Journal of Financial Data Science - With intelligence | 2024 Cite Score: 3.3 | Q2

About this Journal:
International peer-reviewed journal focusing on the intersection of data science, machine learning and quantitative finance.The journal represents both an acknowledgment of, and embracing of, the increasing role that data science and related fields of inquiry play in the financial industry and quantitative asset management. It covers research activity in the areas of data science⁠—big data analytics, artificial intelligence, machine learning, and cognate areas⁠—as they are applied in asset management. Within the journal, you will find high-quality articles from industry-leading experts in the field. The new ideas the articles present aim to challenge the traditional way of thinking about finance, the economy, and investing; providing readers with actionable solutions to the practical problems faced by the investment management community.
Publishes research articles, methodological papers, case studies, and practitioner-oriented pieces that apply modern data-science techniques to financial problems.
Targets both academic researchers and industry practitioners (quantitative analysts, portfolio managers, risk professionals).
Indexed in Scopus and appears on multiple bibliometrics/tracking services; citation metrics vary by source and year.

Scope of the Journal:
Machine learning and AI methods for asset pricing, risk modelling, portfolio construction, and trading strategies.
Statistical methods, robustness, multiple-testing corrections, and reproducibility in empirical finance.
High-frequency and market-microstructure studies using large datasets and modern computation.
Alternative data, natural language processing for finance, and explainable AI applications in financial decision making.
Practical/industry case studies, software/tool descriptions, and tutorials bridging research and practice.