AI-driven Strategies in Econometrics and Finance

Artificial intelligence/machine learning (AI/ML) has driven rapid development of theory, methodology, and techniques in econometrics and finance. It powers applications like credit scoring, fraud detection, algorithmic trading, portfolio management, regulatory compliance and customer service. This Research Topic aims to highlight the significance and impact of AI/ML, showcasing recent advancements in financial data analysis, modeling, learning, and practical implementation. Topics include, but are not limited to, AI/ML-driven methods and techniques in the following areas:1. Dimension reduction and compression techniques for large-scale financial data2. The valuation of options with diffusion processes3. Distributed/parallel computing for financial data4. Asset pricing, portfolio choice, risk management, and pension finance5. Algorithms in financial markets, high-frequency trading, and complex investment strategiesSubmissions that combine theoretical innovations with practical data analysis or case studies in real-world financial applications are particularly encouraged. Topic editors: Jiancheng Jiang, Daniel J Henderson, Pankaj Maskara, PhD, CFA, Weidong Tian Source: https://www.frontiersin.org/research-topics/73765/ai-driven-strategies-in-econometrics-and-finance