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題名 以預期風險溢價建立股票評分系統—以台灣股市為例
Using Expected Risk Premium to Build Scoring System in Taiwan Stock Market作者 徐若庭
Hsu, Jo-Ting貢獻者 郭維裕
徐若庭
Hsu, Jo-Ting關鍵詞 風險溢價
因子溢價
超額報酬
四因子模型
Risk premium
Factor premium
Excess return
Four-factor model日期 2020 上傳時間 2-Sep-2020 11:40:13 (UTC+8) 摘要 經過幾次的金融危機，投資者對於分散風險更為看重，各種投資組合的建構方法不斷地推陳出新。本文結合因子溢價的觀點以及風險曝露來建構個股的分數，依據分數進行排名，接著將排名由低到高組成五組等權重投資組合，持有投資組合並檢視各組的報酬表現，藉此提供投資者一個新的風險評估方法。經本文實證研究得出，排名最低的投資組合，報酬表現較為優異，排名最高的組合，報酬表現則最為不佳。另外運用四因子模型進行分析，投資組合報酬約有84.7%能夠由模型解釋，且多數投資組合皆獲得了超額報酬，其中市場風險因子(MKT)對各投資組合報酬皆有顯著的正相關，經實證組成之五組投資組合顯示多偏向高帳面市值比、價值型投資組合。
After several financial crises, investors are more concerned about diversifying risks. Various methods to construct portfolio are constantly innovating. We integrating the views and exposures of the factor premiums to build the score of stocks, and rank stocks based on the score. Then, according to the ranking from low to high, the stocks are grouped into five equal weight portfolios. We hold the portfolios and observe the performance of each group to provide investors with a new method of risk assessment.The empirical results show that the lowest-ranked portfolio has the best performance, and the highest-ranked portfolio has the worst performance. In addition, we also use the four-factor model for analysis. Approximately 84.7% of the portfolios returns can be explained by the model`s inputs, and most of the portfolios have received excess returns. Market risk factor (MKT) has a significant positive correlation with the return of each portfolio. The five groups of portfolios in the empirical result tend to be high book-to-market ratio portfolios and value portfolios.參考文獻 1. Amenc, N., F. Goltz, and V. Le Sourd (2017). The EDHEC European ETF and Smart Beta Survey 2016. EDHEC Institute Publication Series. Paris: EDHEC-Risk Institute.2. Ang, A., R.J. Hodrick, Y. Xing, and X. Zhang (2006). The Cross- Section of Volatility and Expected Returns. The Journal of Finance, Vol. 61, No. 1, pp. 259-299.3. Arnott, R. D., J. C. Hsu and P. Moore. (2005). Fundamental Indexation. Financial Analysts Journal 61(2): 83-99.4. Black, F., M. Jensen, and M. Scholes (1972). The Capital Asset Pricing Model: Some Empirical Tests, pp. 79-121. In Studies in the Theory of Capital Markets edited by M. Jensen. New York, NY: Praeger.5. Clarke, R., H. de Silva, and S. Thorley (2011). Minimum Variance Portfolio Composition. The Journal of Portfolio Management, Vol. 37, No. 2, pp. 31-45.6. Da Silva, A. S., and W. Lee. (2017). From Risk Premia to Smart Betas: A Unified Framework. The Journal of Portfolio Management 44 (1): 44–54.7. Engle, R. F., Ledoit, O., and Wolf, M. (2017). Large dynamic covariance matrices. Journal of Business & Economic Statistics. doi: 0.1080/07350015.2017.1345683.8. Gordon, Myron J. (1959). Dividends, Earnings and Stock Prices. Review of Economics and Statistics. The MIT Press. 