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政大機構典藏 > 商學院 > 金融學系 > 專書/專書篇章 > Item 140.119/151656

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/151656

Title:	Optimizing Portfolios with ESG, Dividends, and Volatility Factors via Machine Learning
Authors:	張興華 Chang, Hsing-Hua;Lai, Chen-Hsin;Lin, Kuen-Liang;Lin, Shih-Kuei
Contributors:	金融系
Date:	2024-04
Issue Date:	2024-06-12 14:00:05 (UTC+8)
Abstract:	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.
Relation:	Advances in Pacific Basin Business, Economics and Finance, Vol.12, pp.193-214
Data Type:	book/chapter
ISBN:	9781837538652
DOI 連結:	https://doi.org/10.1108/S2514-465020240000012008
DOI:	10.1108/S2514-465020240000012008
Appears in Collections:	[金融學系] 專書/專書篇章

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