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研究生: 王煥傑
Wang, Huan-Chieh
論文名稱: ESG投資在不同情境下之表現–以美國ESG ETF為例
ESG Investing’s Performance under Different Circumstances: An Examination through the Lens of US ESG ETFs
指導教授: 周冠男
Chou, Kuan-Nan
口試委員: 何耕宇
Ho, Keng-Yu
柯冠成
Ko, Kuan-Cheng
陳鴻毅
Chen, Hong-Yi
學位類別: 碩士
Master
系所名稱: 商學院 - 財務管理學系
Department of Finance
論文出版年: 2022
畢業學年度: 110
語文別: 英文
論文頁數: 38
中文關鍵詞: ETFESG投資事件研究法下檔保護超額報酬
外文關鍵詞: ETF, ESG investing, Event study, Downside protection, Excess return
DOI URL: http://doi.org/10.6814/NCCU202200603
相關次數: 點閱:764下載:0
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  • 本研究旨在探討美國ESG ETF在不同情境下的表現。本研究以新冠疫情(COVID-19)為負向事件代表、並以COP26會議為正向事件代表,透過事件研究法本研究發現ESG投資在負向事件情境下並沒有顯著的下檔保護的效果,然而其在正向事件下也未提供顯著的超額報酬。另外,在負向事件下,ETF特性中僅有「週轉率」 和累積異常報酬(cumulative abnormal return) 有顯著負相關;而在正向事件下,ETF特性中「晨星永續評級」和累積異常報酬有顯著正相關,顯示著投資人確實會將永續評級視為考慮投資的一項要素之一。整體而言,本研究結果並不支持ESG投資的下檔保護效果或者捕捉超額報酬的能力。


    This paper studies the performance of U.S. ESG ETFs under different circumstances including negative event as COVID-19 and positive event as COP26 meeting. By implementing event study methodology, this study demonstrates that U.S. ESG ETFs did not provide downside protection under negative event nor did they significantly capture excess return during positive event. Furthermore, “Turnover Ratio” had significant negative correlation with cumulative abnormal return while controlling other ESG ETFs characteristics during COVID-19 event. However, “Morningstar Sustainability Rating” did have positive correlation with cumulative abnormal return under COP26 meeting, indicating that investors did evaluate ESG ETF’s sustainability rating when investing. Overall, the result did not support the idea of downside protection nor the ability of generating excess return from ESG investing.

    1.Introduction 1
    2.Literature Review and Hypothesis Development 5
    2.1 Trend of ESG Investing 5
    2.2 ESG Investing Performance 6
    2.3 ESG Investing Results During Various Events 7
    2.4 Hypothesis Development 8
    3.Data and Sample Selection 9
    3.1 Data 9
    3.2 Event Study Methodology 12
    3.3 CAR Regression Model Construction 13
    4.Empirical Results 15
    4.1 Negative Event: COVID-19 15
    4.2 Positive Event: COP26 22
    4.3 Robustness Check 28
    5.Conclusion 32
    Reference 35

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