A strategic analysis of AI-driven ETFs and traditional ETFs with empirical evidence from 2020–2024
Keywords:
AI-driven ETFs, Traditional ETFs, Strategic Analysis, Interaction Terms, Risk-adjusted PerformanceAbstract
This study examines the strategic performance of exchange-traded funds (ETFs) driven by artificial intelligence (AI) compared with traditional ETFs, using data from funds listed in the United States market. The analysis is conducted within the economic context of 2020–2024, a period characterized by high uncertainty, rapid policy shifts, interest rate fluctuations, and liquidity changes. The research objectives are threefold: (1) to compare the returns and risks of AI-driven and traditional ETFs, (2) to investigate the impact of both single factors and interaction effects on investment performance—particularly the interplay between investment costs and risk, as well as between momentum signals and returns, and (3) to introduce the ISARM+ model, which incorporates logical relationships among financial variables to capture complex market dynamics.
The findings, based on monthly data from four representative ETFs, indicate that AI-driven funds outperform traditional funds in terms of risk-adjusted returns, especially during periods of volatility and policy shifts. Investment costs and their interaction with risk play a significant role in shaping excess returns, while momentum signals strongly influence return patterns. In addition, market liquidity is closely related to the balance between return and risk. The model employed provides statistically reliable evidence, reinforcing the strategic conclusion that AI-driven ETFs with interaction-aware logic are more capable of adapting their strategies to market conditions than their traditional counterparts.
References
Acharya, V. V., & Pedersen, L. H. (2005). Asset pricing with liquidity risk. Journal of Financial Economics, 77(2), 375–410. https://doi.org/10.1016/j.jfineco.2004.06.007
Amihud, Y. (2002). Illiquidity and stock returns: Cross-section and time-series effects. Journal of Financial Markets, 5(1), 31–56. https://doi.org/10.1016/S1386-4181(01)00024-6
Barredo Arrieta, A., Díaz‑Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
Barroso, P., & Santa‑Clara, P. (2015). Momentum has its moments. Journal of Financial Economics, 116(1), 111–120. https://doi.org/10.1016/j.jfineco.2014.11.010
Bertani, F., Ponta, L., Raberto, M., Teglio, A., & Cincotti, S. (2021). The complexity of the intangible digital economy: An agent-based model. Journal of Business Research, 129, 527–540. https://doi.org/10.1016/j.jbusres.2020.03.041
Carhart, M. M. (1997). On persistence in mutual fund performance. The Journal of Finance, 52(1), 57–82. https://doi.org/10.1111/j.1540-6261.1997.tb03808.x
Cho, J., Lee, G. H., Lee, W., & Kim, B. (2023). Machine learning approach for predicting U.S. ETFs’ tracking errors–Implications on U.S. invested fund. http://dx.doi.org/10.2139/ssrn.4726993
Chow, G., Jacquier, E., Kritzman, M., & Lowry, K. (1999). Optimal portfolios in good times and bad. Financial Analysts Journal, 55(3), 65–73. https://doi.org/10.2469/faj.v55.n3.2273
Cilingiroglu, E. (2023). Artificial intelligence in the stock market: Quantitative technical analysis, model weight optimization, and financial sentiment evaluation to predict stock prices. Intersect: The Stanford Journal of Science, Technology, and Society, 17(1), 1-19. https://ojs.stanford.edu/ojs/index.php/intersect/article/view/3031/
DeMiguel, V., Garlappi, L., Nogales, F. J., & Uppal, R. (2009). A generalized approach to portfolio optimization: Improving performance by constraining portfolio norms. Journal of Management Science, 107, 592-606.
