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題名 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
The Empirical Study of Weighted-Norm Minimum Variance Portfolios in Taiwan Stock Market作者 莊丹華
Jhuang, Dan-Hua貢獻者 顏佑銘
Yen, Yu-Min
莊丹華
Jhuang, Dan-Hua關鍵詞 台灣50
最小變異數投資組合
加權懲罰範數
Minimum variance portfolio
Weighted-Norm penalty日期 2017 上傳時間 2-Mar-2018 11:38:43 (UTC+8) 摘要 資產配置問題與方法一直是投資人所關心之重要課題。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，可使得投資人對所持有資產的管理變得更容易且具效率。在這些方法當中，最小變異數投資組合可滿足追求風險極小化之需求。本文亦從此出發，探討一種特殊的最小變異數投資組合：加權範數最小變異數投資組合，並以台灣50作為實證資料，運用十個績效指標來衡量加權範數最小變異數投資組合、其他三種標竿投資組合與指數型基金台灣50之表現。結果發現本研究所選取之台灣市場資料在運用加權範數最小變異數投資組合下，確實可以打敗其他大部分投資組合以及台灣50基金，並且在以下兩論點與過往文獻之敘述一致：加入報酬限制條件無法改善績效、使用替代參數亦可提供相稱績效。
The asset allocation problem has always been an important issue on which investors concern. It is easier and more efficient for investors to manage their assets through constructing their portfolios in different methods to find the most optimized weight of assets. This essay explores a special portfolio, Weighted-Norm Minimum Variance Portfolio (WNMVP), which can minimize the risks of investment, and use Taiwan stock market data to undertake empirical study. The research measured the performance of WNMVP, other three benchmark portfolios, and Taiwan Top 50 ETF (0050) by using ten indicators, bringing three findings. First, WNMVP performs better than most of other portfolios do. Second, adding estimated mean return vector into the WNMVP does not improve performances. Third, three alternative norm penalties provide comparable performance as parameters in WNMVP do. The second and third findings are consistence with previous literature.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，台北。2. Brodie, J., Daubechies, I., De Mol, C., Giannone, D., and Loris, I. (2009) Sparse and stable Markowitz portfolios, Proceedings of the National Academy of Sciences of the United States of America 106, 12267–12272.3. Chopra, Vijay K., and Ziemba, William T. (1993) The Effect of Errors in Means, Variances, and Covariances on Optimal Portfolio Choice, The Journal of Portfolio Management, 19, 6–11.4. DeMiguel, V., Garlappi, L., and Uppal, R. (2009) Optimal versus naive diversification: how inefficient is the 1/N portfolio strategy? Review of Financial Studies 22, 1915–1953.5. DeMiguel, V., Garlappi, L., Nogales, F. J., and Uppal, R. (2009) A generalized approach to portfolio optimization: improving performance by constraining portfolio norms, Management Science 55, 798–812.6. Fan, J., Zhang, J., and Yu, K. (2012) Vast portfolio selection with gross-exposure constraints, Journal of the American Statistical Association 107, 592–606.7. Friedman, J., Hastie, T., Ho¨ fling, H., and Tibshirani, R. (2007) Pathwise coordinate optimization, Annals of Applied Statistics 1, 302–332.8. Jagannathan, R. and Ma, T. (2003) Risk reduction in large portfolios: why imposing the wrong constraints helps, Journal of Finance 58, 1651–1684.9. Ledoit, O. and Wolf, M. (2003) Improved estimation of the covariance matrix of stock returns with an application to portfolio selection, Journal of Empirical Finance 10, 603–621.10. Markowitz, H. (1952) Portfolio Selection, The Journal of Finance, 7, 77–91.11. Merton, R. C. (1980) On estimating the expected return on the market: An exploratory investigation, Journal of Financial Economics, 8, 323–361.12. Tibshirani, R. (1996) Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B (Methodological), 58, 267–288.13. Yen, Y. –M. and Yen, T. J. (2014) Solving norm constrained portfolio optimization via coordinate-wise descent algorithms, Computational Statistics and Data Analysis 76, 737–759.14. Yen, Y. –M. (2015) Sparse Weighted-Norm Minimum Variance Portfolios, Review of Finance, 20, 1259–1287.15. Zou, H. and Hastie, T. (2005) Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67, 301–320. 描述 碩士
國立政治大學
國際經營與貿易學系
