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| 題名 | Optimizing Portfolios with ESG, Dividends, and Volatility Factors via Machine Learning |
| 作者 | 張興華; 林士貴 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; 林士貴 | - |
| 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 | - |