1. NCCU Academic Hub
  2. 學術產出
  3. 商學院
  4. 學位論文
  5. Item

學術產出-學位論文

文章檢視/開啟

書目匯出

Google ScholarTM

政大圖書館

引文資訊

TAIR相關學術產出

題名 基於風險溢酬主成分分析建構效率投資組合：臺灣股市實證
Constructing efficient portfolios based on risk-premium principal component analysis: evidence from Taiwan stock market
作者 蔡圭峯
Tsai, Kuei-Feng
貢獻者 林靖庭<br>羅秉政
Lin, Ching-Ting<br>Kendro Vincent
蔡圭峯
Tsai, Kuei-Feng
關鍵詞 風險溢酬主成分分析
統計因子模型
效率投資組合
Risk premium principal component analysis
Statistical factor models
Efficient portfolios
日期 2025
上傳時間 1-七月-2025 15:16:35 (UTC+8)
摘要 本研究旨在探討風險溢酬主成分分析 (Risk-Premium Principal Component Analysis, RP-PCA) 與凸非負矩陣分解 (Convex Non-negative Matrix Factorization, convex-NMF) 在臺灣股市建構效率投資組合之應用。有別於傳統特徵因子模型，統計因子模型透過直接分析資產報酬的共變異結構，無需預先設定特定公司特徵即可找出潛在的系統性風險因素。本研究以2007年至2023年臺灣上市公司週資料為樣本，系統性比較不同參數設定下的統計因子表現。研究結果顯示，RP-PCA在加入橫斷面定價誤差權重後，其提取的因子具有顯著較高的夏普比率，特別是在較短的滾動視窗下表現最佳。權重調整策略方面，放空限制與130/30策略在樣本外測試中顯著提升投資組合績效，而波動度標準化效果則不一致。市場特徵因子迴歸分析顯示，RP-PCA因子對動能因子有顯著正向關係，對短期反轉因子呈負向關係，展現捕捉不同時間尺度價格動態的能力。研究結果進一步表明，使用三至五個統計因子已能有效捕捉主要系統性風險來源，過度增加因子數量可能導致過度擬合問題而降低策略效率。本研究也顯示統計因子模型與傳統市場特徵因子模型具互補關係，統計方法能有效整合現有風險來源，提供更穩健的投資組合建構策略。
This study investigates the application of Risk-Premium Principal Component Analysis (RP-PCA) and Convex Non-negative Matrix Factorization (convex-NMF) in constructing efficient portfolios in the Taiwan stock market. Unlike traditional characteristic factor models, statistical factor models identify potential systematic risk factors by directly analyzing the covariance structure of asset returns without predetermining specific company characteristics. Using weekly data from Taiwan listed companies from 2007 to 2023, this research systematically compares the performance of statistical factors under different parameter settings. Results indicate that RP-PCA, after incorporating cross-sectional pricing error weights, extracts factors with significantly higher Sharpe ratios, with optimal performance observed particularly in shorter rolling windows. Regarding weight adjustment strategies, both long-only constraints and 130/30 strategies significantly enhance portfolio performance in out-of-sample tests, while volatility normalization shows inconsistent effects. Market characteristic factor regression analysis reveals that RP-PCA factors exhibit significant positive relationships with momentum factors and negative relationships with short-term reversal factors, demonstrating the ability to capture price dynamics across different time scales. Further findings suggest that three to five statistical factors can effectively capture major systematic risk sources, while excessive factor inclusion may lead to overfitting problems that reduce strategy efficiency. This research confirms the complementary relationship between statistical factor models and traditional market characteristic factor models, demonstrating that statistical methods can effectively integrate existing risk sources to provide more robust portfolio construction strategies.
參考文獻 Bai, J., & Ng, S. (2002). Determining the number of factors in approximate factor models. Econometrica, 70(1), 191-221. Baker, M., & Stein, J. C. (2004). Market liquidity as a sentiment indicator. Journal of Financial Markets, 7(3), 271-299. Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52 (1), 57-82. Chamberlain, G., & Rothschild, M. (1983). Arbitrage, factor structure, and mean-variance analysis on large asset markets. Econometrica, 51(5), 1281-1304. Chen, L., Pelger, M., & Zhu, J. (2024). Deep learning in asset pricing. Management Science, 70(2), 714-750. Chordia, T., & Swaminathan, B. (2000). Trading volume and cross-autocorrelations in stock returns. The Journal of Finance, 55(2), 913-935. Connor, G., & Korajczyk, R. A. (1986). Performance measurement with the arbitrage pricing theory: A new framework for analysis. Journal of Financial Economics, 15(3), 373-394. Connor, G., & Korajczyk, R. A. (1988). Risk and return in an equilibrium apt: Application of a new test methodology. Journal of Financial Economics, 21(2), 255-289. De Bondt, W. F. M., & Thaler, R. (1985). Does the Stock Market Overreact? The Journal of Finance, 40(3), 793-805. 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. Ding, C. H., Li, T., & Jordan, M. I. (2010). Convex and semi-nonnegative matrix factorizations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(1), 45-55. Fama, E. F., & French, K. R. (1993). Common risk factors in the return on stocks and bonds. Journal of Financial Economics, 33(1), 3-56. Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1-22. Gervais, S., Kaniel, R., & Mingelgrin, D. H. (2001). The high-volume return premium. The Journal of Finance, 56(3), 877-919. Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273. Jagannathan, R., & Ma, T. (2003). Risk reduction in large portfolios: Why imposing the wrong constraints helps. The Journal of Finance, 58(4), 1651-1683. Jegadeesh, N. (1990). Evidence of predictable behavior of security returns. The Journal of Finance, 45(3), 881-898. Kelly, B. T., Pruitt, S., & Su, Y. (2019). Characteristics are covariances: a unified model of risk and return. Journal of Financial Economics, 134(3), 501-524. Ledoit, O., & Wolf, M. (2004). Honey, I shrunk the sample covariance matrix. The Journal of Portfolio Management, 30(4), 110-119. Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788-791. Lettau, M., & Pelger, M. (2020). Factors that fit the time series and cross-section of stock returns. The Review of Financial Studies, 33(5), 2274-2325. Lintner, J. (1965). The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. Review of Economics and Statistics, 47(1), 13-37. Lo, A. W., & Patel, P. N. (2008). 130/30: The new long-only. The Journal of Portfolio Management, 34(2), 12-38. Maillard, S., Roncalli, T., & Teiletche, J. (2010). The properties of equally weighted risk contribution portfolios. The Journal of Portfolio Management, 36(4), 60-70. Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica, 34(4), 768-783. Roll, R., & Ross, S. A. (1980). An empirical investigation of the arbitrage pricing theory. The Journal of Finance, 35(5), 1073-1103. Ross, S. A. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13(3), 341-360. Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425-442. Spilak, B., & Härdle, W. K. (2022). Risk budget portfolios with convex non-negative matrix factorization. arXiv preprint arXiv:2204.02757. Stock, J. H., & Watson, M. W. (2002). Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association, 97(460), 1167-1179.
描述 碩士
國立政治大學
金融學系
112352013
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112352013
資料類型 thesis
dc.contributor.advisor 林靖庭<br>羅秉政zh_TW
dc.contributor.advisor Lin, Ching-Ting<br>Kendro Vincenten_US
dc.contributor.author (作者) 蔡圭峯zh_TW
dc.contributor.author (作者) Tsai, Kuei-Fengen_US
dc.creator (作者) 蔡圭峯zh_TW
dc.creator (作者) Tsai, Kuei-Fengen_US
dc.date (日期) 2025en_US
dc.date.accessioned 1-七月-2025 15:16:35 (UTC+8)-
dc.date.available 1-七月-2025 15:16:35 (UTC+8)-
dc.date.issued (上傳時間) 1-七月-2025 15:16:35 (UTC+8)-
dc.identifier (其他 識別碼) G0112352013en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/157831-
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) 112352013zh_TW
dc.description.abstract (摘要) 本研究旨在探討風險溢酬主成分分析 (Risk-Premium Principal Component Analysis, RP-PCA) 與凸非負矩陣分解 (Convex Non-negative Matrix Factorization, convex-NMF) 在臺灣股市建構效率投資組合之應用。有別於傳統特徵因子模型，統計因子模型透過直接分析資產報酬的共變異結構，無需預先設定特定公司特徵即可找出潛在的系統性風險因素。本研究以2007年至2023年臺灣上市公司週資料為樣本，系統性比較不同參數設定下的統計因子表現。研究結果顯示，RP-PCA在加入橫斷面定價誤差權重後，其提取的因子具有顯著較高的夏普比率，特別是在較短的滾動視窗下表現最佳。權重調整策略方面，放空限制與130/30策略在樣本外測試中顯著提升投資組合績效，而波動度標準化效果則不一致。市場特徵因子迴歸分析顯示，RP-PCA因子對動能因子有顯著正向關係，對短期反轉因子呈負向關係，展現捕捉不同時間尺度價格動態的能力。研究結果進一步表明，使用三至五個統計因子已能有效捕捉主要系統性風險來源，過度增加因子數量可能導致過度擬合問題而降低策略效率。本研究也顯示統計因子模型與傳統市場特徵因子模型具互補關係，統計方法能有效整合現有風險來源，提供更穩健的投資組合建構策略。zh_TW
dc.description.abstract (摘要) This study investigates the application of Risk-Premium Principal Component Analysis (RP-PCA) and Convex Non-negative Matrix Factorization (convex-NMF) in constructing efficient portfolios in the Taiwan stock market. Unlike traditional characteristic factor models, statistical factor models identify potential systematic risk factors by directly analyzing the covariance structure of asset returns without predetermining specific company characteristics. Using weekly data from Taiwan listed companies from 2007 to 2023, this research systematically compares the performance of statistical factors under different parameter settings. Results indicate that RP-PCA, after incorporating cross-sectional pricing error weights, extracts factors with significantly higher Sharpe ratios, with optimal performance observed particularly in shorter rolling windows. Regarding weight adjustment strategies, both long-only constraints and 130/30 strategies significantly enhance portfolio performance in out-of-sample tests, while volatility normalization shows inconsistent effects. Market characteristic factor regression analysis reveals that RP-PCA factors exhibit significant positive relationships with momentum factors and negative relationships with short-term reversal factors, demonstrating the ability to capture price dynamics across different time scales. Further findings suggest that three to five statistical factors can effectively capture major systematic risk sources, while excessive factor inclusion may lead to overfitting problems that reduce strategy efficiency. This research confirms the complementary relationship between statistical factor models and traditional market characteristic factor models, demonstrating that statistical methods can effectively integrate existing risk sources to provide more robust portfolio construction strategies.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 第二章　文獻回顧　3 第三章　研究方法　7 第一節　傳統主成分分析　7 第二節　風險溢酬主成分分析　9 第三節　凸非負矩陣分解　10 第四節　因子負荷量調整　12 第五節　研究方法與資料　15 第四章　實證結果　25 第一節　統計因子樣本外報酬分析　26 第二節　建構統計因子之最大化夏普比率投資組合　36 第五章　結論　39 參考文獻　42 附錄　45zh_TW
