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| Title: | 機器學習資產配置與台股ESG多因子投資組合建構
Machine Learning Asset Allocation and Construction of ESG Multi-Factor Portfolios in the Taiwan Stock Market |
| Authors: | 林浩詳
Lin, Hau-Siang |
| Contributors: | 羅秉政
Lo, PING-CHENG
林浩詳
Lin, Hau-Siang |
| Keywords: | 機器學習
超參數
資產配置最適化
多因子投資
股利率因子
獲利因子
動能因子
絕對報酬
ETF
ESG
台股
Machine learning
Hyperparameters
Asset allocation optimization
Multi-factor investment
Dividend yield factor
Profit factor
Momentum factor
Absolute return
ETF
ESG
Taiwanese stocks |
| Date: | 2024 |
| Issue Date: | 2024-03-01 12:34:34 (UTC+8) |
| Abstract: | 本研究主要探討建構台股 ESG 計量投資組合，並應用於投信絕對報酬帳戶或是相對報酬帳戶之操作策略，主要分為以下流程。第一、資產配置:應用機器學習模型預測次月股債指數報酬率與波動率，再將其進行股債效率前緣最適化投資比重配置，輸入全球總經變數、利率、匯率、股價與債券指數、原物料報價等因子 (x_t)，進行機器學習模型訓練，其中包括長短期記憶 （Long Short-Term Memory, LSTM）、循環門單元（Gate Recurrent Unit, GRU）模型。在訓練集 (2005/01至2014/12)優化超參數使股債預測漲跌幅之損失函數最小化，並檢視模型預測值與進行資產配置後績效之穩定度。並於測試集 (2015/01至2019/12)與驗證集 (2020/01至2021/12)觀察與調整。實際應用於真實帳戶 (2022/01至2023/12)。
第二，近年 ESG 議題持續受各界重視，不論是政府勞動基金針對出具企業社會責任(CSR)報告書作為可投資清單外，投信業者亦持續推出 ESG概念 ETF產品，或針對 ESG分數較高的公司，納入股票池，對於有投入 ESG公司股價已產生一定影響力，本研究發現E因子分數逐年提升之公司，將具備較高的夏普值 (Sharpe Ratio)，且在空頭市場表現也相對抗跌。本研究將比照政府勞動基金可投資清單，從中採用近年盛行於台灣指數公司所發行的多因子指數、 Smart Beta策略等方式，建構穩健投資組合，本研究觀察近年台灣股利率因子表現十分優異，若結合獲利因子與動能因子將可再提高超額報酬。
從股票配置比重與挑選股票組成採用計量化投資模式，紀律性建構穩健投資組合。
This paper primarily investigates the construction of an ESG investment portfolio for the Taiwanese stocks. The portfolio is then applied to the operational strategies of mutual funds in absolute or relative return accounts.
Asset Allocation: Utilizing machine learning models to predict the next month's stock and bond index returns and volatility. Implementing stock and bond efficiency frontier optimal investment weight allocation based on the predictions. Inputting factors(x_t)such as global macroeconomic variables, interest rates, exchange rates, stock and bond indices, commodity prices, etc., into machine learning models, including LSTM and GRU. Training the models on the training set to optimize hyperparameters and minimize the loss function for predicting stock and bond price movements. Assessing the stability of model predictions and the performance after asset allocation. ESG Impact: Highlighting the impact of ESG considerations on stock prices, as observed in the annual improvement of Environmental (E) factor scores correlating with higher Sharpe Ratios. Recognizing the resilience of companies with increasing ESG scores, even in bear markets. Adapting strategies employed by government labor funds, including the incorporation of recent prevalent multi-factor indices and Smart Beta strategies for constructing robust portfolios. Noting the outstanding performance of the dividend yield factor in the Taiwanese market and the potential for enhanced returns by combining it with profit and momentum factors. The study employs a disciplined quantitative investment approach for asset allocation and stock selection to construct a robust investment portfolio. |
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| Description: | 碩士
國立政治大學
金融學系
108352003 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0108352003 |
| Data Type: | thesis |
| Appears in Collections: | [金融學系] 學位論文
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