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From Classical Rationality to Contextual Reasoning: Quantum Logic as a New Frontier for Human-Centric AI in Finance

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  • Fabio Bagarello
  • Francesco Gargano
  • Polina Khrennikova

Abstract

We consider state of the art applications of artificial intelligence (AI) in modelling human financial expectations and explore the potential of quantum logic to drive future advancements in this field. This analysis highlights the application of machine learning techniques, including reinforcement learning and deep neural networks, in financial statement analysis, algorithmic trading, portfolio management, and robo-advisory services. We further discuss the emergence and progress of quantum machine learning (QML) and advocate for broader exploration of the advantages provided by quantum-inspired neural networks.

Suggested Citation

  • Fabio Bagarello & Francesco Gargano & Polina Khrennikova, 2025. "From Classical Rationality to Contextual Reasoning: Quantum Logic as a New Frontier for Human-Centric AI in Finance," Papers 2510.05475, arXiv.org.
    • Handle: RePEc:arx:papers:2510.05475
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    References listed on IDEAS

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    1. Bagarello, F., 2007. "Stock markets and quantum dynamics: A second quantized description," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 386(1), pages 283-302.
    2. Thomas Langer & Martin Weber, 2001. "Prospect Theory, Mental Accounting, and Differences in Aggregated and Segregated Evaluation of Lottery Portfolios," Management Science, INFORMS, vol. 47(5), pages 716-733, May.
    3. Herbert A. Simon, 1955. "A Behavioral Model of Rational Choice," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 69(1), pages 99-118.
    4. Jean‐Philippe Bouchaud & Philipp Krüger & Augustin Landier & David Thesmar, 2019. "Sticky Expectations and the Profitability Anomaly," Journal of Finance, American Finance Association, vol. 74(2), pages 639-674, April.
    5. Benjamin Enke & Florian Zimmermann, 2019. "Correlation Neglect in Belief Formation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(1), pages 313-332.
    6. Frank Arute & Kunal Arya & Ryan Babbush & Dave Bacon & Joseph C. Bardin & Rami Barends & Rupak Biswas & Sergio Boixo & Fernando G. S. L. Brandao & David A. Buell & Brian Burkett & Yu Chen & Zijun Chen, 2019. "Quantum supremacy using a programmable superconducting processor," Nature, Nature, vol. 574(7779), pages 505-510, October.
    7. Barberis, Nicholas & Shleifer, Andrei & Vishny, Robert, 1998. "A model of investor sentiment," Journal of Financial Economics, Elsevier, vol. 49(3), pages 307-343, September.
    8. John Beshears & James J. Choi & David Laibson & Brigitte C. Madrian, 2017. "Does Aggregated Returns Disclosure Increase Portfolio Risk Taking?," The Review of Financial Studies, Society for Financial Studies, vol. 30(6), pages 1971-2005.
    9. Kerstin Beer & Dmytro Bondarenko & Terry Farrelly & Tobias J. Osborne & Robert Salzmann & Daniel Scheiermann & Ramona Wolf, 2020. "Training deep quantum neural networks," Nature Communications, Nature, vol. 11(1), pages 1-6, December.
    10. Camelia M. Kuhnen, 2015. "Asymmetric Learning from Financial Information," Journal of Finance, American Finance Association, vol. 70(5), pages 2029-2062, October.
    11. Back, Camila & Morana, Stefan & Spann, Martin, 2023. "When do robo-advisors make us better investors? The impact of social design elements on investor behavior," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 103(C).
    12. Haven, Emmanuel & Khrennikova, Polina, 2018. "A quantum-probabilistic paradigm: Non-consequential reasoning and state dependence in investment choice," Journal of Mathematical Economics, Elsevier, vol. 78(C), pages 186-197.
    13. Andrew J. Daley & Immanuel Bloch & Christian Kokail & Stuart Flannigan & Natalie Pearson & Matthias Troyer & Peter Zoller, 2022. "Practical quantum advantage in quantum simulation," Nature, Nature, vol. 607(7920), pages 667-676, July.
    14. Jiang, Hao & Li, Sophia Zhengzi & Wang, Hao, 2021. "Pervasive underreaction: Evidence from high-frequency data," Journal of Financial Economics, Elsevier, vol. 141(2), pages 573-599.
    15. Will Hicks, 2024. "Information Entropy of the Financial Market: Modelling Random Processes Using Open Quantum Systems," Papers 2406.20027, arXiv.org.
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