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題名 機器學習下建構ESG股息波動因子投資組合
Constructing ESG Portfolio with Factor Investing for Dividend Yield and Volatility by Machine Learning作者 賴晨心
Lai, Chen-Hsin貢獻者 林士貴
Lin, Shih-Kuei
賴晨心
Lai, Chen-Hsin關鍵詞 XGBoost
粒子群最佳化
因子投資
投資組合理論
ESG
XGBoost
Particle Swarm Optimization
Factor Investment
Portfolio Theory
ESG日期 2021 上傳時間 4-Aug-2021 14:51:17 (UTC+8) 摘要 因應投資人需求，全球資產管理規模成長迅速，其中ESG (Environmental, Social and Governance, ESG)、股息與波動為長期投資人熱門選擇標的。本文使用美國證券市場於2003到2019年的資料，透過機器學習XGBoost與歷史因子投資法預測未來股息波動因子特性，並以粒子群最佳化 (Particle Swarm Optimization, PSO) 建構限制資產數量與權重的最佳化投資組合，本文探討議題與實證結果歸納為以下四點：(1) 比較歷史因子投資與機器學習兩種方法之預測能力，兩者皆具相當程度的預測能力，且機器學習預測能力較佳，其中機器學習之重要特徵變數為過去殖利率、波動度、本益比；(2) 分別針對歷史因子投資與機器學習預測法建構Markowitz投資組合，機器學習下之因子投資最接近正確股息波動投資組合表現；(3) 利用PSO配置限制資產數量的投資組合，能夠達到Markowitz全樣本投資組合之績效；(4) 比較全體與ESG資料集結合股息波動因子表現，ESG結合股息波動因子對於投資組合的績效表現有正向關係。
In response to the needs of investors, the scale of global asset management has grown rapidly. ESG, high dividends, and low volatility are popular choices for investors in long-term. In the study, data from U.S. securities market from 2003 to 2019 are used to predict the characteristics of future dividend and volatility factors through machine learning XGBoost model and historical factor investing method. Furthermore, PSO is used to construct optimized portfolio with limits of the number of assets, maximum and minimum weight. The empirical results and main topics are summarized into the following three points: (1) Compare the predictability of dividend and volatility between historical factor investing and machine learning methods, both have great predictive ability and ability of machine learning is better. The important characteristic variables of machine learning prediction are historical dividend, volatility, and price-to-earnings ratio. (2) The performance of portfolio with dividend yield and volatility by machine learning is closer to correct data than historical factor investing method. (3) Using PSO to construct portfolio with a limited number of assets can achieve the performance of Markowitz`s full sample portfolio. (4) ESG combined with high dividend and low volatility has a positive relationship with portfolio performance.參考文獻 1. Ashwin Kumar, N. C., Smith, C., Badis, L., Wang, N., Ambrosy, P., & Tavares, R. (2016). ESG factors and risk-adjusted performance: a new quantitative model. Journal of Sustainable Finance & Investment, 6(4), 292-300.2. Avramov, D., & Zhou, G. (2010). Bayesian portfolio analysis. Annual Review of Financial Economics, 2(1), 25-47.3. Baskin, J. (1989). Dividend policy and the volatility of common stocks. Journal of Portfolio Management,15(3), 19.4. Blume, M. E. (1980). Stock returns and dividend yields: Some more evidence. The Review of Economics and Statistics, 567-577.5. Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).6. Deng, G. F., Lin, W. T., & Lo, C. C. (2012). Markowitz-based portfolio selection with cardinality constraints using improved particle swarm optimization. Expert Systems with Applications, 39(4), 4558-4566.7. Fernández, A., & Gómez, S. (2007). Portfolio selection using neural networks. Computers & Operations Research, 34(4), 1177-1191.8. Gombola, M. J., & Liu, F. Y. L. (1993). Dividend yields and stock returns: Evidence of time variation between bull and bear markets. Financial Review, 28(3), 303-327.9. Haugen, R. A., & Baker, N. L. (1991). The efficient market inefficiency of capitalization–weighted stock portfolios. The Journal of Portfolio Management, 17(3), 35-40.10. Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN`95-International Conference on Neural Networks (Vol. 4, pp. 1942-1948). IEEE.11. Markowitz, H. (1959). Portfolio Selection: Efficient Diversification of Investments (Vol. 16). New York: John Wiley.12. Michaud, R. O. (1989). The Markowitz optimization enigma: Is ‘optimized’optimal? Financial Analysts Journal, 45(1), 31-42.13. Nofsinger, J., & Varma, A. (2014). Socially responsible funds and market crises. Journal of Banking & Finance, 48, 180-193.14. Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications, 42(1), 259-268.15. Ratnaweera, A., Halgamuge, S. K., & Watson, H. C. (2004). Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on Evolutionary Computation, 8(3), 240-255.16. Renneboog, L., Ter Horst, J., & Zhang, C. (2008). The price of ethics and stakeholder governance: The performance of socially responsible mutual funds. Journal of Corporate Finance, 14(3), 302-322.17. Shi, Y., & Eberhart, R. (1998, May). A modified particle swarm optimizer. In 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No. 98TH8360) (pp. 69-73). IEEE.18. Verheyden, T., Eccles, R. G., & Feiner, A. (2016). ESG for all? The impact of ESG screening on return, risk, and diversification. Journal of Applied Corporate Finance, 28(2),47-55.19. Zhongbin, Z., & Jinwu, F. (2019, December). Empirical research about quantitative stock picking based on machine learning. In 2019 International Conference on Economic Management and Cultural Industry (ICEMCI 2019) (pp. 138-141). Atlantis Press. 描述 碩士
國立政治大學
金融學系
108352020資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108352020 資料類型 thesis dc.contributor.advisor 林士貴 zh_TW dc.contributor.advisor Lin, Shih-Kuei en_US dc.contributor.author (Authors) 賴晨心 zh_TW dc.contributor.author (Authors) Lai, Chen-Hsin en_US dc.creator (作者) 賴晨心 zh_TW dc.creator (作者) Lai, Chen-Hsin en_US dc.date (日期) 2021 en_US dc.date.accessioned 4-Aug-2021 14:51:17 (UTC+8) - dc.date.available 4-Aug-2021 14:51:17 (UTC+8) - dc.date.issued (上傳時間) 4-Aug-2021 14:51:17 (UTC+8) - dc.identifier (Other Identifiers) G0108352020 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/136361 - 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) 108352020 zh_TW dc.description.abstract (摘要) 因應投資人需求，全球資產管理規模成長迅速，其中ESG (Environmental, Social and Governance, ESG)、股息與波動為長期投資人熱門選擇標的。本文使用美國證券市場於2003到2019年的資料，透過機器學習XGBoost與歷史因子投資法預測未來股息波動因子特性，並以粒子群最佳化 (Particle Swarm Optimization, PSO) 建構限制資產數量與權重的最佳化投資組合，本文探討議題與實證結果歸納為以下四點：(1) 比較歷史因子投資與機器學習兩種方法之預測能力，兩者皆具相當程度的預測能力，且機器學習預測能力較佳，其中機器學習之重要特徵變數為過去殖利率、波動度、本益比；(2) 分別針對歷史因子投資與機器學習預測法建構Markowitz投資組合，機器學習下之因子投資最接近正確股息波動投資組合表現；(3) 利用PSO配置限制資產數量的投資組合，能夠達到Markowitz全樣本投資組合之績效；(4) 比較全體與ESG資料集結合股息波動因子表現，ESG結合股息波動因子對於投資組合的績效表現有正向關係。 zh_TW dc.description.abstract (摘要) In response to the needs of investors, the scale of global asset management has grown rapidly. ESG, high dividends, and low volatility are popular choices for investors in long-term. In the study, data from U.S. securities market from 2003 to 2019 are used to predict the characteristics of future dividend and volatility factors through machine learning XGBoost model and historical factor investing method. Furthermore, PSO is used to construct optimized portfolio with limits of the number of assets, maximum and minimum weight. The empirical results and main topics are summarized into the following three points: (1) Compare the predictability of dividend and volatility between historical factor investing and machine learning methods, both have great predictive ability and ability of machine learning is better. The important characteristic variables of machine learning prediction are historical dividend, volatility, and price-to-earnings ratio. (2) The performance of portfolio with dividend yield and volatility by machine learning is closer to correct data than historical factor investing method. (3) Using PSO to construct portfolio with a limited number of assets can achieve the performance of Markowitz`s full sample portfolio. (4) ESG combined with high dividend and low volatility has a positive relationship with portfolio performance. en_US dc.description.tableofcontents 第一章 緒論 1第一節 研究動機與目的 1第二節 研究架構 3第二章 文獻探討 5第一節 因子投資 5第二節 機器學習方法 6第三節 