Selected Publications

Machine Learning

2024 (3)

• "Tourism and Uncertainty: A Machine Learning Approach", with A. Dimitriadou and T. Papadimitriou, Current Issues in Tourism, forthcoming, Impact Factor 8.0. https://doi.org/10.1080/13683500.2024.2370380

• “Fuel Price Networks in the EU”, with F. Gkatzoglou and T. Papadimitriou, Economies, Impact Factor 2.6, 2024, 12, 102. https://doi.org/10.3390/economies12050102.

• “Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms”, with E. Sofianos, E. Zaganidis, T. Papadimitriou, Energies, 7, 1296, Impact Factor 3.2, https://doi.org/10.3390/en17061296.

2023 (5)

• "Machine Learning in Forecasting Motor Insurance Claims", with T. Poufinas, E. Zaganidis and T. Papadimitriou, Risks, 11, 164, Impact Factor 2.2. https://doi.org/10.3390/risks11090164

• “Forecasting Inflation Spikes with Machine Learning”, with Emmanouil Sofianos and Theophilos Papadimitriou, Encyclopedia of Monetary Policy, Financial Markets and Banking, Elsevier, forthcoming. http://dx.doi.org/10.2139/ssrn.4610424

• “Cryptocurrencies and Long-Range Trends”, with Monica Alexiadou, Emmanouil Sofianos, Periklis Gogas and Theophilos Papadimitriou, International Journal of Financial Studies, 11, 40, Impact Factor 2.3. https://doi.org/10.3390/ijfs11010040

• “Bitcoin, Sentiment Analysis, and the Efficient Market Hypothesis, a Machine Learning Approach”, with G. Toulias, E. Sofianos, Empirical Economics Letters, ABDA C, IF 0.91, Volume 22(1), January 2023. https://dx.doi.org/10.2139/ssrn.4610497

• “Banking Sector Resilience to Major Crises”, with T. Lefkou and E. Sofianos, Journal of Finance and Economics, forthcoming. https://doi.org/10.12691/jfe-11-1-1 

2022 (7)

• “The Convergence Evolution in Europe from a Complex Networks Perspective”, with T. Papadimitriou and F. Gkatzoglou, Journal of Risk and Financial Management, 15(6), 2022, https://doi.org/10.3390/jrfm15100457

• “Forecasting Bitcoin Spikes: a GARCH-SVM Approach”, with T.Papadimitriou, A.F. Athanasiou, Forecasting, 2022. https://doi.org/10.3390/forecast4040041

• “Emerging Trends in Energy Economics”, with T. Papadimitriou, Energies, 15(14), Impact Factor 3.252, 2022. https://doi.org/10.3390/en15145212

• “Forecasting Hospital Readmissions with Machine Learning”, with T. Papadimitriou, P. Micahilides and A. Dimitriadou, Healthcare, 10(6), Impact Factor 3.252, 2022. https://doi.org/10.3390/healthcare10060981

• “Credit Card Fraud Detection with Automated Machine Learning Systems”, with I. Tsamardinos, T. Papadimitriou and V. Plakandaras, Applied Artificial Intelligence, forthcoming. https://doi.org/10.1080/08839514.2022.2086354

• “Supervision of Banking Networks Using the Multivariate Threshold-Minimum Dominating Set (mT-MDS)”, with T. Papadimitriou and M.A. Matthaiou, Journal of Risk and Financial Management, 15(6), 253; https://doi.org/10.3390/jrfm15060253

• "Forecasting Unemployment in the Euro-Area with Machine Learning", with T. Papadimitriou and E. Sofianos, Journal of Forecasting, 2022, 41, 551-566. Impact Factor 3.4. https://doi.org/10.1002/for.2824

2021 (3)

• “Forecasting Natural Gas Spot Prices with Machine Learning”, with D. Mouchtaris, E. Sofianos, and T. Papadimitriou, Energies, MDPI, Impact Factor 3.252, 2021. https://doi.org/10.3390/en14185782

• “Mind the Gap: Forecasting Euro-Area Output Gaps with Machine Learning”, with E. Sofianos and T. Papadimitriou, Applied Economics Letters, Taylor & Francis, Impact Factor 1.157, 2021. https://doi.org/10.1080/13504851.2021.1963403

• “Forecasting Price Spikes in Electricity Markets”, with E. Stathakis and T. Papadimitriou, Review of Economic Analysis, vol. 13, 1, 2021. https://doi.org/10.15353/rea.v13i1.1822

2020 (6)

• “The resilience of the U.S. banking system”, with A. Agrapetidou and T. Papadimitriou, International Journal of Finance and Economics, 2020, Impact Factor 2.9. https://doi.org/10.1002/ijfe.2300

