An Examination of AI-powered Exchange Traded Fund Returns and Their Exposure to ESG
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2024
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Haverford College. Department of Economics
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Thesis
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Award
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eng
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Haverford users only
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Abstract
This thesis analyzes whether AI-powered exchange-traded funds invest in ESG-responsible companies at a higher rate than traditionally managed ETFs. Subsequently, the paper also examines if these same AI-powered funds generate higher excess returns than traditionally managed ETFs. This paper uses performance and ESG score data from Refinitiv. The results do not provide statistically significant evidence suggesting AI-powered funds outperform or possess higher ESG scores than traditional funds. However, the paper produces significant results analyzing the effect of different ESG scores on indexed performance, alpha, and the Sharpe and Treynor ratios for non-AI-powered funds. The findings reveal differing conclusions on the effect that ESG metrics have on the performance of non-AI-powered funds.