41 (2): 99–105.9. Harvey, C. R., Y. Liu, and H. Zhu. (2016). ...and the Cross- Section of Expected Returns. The Review of Financial Studies 29 (1): 5–68.10. Hodges, P., K. Hogan, J. Petterson, and A. Ang. (2017). Factor Timing with Cross-Sectional and Time-Series Predictions. The Journal of Portfolio Management 44 (1): 30–43.11. Hou, K., Xue, C., and Zhang, L. (2015). Digesting anomalies: An investment approach. Review of Financial Studies, 28(3):650–705.12. Ledoit, O. and Wolf, M. (2004b). A well-conditioned estimator for large-dimensional covariance matrices. Journal of Multivariate Analysis, 88(2):365–411.13. Ledoit, O., M. Wolf, and Z. Zhao (2018). Efficient Sorting: A More Powerful Test for Cross-Sectional Anomalies. Working paper no. 238, University of Zurich, Dept. of Economics, https://ssrn.com/abstract=2881417.14. Lee, W. (2014). Constraints and Innovations for Pension Investment: The Cases of Risk Parity and Risk Premia Investing. The Journal of Portfolio Management, Vol. 40, No. 3, pp. 12-20.15. Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7:77–91.16. McLean, R. D. and Pontiff, J. (2016). Does academic research destroy stock return predictability? Journal of Finance, 71(1):5–32.17. Steven P. Greiner and Stoyan V. Stoyanov (2019). Portfolio Scoring by Expected Risk Premium. The Journal of Portfolio Management, 45 (4) 83-90. 描述 碩士
國立政治大學
國際經營與貿易學系
107351031資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107351031 資料類型 thesis dc.contributor.advisor 郭維裕 zh_TW dc.contributor.author (Authors) 徐若庭 zh_TW dc.contributor.author (Authors) Hsu, Jo-Ting en_US dc.creator (作者) 徐若庭 zh_TW dc.creator (作者) Hsu, Jo-Ting en_US dc.date (日期) 2020 en_US dc.date.accessioned 2-Sep-2020 11:40:13 (UTC+8) - dc.date.available 2-Sep-2020 11:40:13 (UTC+8) - dc.date.issued (上傳時間) 2-Sep-2020 11:40:13 (UTC+8) - dc.identifier (Other Identifiers) G0107351031 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131462 - 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) 107351031 zh_TW dc.description.abstract (摘要) 經過幾次的金融危機，投資者對於分散風險更為看重，各種投資組合的建構方法不斷地推陳出新。本文結合因子溢價的觀點以及風險曝露來建構個股的分數，依據分數進行排名，接著將排名由低到高組成五組等權重投資組合，持有投資組合並檢視各組的報酬表現，藉此提供投資者一個新的風險評估方法。經本文實證研究得出，排名最低的投資組合，報酬表現較為優異，排名最高的組合，報酬表現則最為不佳。另外運用四因子模型進行分析，投資組合報酬約有84.7%能夠由模型解釋，且多數投資組合皆獲得了超額報酬，其中市場風險因子(MKT)對各投資組合報酬皆有顯著的正相關，經實證組成之五組投資組合顯示多偏向高帳面市值比、價值型投資組合。 zh_TW dc.description.abstract (摘要) After several financial crises, investors are more concerned about diversifying risks. Various methods to construct portfolio are constantly innovating. We integrating the views and exposures of the factor premiums to build the score of stocks, and rank stocks based on the score. Then, according to the ranking from low to high, the stocks are grouped into five equal weight portfolios. We hold the portfolios and observe the performance of each group to provide investors with a new method of risk assessment.The empirical results show that the lowest-ranked portfolio has the best performance, and the highest-ranked portfolio has the worst performance. In addition, we also use the four-factor model for analysis. Approximately 84.7% of the portfolios returns can be explained by the model`s inputs, and most of the portfolios have received excess returns. Market risk factor (MKT) has a significant positive correlation with the return of each portfolio. The five groups of portfolios in the empirical result tend to be high book-to-market ratio portfolios and value portfolios. en_US dc.description.tableofcontents 第一章 緒論 1第一節 研究背景與動機 1第二節 研究目的 2第三節 研究架構 3第二章 文獻探討 4第一節 標準之選定 4第二節 收益之橫截面 5第三節 資產定價因子之創新 7第四節 SMART BETA策略 8第三章 研究方法 10第一節 資料來源 10第二節 隨機折現因子 10第三節 實證模型 11第四章 實證結果分析 16第一節 