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    題名: Optimizing Portfolios with ESG, Dividends, and Volatility Factors via Machine Learning
    作者: 張興華
    Chang, Hsing-Hua;Lai, Chen-Hsin;Lin, Kuen-Liang;Lin, Shih-Kuei
    貢獻者: 金融系
    日期: 2024-04
    上傳時間: 2024-06-12 14:00:05 (UTC+8)
    摘要: Factor investment is booming in global asset management, especially environmental, social, and governance (ESG), dividend yield, and volatility factors. In this chapter, we use data from the US securities market from 2003 to 2019 to predict dividends and volatility factors through machine learning and historical data–based methods. After that, we utilize particle swarm optimization to construct the Markowitz portfolio with limits on the number of assets and weight restrictions. The empirical results show that that the prediction ability using XGBoost is superior to the historical factor investment method. Moreover, the investment performance of our portfolio with ESG, high-yield, and low-volatility factors outperforms baseline methods, especially the S&P 500 ETF.
    關聯: Advances in Pacific Basin Business, Economics and Finance, Vol.12, pp.193-214
    資料類型: book/chapter
    ISBN: 9781837538652
    DOI 連結: https://doi.org/10.1108/S2514-465020240000012008
    DOI: 10.1108/S2514-465020240000012008
    顯示於類別:[金融學系] 專書/專書篇章

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