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題名 利用smart beta策略與主成分分析建構台灣股票市場資產配置
he Asset Allocation According to Smart Beta and Principal Components Analysis in Taiwan Stock Market
作者 魏巧昀
貢獻者 黃泓智
魏巧昀
關鍵詞 主成分分析
資產配置
股價淨值比
Smart Beta
Sharpe Ratio
ASKSR
日期 2016
上傳時間 20-Jul-2016 17:17:39 (UTC+8)
摘要 本研究以近15年台灣股票市場所有上市、上櫃、下市、下櫃股票為樣本，利用每季公布之財務報表的資料，市值、現金流量與股價比率、本益比、資產報酬率、負債比率、報酬率之標準差等指標作為篩選股票依據。
首先，先用財務報表的資料建構出Smart Beta Factor，結合主成分分析將各股評分，作為股票篩選之指標。第一步驟先把市值較低、成交金額過低的股票刪除，並依照不同指標篩選出五倍符合投資組合之股票數，接著運用主成分分析評分後的指標將各公司排序，選出分數高的作為投資組合，以達到分散風險的目標。
本文所討論之Smart Beta Factors有Size、Quality、Value、Momentum、Volatility，並將各Smart beta factor結合主成分分析，計算分數以選出優良股票，並以等權重方式進行資產配置，希望能建構出最有利的投資組合，使得獲利穩定成長。
In this study, using nearly 15 years quarterly financial statement of stock market in Taiwan as samples. Not only use the financial statement to construct the smart beta factor, also use the principle components analysis to calculate the scores of all the stocks, then choose the stock by the scores.
First, delete the stocks of low market value and the stocks of low turnover rate. Second, selected five times the number of the investment portfolio by different indicators, then elect the number of investment portfolio stocks by the highest scores calculated by principal component analysis. To achieve the goal of risk diversification.
The smart beta factors discussed in the paper are Size, Quality, Value, Momentum, Volatility, also the multiple factor. To combine the method of principal component analysis, calculate the score to select the stocks, in order to contract the portfolio which has the best performance, and can make stable growth of profits.
參考文獻 1. Ait-Sahalia, Yacine, and Michael W Brandt, 2001. Variable selection for portfolio choice: National Bureau of Economic Research.
2. Altman and Edward I, 1968. Financial Ratios, Discriminant Analysis and Prediction of Corporate Bankruptcy. The Journal of Finance, Vol. 23, Issue 4.
3. Andrew Ang, Robert J.Hodrick, Yuhang Xing, and Xiaoyan Zhang, 2006. The Cross-Section of Volatility and Expected Returns. The journal of Finance, Vol. LXI, No. 1.
4. Basu and Sanjoy, 1977. Investment Performance of Common Stocks in Relation to their Price Earnings Ratios: A Test of the Efficient Market Hypothesis. The journal of Finance32 (3):663-682.
5. Carlos Eduardo Thomaz and Gilson Antonio Giraldi, 2009. A new ranking method for principal components analysis and its application to face image analysis. Image and Vision Computing 28, 902–913.
6. Clifford S.Asness, Tobias J.Moskowitz, and Lasse Heje Pedersen, 2013. Value and Momentum Everywhere. The Journal of Finance, Vol. LVIII, No. 3.
7. Clifford S. Asness, Andrea Frazzini, and Lasse H. Pedersen, 2014. Quality Minus Junk.
8. Denys Glushkov, 2015. How Smart are “Smart Beta” ETFs? Analysis of Relative Performance and Factor Exposure. University of Pennsylvania - The Wharton School, Wharton Research.
9. Fangjian Fu, 2009. Idiosyncratic risk and the cross-section of expected stock returns. Journal of Financial Economics 91, 24–37.
10. Fama, & French, Kenneth R, 1992. The Cross-Section of Expected Stock Returns. Journal of Finance, American Finance Association, vol. 47(2), pages 427-65.
11. Fama, & French, Kenneth R, 1995. Size and Book-to-Market Factors in Earnings and Returns. Journal of Finance, American Finance Association, vol. 50(1), pages 131-55.