• “Forecasting Credit Ratings of EU Banks”, with V. Plakandaras and T. Papadimitriou, Efterpi Doumpa and Maria Stefanidou, International Journal of Financial Studies, 2020, 8, 49, Impact Factor 2.3. https://doi.org/10.3390/ijfs8030049

• “Gold Against the Machine”, with V. Plakandaras and T. Papadimitriou, Computational Economics, 57, 5-28, Impact Factor 1.317. https://doi.org/10.1007/s10614-020-10019-z

• "Forecasting S&P 500 Spikes: an SVM Approach", with T. Papadimitriou, A. Athanasiou, Digital Finance, Springer, 2, pages 241–258, 2020. https://doi.org/10.1007/s42521-020-00024-0

• "The evolution of the Cryptocurrencies Market: A Complex Networks approach", with T. Papadimitriou, F. Gatzoglou, Journal of Computational and Applied Mathematics, vol. 376, Impact Factor 1.883. https://doi.org/10.1016/j.cam.2020.112831

• "An AutoML Application to Forecasting Bank Failures", with A. Agrapetidou, P. Charonyktakis, I. Tsamardinos and T. Papadimitriou, Applied Economics Letters, volume 28, issue 1, Impact Factor 1.157. https://doi.org/10.1080/13504851.2020.1725230

2019 (5)

• “A re-evaluation of the term spread as a leading indicator”, with V. Plakandaras, T. Papadimitriou and Rangan Gupta, International Review of Economics and Finance, forthcoming, 2019, Impact Factor 1.432. https://doi.org/10.1016/j.iref.2019.07.002

• “Forecasting transportation demand in the U.S. market”, with V. Plakandaras and T. Papadimitriou, Transportation Research Part A: Policy and Practice, Volume 126, August 2019, 195-214. Impact Factor 3.693. https://doi.org/10.1016/j.tra.2019.06.008

• "Money Neutrality, Monetary Aggregates and Machine Learning", Algorithms, with T. Papadimitriou and E. Sofianos, 12(7), July 2019. https://doi.org/10.3390/a12070137

• "Oil Market Efficiency under a Machine Learning Perspective", with A. Dimitriadou, P. Gogas, T. Papadimitriou and V. Plakandaras, Forecasting, 2019, 1, 157-168. https://doi.org/10.3390/forecast1010011

• “A re-evaluation of the Feldstein-Horioka puzzle in the Eurozone”, with V. Plakandaras, and T. Papadimitriou, Journal of Risk & Control, Volume 6(1), 2019. https://dx.doi.org/10.2139/ssrn.3275922

Complex Networks

• Gogas, P., Gupta, R., Miller, S., Papadimitriou, T. and Sarantitis, G. 2017. Income inequality: A complex network analysis of US states. Physica A: Statistical Mechanics and its Applications, 483, pp.423-437. 

• Sarantitis, G., Papadimitriou, T., and Gogas, P., 2016. A Network Analysis of the United Kingdom's Consumer Price Index. Computational Economics, forthcoming

• Gogas, P., Papadimitriou, T. and Matthaiou, M. A., 2016. Bank supervision using the Threshold-Minimum Dominating Set. Physica A: Statistical Mechanics and its Applications.

• Antonakakis, N, Gogas P, Papadimitriou T. and Sarantitis, G., 2016. International Business Cycle Synchronization since the 1870s: Evidence from a Novel Network Approach. Physica A, vol. 447, pp. 286-296.

• Papadimitriou, T., Gogas, P. and Sarantitis, G., 2014. Convergence of European Business Cycles: A Complex Networks Approach. Computational Economics, 1-23.

• Papadimitriou, T., Gogas, P. and Sarantitis, G., 2014. European Business Cycle Synchronization: a Complex Network Perspective, In Network Models in Economics and Finance, pp. 265-275. Springer International Publishing.

• Papadimitriou, T., Gogas, P. and Sarantitis, G., 2014. Business Cycle Convergence: A Survey of Methods and Models. In Mathematics without Boundaries: Surveys in Interdisciplinary Research, pp. 447-469. Springer New York. 

• Papadimitriou, T., Gogas, P. and Sarantitis, G. An analysis of the U.S. Gross State Product growth co-movement using the Minimum Dominating Set. In Proc. of GlobalSIP-2013. Austin, USA, December, 2013, pp. 1145. 

• Gogas, P., Papadimitriou, T. and Sarantitis, G., 2013. Testing purchasing power parity in a DFA rolling Hurst framework: the case of 23 OECD countries. Applied Financial Economics, vol. 23 (17), pp. 1399-1406.