Markowitz投資組合理論與粒子群最佳化 7第四節 ESG與投資組合報酬 8第三章 研究方法 11第一節 XGBoost機器學習方法 11第二節 Markowitz投資組合理論與實務問題 15第三節 粒子群最佳化 19第四節 預測與績效評估指標 23第四章 實證結果 26第一節 資料描述與模型設定 26第二節 歷史因子投資與機器學習預測績效 28第三節 歷史因子投資與機器學習投資組合報酬績效 32第四節 Markowitz與粒子群最佳配置投資組合報酬績效 36第五節 ESG與股息波動投資組合報酬績效 38第五章 結論與未來展望 47第一節 研究結論 47第二節 未來展望 49參考文獻 50附錄：機器學習模型之輸入變數 52 zh_TW dc.format.extent 2401533 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108352020 en_US dc.subject (關鍵詞) XGBoost zh_TW dc.subject (關鍵詞) 粒子群最佳化 zh_TW dc.subject (關鍵詞) 因子投資 zh_TW dc.subject (關鍵詞) 投資組合理論 zh_TW dc.subject (關鍵詞) ESG zh_TW dc.subject (關鍵詞) XGBoost en_US dc.subject (關鍵詞) Particle Swarm Optimization en_US dc.subject (關鍵詞) Factor Investment en_US dc.subject (關鍵詞) Portfolio Theory en_US dc.subject (關鍵詞) ESG en_US dc.title (題名) 機器學習下建構ESG股息波動因子投資組合 zh_TW dc.title (題名) Constructing ESG Portfolio with Factor Investing for Dividend Yield and Volatility by Machine Learning en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 1. Ashwin Kumar, N. C., Smith, C., Badis, L., Wang, N., Ambrosy, P., & Tavares, R. (2016). ESG factors and risk-adjusted performance: a new quantitative model. Journal of Sustainable Finance & Investment, 6(4), 292-300.2. Avramov, D., & Zhou, G. (2010). Bayesian portfolio analysis. Annual Review of Financial Economics, 2(1), 25-47.3. Baskin, J. (1989). Dividend policy and the volatility of common stocks. Journal of Portfolio Management,15(3), 19.4. Blume, M. E. (1980). Stock returns and dividend yields: Some more evidence. The Review of Economics and Statistics, 567-577.5. Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).6. Deng, G. F., Lin, W. T., & Lo, C. C. (2012). Markowitz-based portfolio selection with cardinality constraints using improved particle swarm optimization. Expert Systems with Applications, 39(4), 4558-4566.7. Fernández, A., & Gómez, S. (2007). Portfolio selection using neural networks. Computers & Operations Research, 34(4), 1177-1191.8. Gombola, M. J., & Liu, F. Y. L. (1993). Dividend yields and stock returns: Evidence of time variation between bull and bear markets. Financial Review, 28(3), 303-327.9. Haugen, R. A., & Baker, N. L. (1991). The efficient market inefficiency of capitalization–weighted stock portfolios. The Journal of Portfolio Management, 17(3), 35-40.10. Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN`95-International Conference on Neural Networks (Vol. 4, pp. 1942-1948). IEEE.11. Markowitz, H. (1959). Portfolio Selection: Efficient Diversification of Investments (Vol. 16). New York: John Wiley.12. Michaud, R. O. (1989). The Markowitz optimization enigma: Is ‘optimized’optimal? Financial Analysts Journal, 45(1), 31-42.13. Nofsinger, J., & Varma, A. (2014). Socially responsible funds and market crises. Journal of Banking & Finance, 48, 180-193.14. Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications, 42(1), 259-268.15. Ratnaweera, A., Halgamuge, S. K., & Watson, H. C. (2004). Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on Evolutionary Computation, 8(3), 240-255.16. Renneboog, L., Ter Horst, J., & Zhang, C. (2008). The price of ethics and stakeholder governance: The performance of socially responsible mutual funds. Journal of Corporate Finance, 14(3), 302-322.17. Shi, Y., & Eberhart, R. (1998, May). A modified particle swarm optimizer. In 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No. 98TH8360) (pp. 69-73). IEEE.18. Verheyden, T., Eccles, R. G., & Feiner, A. (2016). ESG for all? The impact of ESG screening on return, risk, and diversification. Journal of Applied Corporate Finance, 28(2),47-55.19. Zhongbin, Z., & Jinwu, F. (2019, December). Empirical research about quantitative stock picking based on machine learning. In 2019 International Conference on Economic Management and Cultural Industry (ICEMCI 2019) (pp. 138-141). Atlantis Press. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202100788 en_US
