Emerging Methodologies in Economics and Finance

Machine Learning

Complex Networks


THE TEAM. Our research team operates within the Department of Economics, Democritus University of Thrace, Greece. Our research efforts were funded by a Research Grant from the European Union (Research Funding Program THALES) under the title “Study and Forecasting of Economic Data Using Machine Learning”, MIS 380292. Also, two PhD candidates are funded for their research from Hellenic Foundation for Research & Innovation (H.F.R.I.) and State Scholarship Foundation (ΙΚΥ).

MEMBERS. The research team is led by associate professors Periklis Gogas an economist (B.A., M.A., Ph.D.) and Theophilos Papadimitriou a mathematician (B.A.) and electrical engineer (M.Sc., Ph.D). Two Post Docs and six PhD candidates are actively working for the team.

RESEARCH INTERESTS. Our group's research interests include both classic Econometrics and also emerging methodologies of as they are applied to Economics and Finance. We currently work with: a) Machine Learning: Support Vector Machines for Classification and Regression and Deep Learning Architectures and b) Complex Networks: Threshold – Minimum Dominating Set, Weighted Dominating Set, and Multivariate Networks.


Dr. Periklis Gogas, Associate Professor

He was born in Thessaloniki, Greece in 1969. He received his Ph.D. degree from the Department of Economics of the University of Calgary and his Master’s degree from the University of Saskatchewan. His B.A. in Economics is from the University of Macedonia, Greece. Recently, a Visiting Scholar in Finance at the Ross School of Business of the University of Michigan. He also taught in the past at Plovdiv University, and the vocational center of the Athens Stock Exchange. His research interests include macroeconomics, financial economics, chaotic and non-linear dynamics graph theory and machine learning applied to macro and finance. He served in the past at the position of the Financial Director of a large Greek multinational corporation. He authored more than 40 articles in journals such as Journal of Money Credit and Banking, Journal of Banking and Finance, Economic Modeling, Journal of Forecasting, International Finance, Computational Economics, Open Economies Review, etc.

Dr. Theophilos Papadimitriou, Professor

He was born in Thessaloniki, Greece, in 1972. He received the Diploma degree (B.Sc.) from the Department of Mathematics, Aristotle University of Thessaloniki, Greece, and the D.E.A. A.R.A.V.I.S (Automatique, Robotique, Algorithmique, Vision, Image, Signale) degree (M.Sc.) from the Department of Computer Science, University of Nice-Sophia Antipolis, France, in 1996 and the Ph.D. degree from the School of Engineering, Aristotle University of Thessaloniki, Greece in 2000. In 2001, he joined the Department of Economics of the Democritus University of Thrace in Komotini, Greece, where he served as a lecturer (2002-2008), assistant professor (2008-2013) and associate professor (2013-2018). Currently he holds the position of Professor in the same department. Dr. Papadimitriou co-authored more than 80 journal papers, conference papers and book chapters combined. He served as a reviewer for various publications and as a member to scientific committees for Conferences and Workshops. Theophilos Papadimitriou current research interests include complex network, machine learning, and data analysis.

Post Docs: Vasileios Plakandaras, Efthimia Chrysanthidou

Ph.D. Students: Efthimios Stathakis, Maria-Artemis Matthaiou, Anna Agrapetidou, Athanasiou Athanasios-Fotios, Emmanouil Sofianos, Fotios Gkatzoglou

A gif summarizing our most recent research.🌿 Forecasting bank failures in the US banking system🌿 All 5818 US banks from 2000 to 2018🌿 Year by year classification🌿 Methodology: Machine Learning, Support Vector Machines🌿 Healthy banks on the right🌿 Failed banks on the left🌿 The yellow line is the separator🌿 Forecasting accuracy 99.22%🌿 Most important predictors: a) Tier 1 risk based capital and b) Total interest expense over total interest income🌿 The whole US bank cloud moves towards increased resilience#classification #research #forecasting #banks #machinelearning #svm