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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/136361


    Title: 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
    Authors: 賴晨心
    Lai, Chen-Hsin
    Contributors: 林士貴
    Lin, Shih-Kuei
    賴晨心
    Lai, Chen-Hsin
    Keywords: 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
    Date: 2021
    Issue Date: 2021-08-04 14:51:17 (UTC+8)
    Abstract: 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.
    Reference: 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.
    Description: 碩士
    國立政治大學
    金融學系
    108352020
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0108352020
    Data Type: thesis
    DOI: 10.6814/NCCU202100788
    Appears in Collections:[Department of Money and Banking] Theses

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