dc.format.extent 4197296 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112352013en_US
dc.subject (關鍵詞) 風險溢酬主成分分析zh_TW
dc.subject (關鍵詞) 統計因子模型zh_TW
dc.subject (關鍵詞) 效率投資組合zh_TW
dc.subject (關鍵詞) Risk premium principal component analysisen_US
dc.subject (關鍵詞) Statistical factor modelsen_US
dc.subject (關鍵詞) Efficient portfoliosen_US
dc.title (題名) 基於風險溢酬主成分分析建構效率投資組合：臺灣股市實證zh_TW
dc.title (題名) Constructing efficient portfolios based on risk-premium principal component analysis: evidence from Taiwan stock marketen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Bai, J., & Ng, S. (2002). Determining the number of factors in approximate factor models. Econometrica, 70(1), 191-221. Baker, M., & Stein, J. C. (2004). Market liquidity as a sentiment indicator. Journal of Financial Markets, 7(3), 271-299. Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52 (1), 57-82. Chamberlain, G., & Rothschild, M. (1983). Arbitrage, factor structure, and mean-variance analysis on large asset markets. Econometrica, 51(5), 1281-1304. Chen, L., Pelger, M., & Zhu, J. (2024). Deep learning in asset pricing. Management Science, 70(2), 714-750. Chordia, T., & Swaminathan, B. (2000). Trading volume and cross-autocorrelations in stock returns. The Journal of Finance, 55(2), 913-935. Connor, G., & Korajczyk, R. A. (1986). Performance measurement with the arbitrage pricing theory: A new framework for analysis. Journal of Financial Economics, 15(3), 373-394. Connor, G., & Korajczyk, R. A. (1988). Risk and return in an equilibrium apt: Application of a new test methodology. Journal of Financial Economics, 21(2), 255-289. De Bondt, W. F. M., & Thaler, R. (1985). Does the Stock Market Overreact? The Journal of Finance, 40(3), 793-805. 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. Ding, C. H., Li, T., & Jordan, M. I. (2010). Convex and semi-nonnegative matrix factorizations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(1), 45-55. Fama, E. F., & French, K. R. (1993). Common risk factors in the return on stocks and bonds. Journal of Financial Economics, 33(1), 3-56. Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1-22. Gervais, S., Kaniel, R., & Mingelgrin, D. H. (2001). The high-volume return premium. The Journal of Finance, 56(3), 877-919. Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273. Jagannathan, R., & Ma, T. (2003). Risk reduction in large portfolios: Why imposing the wrong constraints helps. The Journal of Finance, 58(4), 1651-1683. Jegadeesh, N. (1990). Evidence of predictable behavior of security returns. The Journal of Finance, 45(3), 881-898. Kelly, B. T., Pruitt, S., & Su, Y. (2019). Characteristics are covariances: a unified model of risk and return. Journal of Financial Economics, 134(3), 501-524. Ledoit, O., & Wolf, M. (2004). Honey, I shrunk the sample covariance matrix. The Journal of Portfolio Management, 30(4), 110-119. Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788-791. Lettau, M., & Pelger, M. (2020). Factors that fit the time series and cross-section of stock returns. The Review of Financial Studies, 33(5), 2274-2325. Lintner, J. (1965). The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. Review of Economics and Statistics, 47(1), 13-37. Lo, A. W., & Patel, P. N. (2008). 130/30: The new long-only. The Journal of Portfolio Management, 34(2), 12-38. Maillard, S., Roncalli, T., & Teiletche, J. (2010). The properties of equally weighted risk contribution portfolios. The Journal of Portfolio Management, 36(4), 60-70. Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica, 34(4), 768-783. Roll, R., & Ross, S. A. (1980). An empirical investigation of the arbitrage pricing theory. The Journal of Finance, 35(5), 1073-1103. Ross, S. A. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13(3), 341-360. Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425-442. Spilak, B., & Härdle, W. K. (2022). Risk budget portfolios with convex non-negative matrix factorization. arXiv preprint arXiv:2204.02757. Stock, J. H., & Watson, M. W. (2002). Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association, 97(460), 1167-1179.zh_TW