104351029資料來源 http://thesis.lib.nccu.edu.tw/record/#G0104351029 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) 莊丹華 zh_TW dc.contributor.author (Authors) Jhuang, Dan-Hua en_US dc.creator (作者) 莊丹華 zh_TW dc.creator (作者) Jhuang, Dan-Hua en_US dc.date (日期) 2017 en_US dc.date.accessioned 2-Mar-2018 11:38:43 (UTC+8) - dc.date.available 2-Mar-2018 11:38:43 (UTC+8) - dc.date.issued (上傳時間) 2-Mar-2018 11:38:43 (UTC+8) - dc.identifier (Other Identifiers) G0104351029 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/116007 - 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) 104351029 zh_TW dc.description.abstract (摘要) 資產配置問題與方法一直是投資人所關心之重要課題。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，可使得投資人對所持有資產的管理變得更容易且具效率。在這些方法當中，最小變異數投資組合可滿足追求風險極小化之需求。本文亦從此出發，探討一種特殊的最小變異數投資組合：加權範數最小變異數投資組合，並以台灣50作為實證資料，運用十個績效指標來衡量加權範數最小變異數投資組合、其他三種標竿投資組合與指數型基金台灣50之表現。結果發現本研究所選取之台灣市場資料在運用加權範數最小變異數投資組合下，確實可以打敗其他大部分投資組合以及台灣50基金，並且在以下兩論點與過往文獻之敘述一致：加入報酬限制條件無法改善績效、使用替代參數亦可提供相稱績效。 zh_TW dc.description.abstract (摘要) The asset allocation problem has always been an important issue on which investors concern. It is easier and more efficient for investors to manage their assets through constructing their portfolios in different methods to find the most optimized weight of assets. This essay explores a special portfolio, Weighted-Norm Minimum Variance Portfolio (WNMVP), which can minimize the risks of investment, and use Taiwan stock market data to undertake empirical study. The research measured the performance of WNMVP, other three benchmark portfolios, and Taiwan Top 50 ETF (0050) by using ten indicators, bringing three findings. First, WNMVP performs better than most of other portfolios do. Second, adding estimated mean return vector into the WNMVP does not improve performances. Third, three alternative norm penalties provide comparable performance as parameters in WNMVP do. The second and third findings are consistence with previous literature. 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 文獻回顧 4第一節 投資組合理論起源 4第二節 懲罰範數投資組合策略 5第三節 投資組合策略之比較 7第三章 研究方法 9第一節 加權範數最小變異數投資組合 9第二節 比較績效表現之指標 11第三節 替代懲罰範數 15第四章 實證分析 18第一節 樣本資料敘述 18第二節 實證結果與分析 19第三節 加入限制報酬條件 24第四節 替代懲罰範數之表現 27第五章 結論與建議 31第一節 結論 31第二節 未來研究建議 32參考文獻 33 zh_TW dc.format.extent 1164761 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0104351029 en_US dc.subject (關鍵詞) 台灣50 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) Minimum variance portfolio en_US dc.subject (關鍵詞) Weighted-Norm penalty en_US dc.title (題名) 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 zh_TW dc.title (題名) The Empirical Study of Weighted-Norm Minimum Variance Portfolios in Taiwan Stock Market 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，台北。2. Brodie, J., Daubechies, I., De Mol, C., Giannone, D., and Loris, I. (2009) Sparse and stable Markowitz portfolios, Proceedings of the National Academy of Sciences of the United States of America 106, 12267–12272.3. Chopra, Vijay K., and Ziemba, William T. (1993) The Effect of Errors in Means, Variances, and Covariances on Optimal Portfolio Choice, The Journal of Portfolio Management, 19, 6–11.4. DeMiguel, V., Garlappi, L., and Uppal, R. (2009) Optimal versus naive diversification: how inefficient is the 1/N portfolio strategy? Review of Financial Studies 22, 1915–1953.5. DeMiguel, V., Garlappi, L., Nogales, F. J., and Uppal, R. (2009) A generalized approach to portfolio optimization: improving performance by constraining portfolio norms, Management Science 55, 798–812.6. Fan, J., Zhang, J., and Yu, K. (2012) Vast portfolio selection with gross-exposure constraints, Journal of the American Statistical Association 107, 592–606.7. Friedman, J., Hastie, T., Ho¨ fling, H., and Tibshirani, R. (2007) Pathwise coordinate optimization, Annals of Applied Statistics 1, 302–332.8. Jagannathan, R. and Ma, T. (2003) Risk reduction in large portfolios: why imposing the wrong constraints helps, Journal of Finance 58, 1651–1684.9. Ledoit, O. and Wolf, M. (2003) Improved estimation of the covariance matrix of stock returns with an application to portfolio selection, Journal of Empirical Finance 10, 603–621.10. Markowitz, H. (1952) Portfolio Selection, The Journal of Finance, 7, 77–91.11. Merton, R. C. (1980) On estimating the expected return on the market: An exploratory investigation, Journal of Financial Economics, 8, 323–361.12. Tibshirani, R. (1996) Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B (Methodological), 58, 267–288.13. Yen, Y. –M. and Yen, T. J. (2014) Solving norm constrained portfolio optimization via coordinate-wise descent algorithms, Computational Statistics and Data Analysis 76, 737–759.14. Yen, Y. –M. (2015) Sparse Weighted-Norm Minimum Variance Portfolios, Review of Finance, 20, 1259–1287.15. Zou, H. and Hastie, T. (2005) Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67, 301–320. zh_TW
