Artificial Intelligence and the Disparities in Investor Return

Abstract

Individual investors often trail institutional ones in investor return. Artificial intelligence (AI) has been increasingly used in investing and finance sectors. However, its impact on the disparity in investor returns is unclear. We therefore discuss how and to what extent the application of AI tools exacerbates return disparities between individual and institutional investors. Literature search and review were conducted. Hypothetical drawdowns during 2020 market crisis were simulated and reported. Our data and review of literature show that AI may worsen these disparities through additional technological and psychological edges gained by institutional (versus individual) investors and large (versus smaller) institutions. To address this concern, we propose several approaches to mitigate the increasing disparities in investor return, including increasing awareness of the risks of AI-driven tools, playing defensively in the market, actions by the law makers and law enforcement agencies and fiduciary requirement of financial advisors and brokers. However, there are several exceptions to the increasing disparities that may help individual investors and those in small institutions. In summary, AI tools will likely increase the disparity in the investor return between individual and institutional investors and that between large and smaller institutions. Yet we believe that these disparities can be prevented or mitigated through collaborative efforts of the investors, public, academics, and government officials.

Keywords

artificial intelligence / machine learning / investment / investor return / wealth disparity

Author Contributions

Article conceptualization and manuscript writing (I.Z.S. and L.Z.). Both authors contributed to the writing or revision of the article and approved the final publication version.

Funding

This research received no external funding.

3Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no other conflict of interests.

Compliance with Ethical Standards

Not applicable. We used AI tools (Deepseek and ChatGPT) to collect data and generate graphs.

References

[1] Winston E. Unequal Investment: A Regulatory Case Study. Cornell L. Rev. 2021, 107, 781.

[2] Barber B.M.; Odean T. Trading is hazardous to your wealth: The common stock investment performance of individual investors. J. Financ. 2000, 55, 773-806. https://doi.org/10.1111/0022-1082.00226.

[3] Kacperczyk M.; Nosal J.; Stevens L. Investor sophistication and capital income inequality. J. Monet. Econ. 2019, 107, 18-31. https://doi.org/10.1016/j.jmoneco.2018.11.002.

[4] Barber B.M.; Odean T. The behavior of individual investors. In Handbook of the Economics of Finance; Elsevier: Amsterdam, The Netherlands, 2013. pp. 1533-1570. https://doi.org/10.1016/B978-0-44-459406-8.00022-6.

[5] Gompers P.A.; Metrick A. Institutional investors and equity prices. Q. J. Econ. 2001, 116, 229-259. https://doi.org/10.1162/003355301556392.

[6] Friesen G.C.; Sapp T.R.A. Mutual fund flows and investor returns: An empirical examination of fund investor timing ability. J. Bank. Financ. 2007, 31, 2796-2816. https://doi.org/10.1016/j.jbankfin.2007.01.024.

[7] Rehman M.; Dhiman B.; Cheema S.C. Minds and Machines: Impact of Emotional Intelligence on Investment Decisions with Mediating the Role of Artificial Intelligence. Int. J. Eng. Bus. Manag. 2024, 8, 1-10. https://doi.org/10.22161/ijebm.8.1.

[8] Ma T.; Wang W.; Jiang F. Machine learning the performance of hedge fund. J. Int. Money Financ. 2025, 155, 103332. https://doi.org/10.1016/j.jimonfin.2025.103332.

[9] Grobys K.; Kolari J.W.; Niang J. Man versus machine: On artificial intelligence and hedge funds performance. Appl. Econ. 2022, 54, 4632-4646. https://doi.org/10.1080/00036846.2022.2032585.

[10] Hoang D.; Wiegratz K. Machine learning methods in finance: Recent applications and prospects. Eur. Financ. Manag. 2023, 29, 1657-1701. https://doi.org/10.1111/eufm.12408.

[11] Aziz S.; Dowling M.; Hammami H.; et al. Machine learning in finance: A topic modeling approach. Eur. Financ. Manag. 2021, 28, 744-770. https://doi.org/10.1111/eufm.12326.

[12] Manrai R.; Gupta K.P. Investor’s perceptions on artificial intelligence (AI) technology adoption in investment services in India. J. Financ. Serv. Mark. 2023, 28, 1-14. https://doi.org/10.1057/s41264-021-00134-9.

[13] Faheem M.; Aslam M.; Kakolu S. Artificial Intelligence in Investment Portfolio Optimization: A Comparative Study of Machine Learning Algorithms. Int. J. Sci. Res. Arch. 2022, 6, 335-342. https://doi.org/10.30574/ijsra.2022.6.1.0131.

[14] Zhemerov V. The Quantitative Edge: How High-Frequency Trading and Market Making Strategies Are Revolutionizing Financial Markets. 1 May 2025. Available online: accessed on 14 June 2025).

[15] Munger C.T. Poor Charlie’s Almanack: The Essential Wit and Wisdom of Charles T. Munger; Stripe Press: San Francisco, CA, USA, 2023.

[16] Alp Coşkun E.; Kahyaoglu H.; Lau C.K.M. Which return regime induces overconfidence behavior? Artificial intelligence and a nonlinear approach. Financ. Innov. 2023, 9, 30. https://doi.org/10.1186/s40854-022-00446-2.

[17] Fein M.L. FINRA’s Report on Robo-Advisors: Fiduciary Implications. SSRN 2016. https://doi.org/10.2139/ssrn.2768295.

[18] Weber R.M. Who’s Your Fiduciary? J. Financ. Serv. Prof. 2024, 78, 24.