Mathematical and Statistical Approaches in Quantitative Finance with Applications in AI, Machine Learning, and Reinforcement Learning
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E5: Financial Mathematics".
Deadline for manuscript submissions: 31 January 2026 | Viewed by 106
Special Issue Editor
Interests: asset pricing; statistical analysis; applied statistics; stochastic processes; risk management; financial risk management; financial econometrics; portfolio management; R programming
Special Issue Information
Dear Colleagues,
This Special Issue aims to explore the synergy between advanced mathematical finance, statistical methodologies, and modern computational approaches in the ever-evolving field of quantitative finance. As financial markets become increasingly complex and data-driven, techniques such as artificial intelligence, machine learning, and reinforcement learning are redefining how we model, analyze, and predict financial phenomena. We welcome contributions that integrate mathematical finance frameworks, including stochastic processes, partial differential equations, and risk-neutral valuation, with AI-driven methodologies to address challenges related to portfolio optimization, algorithmic trading, risk management, and derivative pricing. We also welcome submissions that highlight theoretical advancements or real-world applications, such as Bayesian modeling, deep learning for financial prediction, and reinforcement learning for decision-making in dynamic markets. By integrating classical mathematical finance with novel technologies, this Special Issue seeks to foster innovation and provide a comprehensive platform for advancing quantitative finance in the age of intelligent systems.
Dr. Enrique ter Horst
Guest Editor
Manuscript Submission Information
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Keywords
- mathematical finance
- statistical methodologies
- computational approaches
- AI, machine learning, and reinforcement learning
- quantitative finance
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