12. Gregory Connor and Robert A. Korajczyk, 1993. A Test for the Number of Factors in an Approximate Factor Model. Journal of Finance, Vol. 48, 1263-1291.
13. Joseph D. Piotroski, 2002. Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers. Journal of Accounting Research. Vol, 38.
14. Julian Rachlin, 2006. Principal Component Analysis and Extreme Value Theory in Financial Applications.
15. J. Dauxious, A. Pousse, Y. Romain, 1982. Asymptotic Theory for the Principal Component Analysis of a Vector Random Function: Some Applications to Statistical Inference. Journal of multivariate analysis. 12, 136–154.
16. Louis K. C. Chan, Jason Karceski, Josef Lakonishok, 1988. The risk and return from factors. The Journal of Financial and Quantitative Analysis, Vol, 33, No. 2. 159-188.
17. Ludwig B Chincarini and Daehwan Kim, 2006. Quantitative Equity Portfolio Management. : An Active Approach to Portfolio Construction and Management (McGraw-Hill Library of Investment and Finance).
18. Noël Amenc, Felix Goltz amd Lionel Martellini, 2013. Smart Beta 2.0. EDHEC-RISK Position Paper.
19. Noël Amenc, Felix Goltz, Ashish Lodh, and Sivagaminathan Sivasubramanian, 2014. Robustness of Smart Beta Strategies. The Journal of Index Investing, 1:17-38.
20. Noël Amenc, Felix Goltz, 2014. ERI Scientific Beta Developed Low-Volatility Diversified Multi-Strategy. An ERI Scientific Beta Publication.
21. Noël Amenc, Felix Goltz, 2014. ERI Scientific Beta Publication Scientific Beta Multi-Beta Multi-Strategy Indices Equity Portfolios. An ERI Scientific Beta Publication.
22. Partha S. Mohanram, 2005. Separating Winners from Losers among Low Book-to-Market Stocks using Financial Statement Analysis. Springer Science+Business Media, Inc., Manufactured in The Netherlands. 10, 133–170.
23. Rocio Duran-Vazquez, Arturo Lorenzo-Valdes, and Claudia E. Castillo-Ramirez, 2014. Effectiveness of corporate finance valuation methods: Piotroski score in an Ohlson model: the case of Mexico. Journal of Economics Finance and Administrative Science, Vol. 19, Issue 37, P. 104-107.
24. Roger Clarke, Harindra de Silva, and Steven Thorley, 2006. Minimum-Variance Portfolios in the U.S. Equity Market. The Journal of Portfolio Management.
25. Ronald N.Kahn and Michael Lemmon, 2015. Smart Beta: The Owner’s Manual. The Journal of Portfolio Management. Vol. 41, No. 2: pp. 76-83.
26. Saud AlMahdi, 2015. Smart Beta Portfolio Optimization. Journal of Mathematical Finance, 202-211.
27. Shlens J, 2009. A Tutorial on Principal Component Analysis. http://www.snl.salk.edu/~shlens/pca.pdf
28. Vincent Denoiseux, 2014. Smart Beta: Building Low-Volatility Portfolios of ETFs. The Journal of Index Investing, 1:127-135
29. Yacine Ait-Sahalia and Dacheng Xiu, 2015. Principal Component Analysis of High Frequency Data. NBER Working Paper No. 21584.
