Submit a Manuscript to the Journal
Data Science in Science
For a Special Issue on
Data Science in Modern Finance
31 December 2023
Special Issue Editor(s)
Mila Getmansky Sherman,
Isenberg School of Management, University of Massachusetts Amherst
Ruey S. Tsay,
Booth School of Business, University of Chicago
Department of Statistics, Rutgers University
Management Science & Engineering, Stanford University
Department of Statistics and Data Science, Cornell University
Daniel R. Kowal,
Department of Statistics, Rice University
Data Science in Modern Finance
This Special Issue of Data Science in Science invites both original and survey manuscripts that bring together novel data science, machine learning, and AI methodology and applications in modern finance. We evaluate submissions on the basis of scientific rigor, technical depth, and ethical standards, regardless of perceived novelty. We also value high-quality papers with interesting and relevant research hypotheses and/or new datasets from previously untapped data sources (digital economy, blockchain ledgers, etc). We welcome original, unpublished, and innovative submissions including, but not limited to the following areas of research:
- Advances in financial time series, including derivative pricing, risk management, portfolio optimization, algorithmic trading, game theory, and insurance.
- Novel and emerging financial markets, e.g., climate/ESG investment, cryptocurrency, NFTs, etc.
- Applications of deep learning, AI, natural language processing, and generative models in finance and financial economics.
- Data mining and knowledge discovery for FinTech, e-commerce, electronic auctions, etc.
- Risk, volatility, and uncertainty quantification for time series analysis and/or forecasting.
- Graphs, networks, and interconnectedness of financial institutions.
- Measures of systemic risk for understanding and predicting financial crises.
- Mathematical foundations for financial data science, including advances in optimization and Bayesian methodology.
- Real-time measurement of key financial and macroeconomic indicators through public sources, e.g., social media, blockchain.
- Causal inference and observational studies in capital markets.
- Novel applications of methodology stemming from financial literature to other domains, including but not limited to economics, healthcare, and social sciences.
Mila Getmansky Sherman (UMass), Ruey S. Tsay (UChicago), Rong Chen (Rutgers), Markus Pelger (Stanford), Sumanta Basu (Cornell), Daniel R. Kowal (Rice), Rebecca Killick (Lancaster), Shawn Mankad (Cornell), Ines Wilms (Maastricht), Abolfazl Safikhani (UFlorida), Linden McBride (US Census), David S. Matteson (Cornell)
- All manuscripts must be in English and written in accordance with the "Instructions for Authors" which can be found on the Journal’s homepage.
- All submissions will be peer-reviewed and meet the same requirements and standards as that of a regular paper submission.
- Submissions must not have been previously published in other journals or conferences; submissions that have already been uploaded to preprints such as arXiv are allowed.
- Select "Data Science in Modern Finance” when submitting your paper to Data Science in Science in ScholarOne.
- If you do not have APC funding covered by your institution or funder, then discretionary waivers are available. Please email the Editor-in-Chief ([email protected]) to discuss this ahead of submitting your manuscript so a code can be provided.
- For inquiries about the Special Issue, contact the editors by e-mail.