Forecasting ETF Performance: A Comparative Study of Deep Learning Models and the Fama-French Three-Factor Model

Kuang-Hsun Shih, Yi-Hsien Wang (), I-Chen Kao and Fu-Ming Lai
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Kuang-Hsun Shih: Doctorate Program in Intelligent Banking and Finance, CTBC Business School, Tainan 709, Taiwan
Yi-Hsien Wang: Doctorate Program in Intelligent Banking and Finance, CTBC Business School, Tainan 709, Taiwan
I-Chen Kao: Department of Banking and Finance, Chinese Culture University, Taipei 111396, Taiwan
Fu-Ming Lai: Doctorate Program in Intelligent Banking and Finance, CTBC Business School, Tainan 709, Taiwan

Mathematics, 2024, vol. 12, issue 19, 1-15

Abstract: The global financial landscape has witnessed a significant shift towards Exchange-Traded Funds (ETFs), with their market capitalization surpassing USD 10 trillion in 2023, due to advantages such as low management fees, high liquidity, and broad market exposure. As ETFs become increasingly central to investment strategies, accurately forecasting their performance has become crucial. This study addresses this need by comparing the efficacy of deep learning models against the traditional Fama-French three-factor model in predicting daily ETF returns. The methodology employs eight artificial neural network architectures, including ANN, LSTM, GRU, CNN, and their variants, implemented in Python and applied to data ranging from 2010 to 2020, while also exploring the impact of additional factors on forecast accuracy. Empirical results reveal that LSTM and the Fama-French three-factor model exhibit a superior performance in ETF return prediction. This study contributes to the literature on financial forecasting and offers practical insights into investment decision making. By leveraging advanced artificial intelligence techniques, this study aims to enhance the toolkit available for ETF performance analysis, potentially improving investment strategies in this dynamic market segment.

Keywords: ETFs; deep learning; Fama-French three-factor model; return forecasting; neural networks; global investment trends (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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