因子之表現 16第二節 投資組合之表現 18第三節 投資組合之迴歸分析 22第五章 結論與建議 25第一節 研究結論 25第二節 研究限制和未來研究方向 26參考文獻 27附錄 30 zh_TW dc.format.extent 2004816 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107351031 en_US dc.subject (關鍵詞) 風險溢價 zh_TW dc.subject (關鍵詞) 因子溢價 zh_TW dc.subject (關鍵詞) 超額報酬 zh_TW dc.subject (關鍵詞) 四因子模型 zh_TW dc.subject (關鍵詞) Risk premium en_US dc.subject (關鍵詞) Factor premium en_US dc.subject (關鍵詞) Excess return en_US dc.subject (關鍵詞) Four-factor model en_US dc.title (題名) 以預期風險溢價建立股票評分系統—以台灣股市為例 zh_TW dc.title (題名) Using Expected Risk Premium to Build Scoring System in Taiwan Stock Market en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 1. Amenc, N., F. Goltz, and V. Le Sourd (2017). The EDHEC European ETF and Smart Beta Survey 2016. EDHEC Institute Publication Series. Paris: EDHEC-Risk Institute.2. Ang, A., R.J. Hodrick, Y. Xing, and X. Zhang (2006). The Cross- Section of Volatility and Expected Returns. The Journal of Finance, Vol. 61, No. 1, pp. 259-299.3. Arnott, R. D., J. C. Hsu and P. Moore. (2005). Fundamental Indexation. Financial Analysts Journal 61(2): 83-99.4. Black, F., M. Jensen, and M. Scholes (1972). The Capital Asset Pricing Model: Some Empirical Tests, pp. 79-121. In Studies in the Theory of Capital Markets edited by M. Jensen. New York, NY: Praeger.5. Clarke, R., H. de Silva, and S. Thorley (2011). Minimum Variance Portfolio Composition. The Journal of Portfolio Management, Vol. 37, No. 2, pp. 31-45.6. Da Silva, A. S., and W. Lee. (2017). From Risk Premia to Smart Betas: A Unified Framework. The Journal of Portfolio Management 44 (1): 44–54.7. Engle, R. F., Ledoit, O., and Wolf, M. (2017). Large dynamic covariance matrices. Journal of Business & Economic Statistics. doi: 0.1080/07350015.2017.1345683.8. Gordon, Myron J. (1959). Dividends, Earnings and Stock Prices. Review of Economics and Statistics. The MIT Press. 41 (2): 99–105.9. Harvey, C. R., Y. Liu, and H. Zhu. (2016). ...and the Cross- Section of Expected Returns. The Review of Financial Studies 29 (1): 5–68.10. Hodges, P., K. Hogan, J. Petterson, and A. Ang. (2017). Factor Timing with Cross-Sectional and Time-Series Predictions. The Journal of Portfolio Management 44 (1): 30–43.11. Hou, K., Xue, C., and Zhang, L. (2015). Digesting anomalies: An investment approach. Review of Financial Studies, 28(3):650–705.12. Ledoit, O. and Wolf, M. (2004b). A well-conditioned estimator for large-dimensional covariance matrices. Journal of Multivariate Analysis, 88(2):365–411.13. Ledoit, O., M. Wolf, and Z. Zhao (2018). Efficient Sorting: A More Powerful Test for Cross-Sectional Anomalies. Working paper no. 238, University of Zurich, Dept. of Economics, https://ssrn.com/abstract=2881417.14. Lee, W. (2014). Constraints and Innovations for Pension Investment: The Cases of Risk Parity and Risk Premia Investing. The Journal of Portfolio Management, Vol. 40, No. 3, pp. 12-20.15. Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7:77–91.16. McLean, R. D. and Pontiff, J. (2016). Does academic research destroy stock return predictability? Journal of Finance, 71(1):5–32.17. Steven P. Greiner and Stoyan V. Stoyanov (2019). Portfolio Scoring by Expected Risk Premium. The Journal of Portfolio Management, 45 (4) 83-90. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202001347 en_US
