Econophysics, Financial Markets, and Artificial Intelligence
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Financial Mathematics".
Deadline for manuscript submissions: 31 March 2025 | Viewed by 1013
Special Issue Editor
Interests: complexity and chaos; econophysics; nonlinear models; multiagent systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Econophysics emerged at the beginning of this century as a beacon of innovation in the frontier between statistical physics and economy in a context where conventional economic theories struggle to explain the complexities of global markets. Through blending the principles of physics with the dynamics of financial systems, many different ideas and models have been proposed to unlock the hidden laws governing economic phenomena. These mathematical models delve into the intricate interactions between millions of agents, uncovering underlying patterns and universal principles through computational simulations and also analytical calculations. These new research approaches to the market have revealed surprising insights into how markets can decay to statistical equilibrium and the systemic risks that can generate new behaviors and different statistical wealth distributions. Yet, their use is not without controversy, as traditional economists clash with their unconventional approach. Today, a groundbreaking alliance has emerged between econophysics and artificial intelligence. In this Special Issue, papers that aim to reunite both worlds are welcome. By taking advantage of the power of machine learning algorithms, new ideas can emerge on how to implement intelligence into economic agents and mathematical models to delve into the dynamics of markets, attempting to unveil hidden correlations and nonlinear relationships and revolutionizing how we understand economic systems.
Prof. Dr. Ricardo Lopez-Ruiz
Guest Editor
Manuscript Submission Information
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Keywords
- econophysics
- agent-based modeling
- financial markets
- machine learning
- neural networks
- reinforcement learning
- market prediction
- algorithmic trading
- computational finance
- nonlinear dynamics
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