The Journal of Finance and Data Science (JFDS) is the leading interdisciplinary journal on finance and data science, providing detailed analyses of theoretical and empirical foundations and their applications in financial economics.

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

• Fintech

• Machine-learning, high-frequency trading for algorithm trading in finance

• Theoretical and empirical financial results from the perspective of data science

• Current practical treatments for data science in financial economics

• Innovative designs and techniques for computer hardware and software for finance

• Applications of theoretical results for real-world problems related to data science

• Illustrations and rigorous analyses of essential innovations in data science for financial economics

• New financial products modeled by data science

• Case studies on the industry of financial services, financial economics related to data science, and traditional financial research areas

Editorial Board