Enke, B., Schwerter, F., & Zimmermann, F. (2024). Associative memory, beliefs and market interactions. Journal of Financial Economics, 157, Article 103853. https://doi.org/10.1016/j.jfineco.2024.103853
Goel, A., Pasricha, P., Magris, M., & Kanniainen, J. (2022). Foundation time-series AI model for realized volatility forecasting. ArXiv. Advanced online publications. https://arxiv.org/abs/2505.11163
Gowani, R., & Kanjiani, Z. (2024). Advanced LSTM Neural networks for predicting directional changes in sector-specific ETFs using machine learning techniques. ArXiv. https://doi.org/10.48550/arXiv.2409.05778
Gudelek, M. U., Boluk, S. A., & Ozbayoglu, A. M. (2017). A deep-learning based stock-trading model with 2-D CNN trend detection. In Proceedings of the IEEE International Conference on Social Sciences and Computing (SSCI) (pp. 1-8). IEEE. https://doi.org/10.1109/SSCI.2017.8285188
Hanauer, M. X., & Windmüller, S. (2019). Enhanced momentum. Technische Universität München. https://wp.lancs.ac.uk/mhf2019/files/2019/09/MHF-2019-076-Matthias-Hanauer.pdf
Hong, M., Chen, Z., Soliman, W. M., & Zhang, K. (2024). A comparative study of LSTM, LightGBM, and autoregressive model in narrow-based ETF market prediction with multi-ticker models. In MLMI '23: Proceedings of the 6th International Conference on Machine Learning and Machine Intelligence (pp. 10-16). https://doi.org/10.1145/3635638.3635640
James, C. (2021, February). Comparative analysis of traditional vs. AI-based financial forecasting techniques. Stanford University. https://www.researchgate.net/publication/386874402_Comparative_Analysis_of_Traditional_vs_AI-Based_Financial_Forecasting_Techniques
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48(1), 65–91. https://doi.org/10.1111/j.1540-6261.1993.tb04702.x
Kacperczyk, M., Sialm, C., & Zheng, L. (2005). On the industry concentration of actively managed equity mutual funds. The Journal of Finance, 60(4), 1983–2011. https://doi.org/10.1111/j.1540-6261.2005.00785.x
Khattak, M. A., Ali, M., Azmi, W., & Rizvi, S. A. R. (2023). Digital transformation, diversification and stability: What do we know about banks? Economic Analysis and Policy, 78, 122–132. https://doi.org/10.1016/j.eap.2023.03.004
Kumari, S. (2024). AI‑enhanced portfolio management: Leveraging machine learning for optimized investment strategies in 2024. Journal of Informatics Education and Research, 4(3), 1542-1554. https://doi.org/10.52783/jier.v4i3.1487
Liew, J. K.-S., & Mayster, B. (2018). Forecasting ETFs with machine learning algorithms. The Journal of Alternative Investments, 20(3), 58–78. https://doi.org/10.3905/jai.2018.20.3.058
Lo, A. W. (2004). The adaptive markets hypothesis. The Journal of Portfolio Management, 30(5), 15–29. https://doi.org/10.3905/jpm.2004.442611
Mehta, P., Pandya, S., & Kotecha, K. (2021). Harvesting social media sentiment analysis to enhance stock market prediction using deep learning. PeerJ Computer Science, 7, Article e476. https://doi.org/10.7717/peerj-cs.476
Nyukorong, R. (2020, July). Exchange-traded funds: What you need to know? European Scientific Journal, 16(19), 1-27. https://eujournal.org/index.php/esj/article/download/13164/13297?utm_source=chatgpt.com
Pastor, L., & Stambaugh, R. F. (2003). Liquidity risk and expected stock returns. Journal of Political Economy, 111(3), 642–685. https://doi.org/10.1086/374184
Phurahong, N. (2021). The impact of intellectual capital reporting on debt capital: An empirical evidence from listed companies in the Stock Exchange of Thailand [Doctoral dissertation, Mahasarakham University]. DSpace at Mahasarakham University. http://202.28.34.124/dspace/handle123456789/1288
Pollet, J. M., & Wilson, M. (2008). How does size affect mutual fund behavior? Journal of Finance, 63(6), 2941–2969. https://doi.org/10.1111/j.1540-6261.2008.01417.x
Servaes, H., & Sigurdsson, K. (2018). The costs and benefits of performance fees in mutual funds. (ECGI - Finance Working Paper No. 588/2018; SSRN Working Paper No. 3250315). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3250315
Sharpe, W. F. (1994). The sharpe ratio. The Journal of Portfolio Management, 21(1), 49–58. https://doi.org/10.3905/jpm.1994.409501
Sutiene, K., Schwendner, P., Sipos, C., Lorenzo, L., Mirchev, M., Lameski, P., Kabasinskas, A., Tidjani, C., Ozturkkal, B., & Cerneviciene, J. (2024). Enhancing portfolio management using artificial intelligence: Literature review. Frontiers in Artificial Intelligence, 7, Article 1371502. https://doi.org/10.3389/frai.2024.1371502
Tiwari, A. K., Abakah, E. J. A., Bonsu, C. O., Karikari, N. K., & Hammoudeh, S. (2022, January). The effects of public sentiments and feelings on stock market behavior: Evidence from Australia. Journal of Economic Behavior & Organization, 193, 443-472. https://doi.org/10.1016/j.jebo.2021.11.026
Truyols-Pont, M. A., Bilbao-Terol, A., & Arenas-Parra, M. (2024). Machine learning for sustainable portfolio optimization applied to a water market. Mathematics, 12(24), Article 3975. https://doi.org/10.3390/math12243975
Vuorela, K. (2024). Assessing the impact of AI-managed ETFs on investment performance and risk compared to benchmark index [Master’s thesis, LUT University]. LUT University. https://urn.fi/URN:NBN:fi-fe202501031204
Zhu, X., Xu, Y., & Liu, T., Sun, J., Zhang, Y., & Tong, X. (2025). Intelligent interaction strategies for context-aware cognitive augmentation. In Proceedings of the 2025 ACM CHI Workshop on Human-AI Interaction for Augmented Reasoning. https://doi.org/10.48550/arXiv.2504.13684
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Dhurakij Pundit University
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Content and information of the article published at Suthiparithat Journal are based on the sole opinions and responsibility of author(s) only. Neither the editorial board involve in......