30. 蘇嘉雄，2013。 以財務報表資訊為台灣股票市場建構最適資產配置
描述 碩士
國立政治大學
風險管理與保險研究所
103358011
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0103358011
資料類型 thesis
dc.contributor.advisor 黃泓智zh_TW
dc.contributor.author (Authors) 魏巧昀zh_TW
dc.creator (作者) 魏巧昀zh_TW
dc.date (日期) 2016en_US
dc.date.accessioned 20-Jul-2016 17:17:39 (UTC+8)-
dc.date.available 20-Jul-2016 17:17:39 (UTC+8)-
dc.date.issued (上傳時間) 20-Jul-2016 17:17:39 (UTC+8)-
dc.identifier (Other Identifiers) G0103358011en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/99345-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 風險管理與保險研究所zh_TW
dc.description (描述) 103358011zh_TW
dc.description.abstract (摘要) 本研究以近15年台灣股票市場所有上市、上櫃、下市、下櫃股票為樣本，利用每季公布之財務報表的資料，市值、現金流量與股價比率、本益比、資產報酬率、負債比率、報酬率之標準差等指標作為篩選股票依據。
首先，先用財務報表的資料建構出Smart Beta Factor，結合主成分分析將各股評分，作為股票篩選之指標。第一步驟先把市值較低、成交金額過低的股票刪除，並依照不同指標篩選出五倍符合投資組合之股票數，接著運用主成分分析評分後的指標將各公司排序，選出分數高的作為投資組合，以達到分散風險的目標。
本文所討論之Smart Beta Factors有Size、Quality、Value、Momentum、Volatility，並將各Smart beta factor結合主成分分析，計算分數以選出優良股票，並以等權重方式進行資產配置，希望能建構出最有利的投資組合，使得獲利穩定成長。		zh_TW
dc.description.abstract (摘要) In this study, using nearly 15 years quarterly financial statement of stock market in Taiwan as samples. Not only use the financial statement to construct the smart beta factor, also use the principle components analysis to calculate the scores of all the stocks, then choose the stock by the scores.
First, delete the stocks of low market value and the stocks of low turnover rate. Second, selected five times the number of the investment portfolio by different indicators, then elect the number of investment portfolio stocks by the highest scores calculated by principal component analysis. To achieve the goal of risk diversification.
The smart beta factors discussed in the paper are Size, Quality, Value, Momentum, Volatility, also the multiple factor. To combine the method of principal component analysis, calculate the score to select the stocks, in order to contract the portfolio which has the best performance, and can make stable growth of profits.		en_US
dc.description.tableofcontents 第一章 緒論 6
第一節 動機與研究背景 6
第二節 研究目的 7
第三節 研究流程 8
第二章 文獻探討 10
第一節 SMART BETA FACTOR之文獻探討 10
第二節 主成分分析之文獻探討 12
第三章 研究方法 14
第一節 建構SMART BETA FACTOR之分數 14
第二節 建立投資組合 23
第四章 實證結果分析 28
第一節 資料整理與簡介 28
第二節 投資組合之表現 28
第五章 結論與未來建議 38
參考文獻 40
附錄 43		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0103358011en_US
dc.subject (關鍵詞) 主成分分析zh_TW
dc.subject (關鍵詞) 資產配置zh_TW
dc.subject (關鍵詞) 股價淨值比zh_TW
dc.subject (關鍵詞) Smart Betaen_US
dc.subject (關鍵詞) Sharpe Ratioen_US
dc.subject (關鍵詞) ASKSRen_US
dc.title (題名) 利用smart beta策略與主成分分析建構台灣股票市場資產配置zh_TW
dc.title (題名) he Asset Allocation According to Smart Beta and Principal Components Analysis in Taiwan Stock Marketen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 1. Ait-Sahalia, Yacine, and Michael W Brandt, 2001. Variable selection for portfolio choice: National Bureau of Economic Research.
2. Altman and Edward I, 1968. Financial Ratios, Discriminant Analysis and Prediction of Corporate Bankruptcy. The Journal of Finance, Vol. 23, Issue 4.
3. Andrew Ang, Robert J.Hodrick, Yuhang Xing, and Xiaoyan Zhang, 2006. The Cross-Section of Volatility and Expected Returns. The journal of Finance, Vol. LXI, No. 1.
4. Basu and Sanjoy, 1977. Investment Performance of Common Stocks in Relation to their Price Earnings Ratios: A Test of the Efficient Market Hypothesis. The journal of Finance32 (3):663-682.
5. Carlos Eduardo Thomaz and Gilson Antonio Giraldi, 2009. A new ranking method for principal components analysis and its application to face image analysis. Image and Vision Computing 28, 902–913.
6. Clifford S.Asness, Tobias J.Moskowitz, and Lasse Heje Pedersen, 2013. Value and Momentum Everywhere. The Journal of Finance, Vol. LVIII, No. 3.
