Manuscript Topics

Recent advances in artificial intelligence have sparked a surge of innovative research within financial markets. In particular, studies employing machine learning and generative AI have produced significant breakthroughs, offering solutions to challenges that traditional approaches have struggled to address and uncovering insights that were previously inaccessible.

The aim of this Special Issue is to bring together high-quality research that leverages machine learning and generative AI to tackle a wide range of problems in financial markets. We welcome contributions that advance methodological development, deepen our understanding of market dynamics, or provide novel applications that push the boundaries of finance and data science.

Topics of interest include (but are not limited to):

- Machine learning–based asset pricing models
(deep learning, ensemble models, hybrid architectures)
- Generative AI applications in financial forecasting
(LLMs, diffusion models, GANs for time series generation or simulation)
- Deep learning approaches to volatility modeling and risk forecasting
(LSTM/Transformer volatility models, tail-risk prediction, VaR/ES estimation)
- AI-driven portfolio optimization and asset allocation
- Market microstructure analysis using machine learning or generative AI
(order-flow prediction, liquidity modeling, high-frequency trading)
- Explainable AI (XAI) in finance
(SHAP, counterfactuals, interpretability of financial ML systems)
- Stress testing and scenario generation using generative models
- Large Language Models for financial sentiment, news analytics, and macro-interpretation
(text mining, chatbot-based forecasting, financial document analysis)
- Anomaly detection in financial markets using ML and generative AI
(fraud detection, regime shifts, structural breaks)
- Hybrid econometric–machine learning frameworks
(TVP-VAR + ML, GARCH + neural networks, wavelet–deep learning models)
- Network-based analysis of systemic risk and contagion with AI methods
- Climate finance, ESG analytics, and sustainable investing using AI techniques
- Cryptocurrency and digital-asset markets analyzed through ML and generative AI
(volatility spillovers, price discovery, risk transmission)
- Agent-based modeling enhanced with machine learning or generative AI
- Simulation, synthetic data generation, and reinforcement learning in trading strategies

Both original research articles and comprehensive reviews are welcome.

Instructions for authors
https://www.aimspress.com/nhm/news/solo-detail/instructionsforauthors
Please submit your manuscript to online submission system
https://aimspress.jams.pub/