Journal of Finance and Data Science - KeAi Communications Co | 2025 Impact Factor: 3.7 | Cite Score: 6.5 | SCOPUS Q2

About this Journal:
Peer-reviewed, open access journal at the intersection of finance and data science.JFDS publishes evaluations of both well-established and new theories using financial data, data-scientific measurements of variables relevant to financial decision-making and financial service, the econometrics of financial market data, and the development of new econometric methodologies with financial applications. JFDS also provides readers with results-oriented computer hardware and software treatments and enhanced treatment of data information for practical finance products and techniques. Big data financial economic analytics has led to new challenges and advanced state-of-the-art computer science techniques; in return, computer data science has provided indispensable techniques for financial analysis. JFDS includes articles on, Big data and quantitative analysis in finance, accounting, and financial economics,FintechMachine-learning, high-frequency trading for algorithm trading in finance
Publishes theoretical and empirical research emphasizing data-driven methods for financial economics and applications (machine learning, forecasting, risk, market microstructure, etc.).
Targets researchers working on predictive analytics, algorithmic trading, fintech, asset pricing with data-intensive methods, and related fields.
Indexed in major databases (Scopus, DOAJ; recently added to ESCI/Web of Science coverage announcements have appeared).

Scope of the Journal:
Machine learning and AI methods applied to finance (prediction, classification, anomaly detection).
Financial econometrics, time-series forecasting, and big-data approaches for markets.
Market microstructure, high-frequency data analysis, and algorithmic trading studies.
Risk management, portfolio optimization, and asset pricing using data-driven models.
Fintech, blockchain/crypto asset analysis, and computational methods for financial systems.
Methodological contributions (feature engineering, model interpretability, evaluation metrics) relevant to financial data science.