Intelligent Financial Systems: Algorithms, Learning, and Decision Mechanisms

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E5: Financial Mathematics".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 2808

Special Issue Editors

School of Business, Singapore University of Social Sciences, 463 Clementi Road, Singapore 599494, Singapore
Interests: applied artificial intelligence; machine learning; deep learning; fintech; mathematical modeling; finance

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Guest Editor
School of Business, Singapore University of Social Sciences (SUSS), Singapore 599494, Singapore
Interests: multi-criteria decision-making; system-level optimisation; surrogate modelling; AI-enabled analytics; ESG-XAI; modelling of system trade-offs
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Business, Singapore University of Social Sciences, 463 Clementi Road, Singapore 599494, Singapore
Interests: stock price crash risk; corporate hedging; options trading; financial maket; fintech; ESG

Special Issue Information

Dear Colleagues,

Accelerating progress in machine learning, deep learning, and generative AI is transforming the financial sector across trading, portfolio management, banking, payments, and insurance. This Special Issue invites submissions on AI in finance that combine methodological rigor with demonstrable impact. Submissions may include deployed or near-deployment applications, high-fidelity simulations, and industry–academic collaborations; works addressing governance and assurance (e.g., robustness, fairness, privacy, and accountability); and surveys that consolidate current practice and identify research frontiers.

Dr. Qinxu Ding
Dr. Zhiyuan Wang
Dr. Chongwu Xia
Guest Editors

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Keywords

  • AI and finance
  • financial time series
  • machine learning
  • deep learning
  • neural networks
  • large language models
  • retrieval-augmented generation
  • AI agents
  • graph learning
  • reinforcement learning
  • causal inference
  • forecasting
  • anomaly detection
  • portfolio optimization
  • risk management
  • algorithmic trading
  • sentiment analysis
  • text mining
  • data mining
  • natural language processing
  • explainability
  • DeFi
  • crypto
  • financial services
  • corporate finance
  • accounting
  • ESG
  • green finance
  • decision-making
  • optimization
  • financial accounting

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Published Papers (1 paper)

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Research

36 pages, 2388 KB  
Article
Optimizing Crypto-Trading Performance: A Comparative Analysis of Innovative Reward Functions in Reinforcement Learning Models
by Ergashevich Halimjon Khujamatov, Kobuljon Ismanov, Oybek Usmankulovich Mallaev and Otabek Sattarov
Mathematics 2026, 14(5), 794; https://doi.org/10.3390/math14050794 - 26 Feb 2026
Viewed by 2478
Abstract
Cryptocurrency trading presents significant challenges due to extreme market volatility, rapid regime transitions, and non-stationary dynamics that render traditional trading strategies ineffective. Existing reinforcement learning approaches for cryptocurrency trading typically employ simplistic profit-based reward functions that fail to adequately capture risk management considerations, [...] Read more.
Cryptocurrency trading presents significant challenges due to extreme market volatility, rapid regime transitions, and non-stationary dynamics that render traditional trading strategies ineffective. Existing reinforcement learning approaches for cryptocurrency trading typically employ simplistic profit-based reward functions that fail to adequately capture risk management considerations, market microstructure costs, temporal dependencies, and regime-specific optimal behaviors. This limitation often results in strategies that perform well during favorable market conditions but suffer catastrophic losses during downturns. This paper introduces five novel reward functions grounded in economic utility theory, market microstructure, behavioral finance, adaptive risk management, and regime-conditional optimization. We systematically evaluate these reward functions across three reinforcement learning algorithms (Deep Q-Network, Proximal Policy Optimization, and Advantage Actor–Critic) and four distinct market regimes (bull, bear, high volatility, and recovery), using Bitcoin hourly data from 2018–2022. Our comprehensive experimental evaluation demonstrates that the Adaptive Risk Control reward function achieves exceptional performance, with a Sharpe ratio of 2.47, cumulative return of 26.4%, and maximum drawdown of only 16.8% during the predominantly bearish 2022 test period. Critically, regime-specific analysis reveals substantial performance heterogeneity: Adaptive Risk Control excels during high volatility (Sharpe ratio 3.21), while Temporal Coherence and Asymmetric Market-Conditional rewards dominate in trending and bear markets, respectively. These findings establish that sophisticated, theory-grounded reward engineering—rather than algorithmic innovations alone—constitutes the primary lever for improving RL trading systems, enabling positive risk-adjusted returns even during severe market downturns. Full article
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