Paper: arXiv 2510.05475

Authors: 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.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 2.0/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper explores advanced mathematical frameworks like quantum logic and quantum machine learning, indicating high theoretical density, but lacks empirical components such as backtests, datasets, or statistical results, placing it in the theoretical research category.

Research Flowchart

  flowchart TD
  R["Research Goal:<br/>Explore AI in Financial Expectations<br/>& Quantum Logic Potential"]
  D["Data & Inputs:<br/>Financial Statements<br/>Market Data<br/>Human Expectation Models"]
  M["Methodology:<br/>Classical ML (RL, DNN)<br/>+ Quantum ML Analysis"]
  C["Computational Processes:<br/>Simulation & Comparative<br/>Modeling of Financial Systems"]
  F1["Outcome 1:<br/>RL & DNN Enhance<br/>Trading & Portfolio Mgmt"]
  F2["Outcome 2:<br/>Quantum Logic Offers<br/>New Frontiers for Contextual AI"]
  R --> D
  D --> M
  M --> C
  C --> F1
  C --> F2