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| Title: | 考慮ESG因子之AI投資策略
AI investment strategies considering ESG factors |
| Authors: | 吳柏賢
Wu, Po-Hsien |
| Contributors: | 楊曉文
黃泓智
Yang, Sheau-Wen
Huang, Hong-Chih
吳柏賢
Wu, Po-Hsien |
| Keywords: | 特徵篩選
機器學習
投資組合
ESG
集成學習
Feature Selection
Machine Learning
Investment Portfolio
ESG
Ensemble Learning |
| Date: | 2024 |
| Issue Date: | 2025-01-02 11:40:24 (UTC+8) |
| Abstract: | 最近幾年，ESG 在⾦融市場是個熱⾨的話題，許多⾦融機構想著如何在投資的過程中納⼊永續相關的概念，以促進公司的發展以及獲得好的報酬。因此，本研究針對市場上現有的 ESG 相關因⼦結合公司財務⽐率，進⾏特徵篩選，選出對於股價有解釋能⼒的變數。本⽂選⽤的⽅法為主成份分析、套索回歸和基因演算法這三種模型，對三組變數分別篩選。再來是運⽤機器學習模型，為隨機森林、⾧短期記憶模型、⽀撐向量回歸、集成學習法和極限學習機這五種，分別進⾏預測下⼀期股票價格。本⽂會根據預測報酬率進⾏排名，選出排前幾名的股票納⼊投資組合內。最後，觀察其績效表現。研究結果表明，集成學習法在所有模型中表現最為出⾊，具有精準的預測能⼒，相⽐於另外四個機器學習模型產⽣之投資組合是能有效產⽣⾼報酬和低⾵險。除此之外，發現當投資組合考慮 ESG 相關因⼦時，能產⽣⾼報酬和低⾵險，以及展現良好的抗跌能⼒。本⽂認為有助於幫助投資⼈做決策，未來需關注的不只是企業獲利相關能⼒，還需考慮到企業在永續⽅⾯的作為。
In recent years, ESG (Environmental, Social, and Governance) has become a hot topic in the financial market, with many financial institutions considering how to incorporate sustainability-related concepts into the investment process to promote corporate development and achieve good returns. Therefore, this study focuses on the existing ESG-related factors in the market combined with company financial ratios, conducting feature selection to identify variables that can explain stock prices. The methods used in this paper include Principal Component Analysis (PCA), Lasso Regression, and Genetic Algorithms to select features from three sets of variables. Subsequently, machine learning models, including Random Forest, Long Short-Term Memory (LSTM), Support Vector Regression (SVR), Ensemble Learning, and Extreme Learning Machine (ELM), are employed to predict stock prices in the next period. This study ranks stocks based on predicted returns and selects the top-ranking stocks to form an investment portfolio. Finally, the performance of the portfolio is observed. The results indicate that the Ensemble Learning method outperforms all other models, demonstrating precise predictive capabilities and effectively generating high returns and low risk compared to portfolios generated by the other four machine learning models. Moreover, it is found that when the investment portfolio considers ESG-related factors, it can generate high returns, low risk, and exhibit good resistance to downturns. This paper suggests that in the future investors should not only focus on a company's profitability but also consider its sustainability efforts. |
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| Description: | 碩士
國立政治大學
金融學系
111352020 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0111352020 |
| Data Type: | thesis |
| Appears in Collections: | [金融學系] 學位論文
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