7. Clifford S. Asness, Andrea Frazzini, and Lasse H. Pedersen, 2014. Quality Minus Junk.
8. Denys Glushkov, 2015. How Smart are “Smart Beta” ETFs? Analysis of Relative Performance and Factor Exposure. University of Pennsylvania - The Wharton School, Wharton Research.
9. Fangjian Fu, 2009. Idiosyncratic risk and the cross-section of expected stock returns. Journal of Financial Economics 91, 24–37.
10. Fama, & French, Kenneth R, 1992. The Cross-Section of Expected Stock Returns. Journal of Finance, American Finance Association, vol. 47(2), pages 427-65.
11. Fama, & French, Kenneth R, 1995. Size and Book-to-Market Factors in Earnings and Returns. Journal of Finance, American Finance Association, vol. 50(1), pages 131-55.
12. Gregory Connor and Robert A. Korajczyk, 1993. A Test for the Number of Factors in an Approximate Factor Model. Journal of Finance, Vol. 48, 1263-1291.
13. Joseph D. Piotroski, 2002. Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers. Journal of Accounting Research. Vol, 38.
14. Julian Rachlin, 2006. Principal Component Analysis and Extreme Value Theory in Financial Applications.
15. J. Dauxious, A. Pousse, Y. Romain, 1982. Asymptotic Theory for the Principal Component Analysis of a Vector Random Function: Some Applications to Statistical Inference. Journal of multivariate analysis. 12, 136–154.
16. Louis K. C. Chan, Jason Karceski, Josef Lakonishok, 1988. The risk and return from factors. The Journal of Financial and Quantitative Analysis, Vol, 33, No. 2. 159-188.
17. Ludwig B Chincarini and Daehwan Kim, 2006. Quantitative Equity Portfolio Management. : An Active Approach to Portfolio Construction and Management (McGraw-Hill Library of Investment and Finance).
18. Noël Amenc, Felix Goltz amd Lionel Martellini, 2013. Smart Beta 2.0. EDHEC-RISK Position Paper.
19. Noël Amenc, Felix Goltz, Ashish Lodh, and Sivagaminathan Sivasubramanian, 2014. Robustness of Smart Beta Strategies. The Journal of Index Investing, 1:17-38.
20. Noël Amenc, Felix Goltz, 2014. ERI Scientific Beta Developed Low-Volatility Diversified Multi-Strategy. An ERI Scientific Beta Publication.
21. Noël Amenc, Felix Goltz, 2014. ERI Scientific Beta Publication Scientific Beta Multi-Beta Multi-Strategy Indices Equity Portfolios. An ERI Scientific Beta Publication.
22. Partha S. Mohanram, 2005. Separating Winners from Losers among Low Book-to-Market Stocks using Financial Statement Analysis. Springer Science+Business Media, Inc., Manufactured in The Netherlands. 10, 133–170.
23. Rocio Duran-Vazquez, Arturo Lorenzo-Valdes, and Claudia E. Castillo-Ramirez, 2014. Effectiveness of corporate finance valuation methods: Piotroski score in an Ohlson model: the case of Mexico. Journal of Economics Finance and Administrative Science, Vol. 19, Issue 37, P. 104-107.
24. Roger Clarke, Harindra de Silva, and Steven Thorley, 2006. Minimum-Variance Portfolios in the U.S. Equity Market. The Journal of Portfolio Management.
25. Ronald N.Kahn and Michael Lemmon, 2015. Smart Beta: The Owner’s Manual. The Journal of Portfolio Management. Vol. 41, No. 2: pp. 76-83.
26. Saud AlMahdi, 2015. Smart Beta Portfolio Optimization. Journal of Mathematical Finance, 202-211.
27. Shlens J, 2009. A Tutorial on Principal Component Analysis. http://www.snl.salk.edu/~shlens/pca.pdf
28. Vincent Denoiseux, 2014. Smart Beta: Building Low-Volatility Portfolios of ETFs. The Journal of Index Investing, 1:127-135
29. Yacine Ait-Sahalia and Dacheng Xiu, 2015. Principal Component Analysis of High Frequency Data. NBER Working Paper No. 21584.
30. 蘇嘉雄，2013。 以財務報表資訊為台灣股票市場建構最適資產配置		zh_TW