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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 12-六月-2024 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 https://doi.org/10.1108/S2514-465020240000012008
dc.contributor 金融系
dc.creator (作者) 張興華
dc.creator (作者) Chang, Hsing-Hua;Lai, Chen-Hsin;Lin, Kuen-Liang;Lin, Shih-Kuei
dc.date (日期) 2024-04
dc.date.accessioned 12-六月-2024 14:00:05 (UTC+8)-
dc.date.available 12-六月-2024 14:00:05 (UTC+8)-
dc.date.issued (上傳時間) 12-六月-2024 14:00:05 (UTC+8)-
dc.identifier.isbn (ISBN) 9781837538652
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/151656-
dc.description.abstract (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.
dc.format.extent 112 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Advances in Pacific Basin Business, Economics and Finance, Vol.12, pp.193-214
dc.title (題名) Optimizing Portfolios with ESG, Dividends, and Volatility Factors via Machine Learning
dc.type (資料類型) book/chapter
dc.identifier.doi (DOI) 10.1108/S2514-465020240000012008
dc.doi.uri (DOI) https://doi.org/10.1108/S2514-465020240000012008