journal of Finance 20, 587–615. Markowitz, Harry, 1952, Portfolio selection, Journal of Finance 7, 77–91. Merton, Robert C, 1972, An analytic derivation of the efficient portfolio frontier, Jour-nal of Financial and Quantitative Analysis 7, 1851–1872. Michaud, Richard O, 1989, The Markowitz optimization enigma: Is “optimized” opti-mal?, Financial Analysts Journal 45, 31–42. Michaud, Richard O, and Robert Michaud, 2007, Estimation error and portfolio opti-mization: A resampling solution, Available at SSRN 2658657. Mossin, Jan, 1966, Equilibrium in a capital asset market, Econometrica 34, 768–783. Newey, Whitney K., and Kenneth D. West, 1987, A simple, positive semi-definite, het- eroskedasticity and autocorrelation consistent covariance matrix, Econometrica 55, 703–708. Newey, Whitney K., and Kenneth D. West, 1994, Automatic lag selection in covariance matrix estimation, Review of Economic Studies 61, 631–653. Scherer, Bernd, 2002, Portfolio resampling: Review and critique, Financial Analysts Journal 58, 98–109. Sharpe, William F., 1964, Capital asset prices: A theory of market equilibrium under conditions of risk, Journal of Finance 19, 425–442. 張芷涵, 2021, 最佳資產配置法與多因子模型探討:以台灣市場為例, 國立政治大 學金融學系碩士論文. 莊丹華, 2017, 加權範數最小變異數投資組合之實證應用:以台灣股市為例, 國立 政治大學國際經營與貿易學系碩士論文. 裘涵, 2017, 高維度平均-變異數最佳化之共變異數矩陣估計:以台灣資料為例, 國立臺灣大學統計碩士學位學程碩士論文.zh_TW