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研究生： 黃子芮
Zi-Ruei Huang
論文名稱： An Analysis of Ethics-augmented Meanvariance Efficient Portfolios
指導教授： 周賓凰
Pin-Huang Chou
口試委員:
學位類別： 碩士
Master
系所名稱： 管理學院 - 財務金融學系
Department of Finance
論文出版年： 2024
畢業學年度： 112
語文別： 英文
論文頁數： 35
中文關鍵詞： 效率前緣 、投資組合優化 、永續投資策略 、ESG 評等 、投組績效評估
外文關鍵詞： augmented efficient frontier, portfolio optimization, sustainable investment strategy, ESG rating score, performance evaluation
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    • 隨著全球對環境、社會和治理（ESG）問題的關注不斷增長，永續投資已成為
    許多投資策略的重中之重。企業在財務決策中納入 ESG 考量，對投資組合回報的
    影響變得極為重要。本研究旨在探討優先考慮 ESG因素是否會影響投資組合績效，
    從而幫助投資者構建更高效的永續投資組合。我們通過在傳統的平均數－變異數
    投資組合模型中引入 ESG 因子，推導出平均數-變異數-ESG 最佳投資組合的分析
    結果。利用真實數據進行情境分析後，我們發現，ESG評分越高的投資組合可以帶
    來更高的報酬，但也伴隨著更高的波動性。因此，這並未帶來更高效的投資績效。
    換言之，當投資策略中優先考慮道德因素時，會增加投資組合的績效不穩定性。

    With the growing global focus on environmental, social, and governance (ESG)
    issues, sustainable investing is becoming a key strategy for many investors. As companies incorporate ESG considerations into their financial decisions, it is critical to understand the impact of these factors on portfolio returns. This study examines whether prioritizing ESG factors leads to better or worse portfolio performance, thereby helping investors construct effective green portfolios. By extending the traditional mean-variance portfolio model with an ESG dimension, we derive analytical results for the mean-variance ESG optimal portfolio. Our results show that the optimal portfolio is a mixture of the conventional tangent portfolio and an ESG-focused tangent portfolio. We validate these results through scenario analysis using real world data, and show that portfolios with higher ESG scores can generate higher returns, but also exhibit higher volatility

    摘要 ii Abstract iii Acknowledgments iv List of Figures vii List of Tables viii 1 Introduction 1 2 The mean-ESG-variance mathematics 4 3 Data 7 3.1 Historical constituents list of S&P 500 . . . . . . . . . . . . . . . . . 7 3.2 ESG Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.3 Stock price and factors . . . . . . . . . . . . . . . . . . . . . . . . . . 9 4 Numerical research design 10 4.1 Data preprocessing and filtering . . . . . . . . . . . . . . . . . . . . . 11 4.2 Asset pricing models . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.2.1 Market model . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.2.2 Two-factor model . . . . . . . . . . . . . . . . . . . . . . . . . 12 4.2.3 Shrinkage of covariance matrix . . . . . . . . . . . . . . . . . 15 4.3 Optimal portfolio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 5 Results 17 5.1 Number of assets picked . . . . . . . . . . . . . . . . . . . . . . . . . 17 5.2 Portfolio performance . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 5.2.1 In-sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 5.2.2 Out-sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 ii Abstract iii Acknowledgements iv List of Figures vii List of Tables viii 1 Introduction 1 2 The mean-ESG-variance mathematics 4 3 Data 7 3.1 Historical constituents list of S&P 500 . . . . . . . . . . . . . . . . . 7 3.2 ESG Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.3 Stock price and factors . . . . . . . . . . . . . . . . . . . . . . . . . . 9 4 Numerical research design 10 4.1 Data preprocessing and filtering . . . . . . . . . . . . . . . . . . . . . 11 4.2 Asset pricing models . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.2.1 Market model . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.2.2 Two-factor model . . . . . . . . . . . . . . . . . . . . . . . . . 12 4.2.3 Shrinkage of covariance matrix . . . . . . . . . . . . . . . . . 15 4.3 Optimal portfolio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 5 Results 17 5.1 Number of assets picked . . . . . . . . . . . . . . . . . . . . . . . . . 17 5.2 Portfolio performance . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 5.2.1 In-sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 5.2.2 Out-sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 ii Abstract iii Acknowledgements iv List of Figures vii List of Tables viii 1 Introduction 1 2 The mean-ESG-variance mathematics 4 3 Data 7 3.1 Historical constituents list of S&P 500 . . . . . . . . . . . . . . . . . 7 3.2 ESG Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.3 Stock price and factors . . . . . . . . . . . . . . . . . . . . . . . . . . 9 4 Numerical research design 10 4.1 Data preprocessing and filtering . . . . . . . . . . . . . . . . . . . . . 11 4.2 Asset pricing models . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.2.1 Market model . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.2.2 Two-factor model . . . . . . . . . . . . . . . . . . . . . . . . . 12 4.2.3 Shrinkage of covariance matrix . . . . . . . . . . . . . . . . . 15 4.3 Optimal portfolio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 5 Results 17 5.1 Number of assets picked . . . . . . . . . . . . . . . . . . . . . . . . . 17 5.2 Portfolio performance . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 5.2.1 In-sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 5.2.2 Out-sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Appendix A: Proof of the mean-ESG-variance mathematics . . . . . . . . . . . . . . . . . . . . . . . . . . . .24 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

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