Publications

Machine Learning

2026 (4)

• “Using DSGE and Machine Learning to Forecast Public Debt for France”, with E. Sofianos, T. Betti, T. Papadimitriou and A. Barbier-Gauchard, Journal of Forecasting, 45(5), 2173-2185, 2026. https://doi.org/10.1002/for.70144

• “From Prediction to Insight: Understanding Drivers of UK Tourism Demand with Machine Learning”, with A. Dimitriadou and T. Papadimitriou, Economies, 14(4), 141, 2026. https://doi.org/10.3390/economies14040141

• “Sovereign Ratings and Risk Pricing, Agency Divergences in the European Union”, with A. Afonso, J. Alves and T. Papadimitriou, CESifo Working Paper No. 12832, 2026. https://doi.org/10.65864/rci08arf8l

• “4Cast Lab: Open-Source App for Machine Learning & Statistical Analysis”, P. Gogas, arXiv preprint arXiv:2610.16800, 2026.

2025 (5)

• “Do International Reserve Holdings Still Predict Economic Crises? Insights from Recent Machine Learning Techniques”, with T. Papadimitriou, E. Sofianos, N. Giannakis and J. Saadaoui, SSRN Working Paper, 2025. https://doi.org/10.2139/ssrn.5237256

• “Identification of Investment-Ready SMEs: A Machine Learning Framework to Enhance Equity Access and Economic Growth”, P. Gogas, T. Papadimitriou, P. Goumenidis, A. Kontos, N. Giannakis, Forecasting, forthcoming, IF 3.2.

• “O-016 Semen human papilloma virus and in vitro fertilization: Machine learning insights into infection prevalence and embryologic outcomes”, with J Francis , P Tsiartas , J Hreinsson , M Andersson , J Hermansson , P Gogas , T Papadimitriou , C Kärrberg , M Brännström , R Akouri, Human Reproduction, Volume 40, Issue Supplement_1, June 2025, deaf097.016, 5-Year Impact Factor (Clarivate): 6.1. https://doi.org/10.1093/humrep/deaf097.016

• “Investigating the Investment Readiness of European SMEs: A Machine Learning Approach”, International Review of Financial Analysis, with C. Alexakis, G. Petrela, F. Salvade and M. Polemis, IF 9.8, ABS 3*, Volume 105, 2025. https://doi.org/10.1016/j.irfa.2025.104439

• "Semen HPV and IVF: Insights from infection prevalence to embryologic outcomes", with E. Sofianos, T. Papadimitriou, P. Tsiartas, et al., Journal of Assisted Reproduction and Genetics, IF 3.2, Forthcoming. https://doi.org/10.1007/s10815-025-03513-6  

2024 (4)

• "Machine Learning Forecasting in the Macroeconomic Environment: The case of the US output Gap", with E. Sofianos, C. Alexakis, T. Papadimitriou, Economic Change and Restructuring, IF 4.0, ABS 1*.
  https://doi.org/10.1007/s10644-024-09849-w 

• "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 (6)

• “Machine Learning in Renewable Energy”, with T. Papadimitriou, Energies, 16(5), 2260, 2023. https://doi.org/10.3390/en16052260

• "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 (5)

• “Machine Learning in Economics and Finance”, with T. Papadimitriou, Computational Economics, 57(1), 1-4, 2021. https://doi.org/10.1007/s10614-021-10094-w

• “Credit Rating Agencies: Evolution or Extinction?”, with A. Dimitriadou, A. Agrapetidou and T. Papadimitriou, SSRN Working Paper, 2021. https://doi.org/10.2139/ssrn.3935710

• “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

2018 (3)

• “Forecasting Bank Failures and Stress Testing: A Machine Learning Approach”, with T. Papadimitriou and A. Agrapetidou, International Journal of Forecasting, 34(3), 440-455, 2018. https://doi.org/10.1016/j.ijforecast.2018.01.009

• “The Effects of Geopolitical Uncertainty in Forecasting Financial Markets: A Machine Learning Approach”, with V. Plakandaras and T. Papadimitriou, Algorithms, 12(1), 1, 2018. https://doi.org/10.3390/a12010001

• “The Fama 3 and Fama 5 Factor Models under a Machine Learning Framework”, with T. Papadimitriou and D. Karagkiozis, Rimini Centre for Economic Analysis Working Paper, 2018.

2017 (2)

• “The Informational Content of the Term Spread in Forecasting the US Inflation Rate: A Nonlinear Approach”, with V. Plakandaras, T. Papadimitriou and R. Gupta, Journal of Forecasting, 36(2), 109-121, 2017. https://doi.org/10.1002/for.2417

• “Forecasting Electricity Price Spikes Using Support Vector Machines”, with E. Stathakis and T. Papadimitriou, SSRN Working Paper, 2017. https://doi.org/10.2139/ssrn.2990407

2016 (2)

• “Testing Exchange Rate Models in a Small Open Economy: an SVR Approach”, with T. Papadimitriou and V. Plakandaras, Bulletin of Applied Economics, 3(2), 9-29, 2016.

• “The Term Premium as a Leading Macroeconomic Indicator”, with V. Plakandaras, T. Papadimitriou and R. Gupta, University of Pretoria Working Paper, 2016.

2015 (6)

• “Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques”, with V. Plakandaras and T. Papadimitriou, Journal of Forecasting, 34(7), 560-573, 2015. https://doi.org/10.1002/for.2354

• “Yield Curve and Recession Forecasting in a Machine Learning Framework”, with T. Papadimitriou, M. Matthaiou and E. Chrysanthidou, Computational Economics, 45(4), 635-645, 2015. https://doi.org/10.1007/s10614-014-9432-0

• “Yield Curve Point Triplets in Recession Forecasting”, with T. Papadimitriou and E. Chrysanthidou, International Finance, 18(2), 207-226, 2015. https://doi.org/10.1111/infi.12067

• “Forecasting the U.S. Real House Price Index”, with V. Plakandaras, R. Gupta and T. Papadimitriou, Economic Modelling, 45, 259-267, 2015. https://doi.org/10.1016/j.econmod.2014.10.050

• “US Inflation Dynamics on Long-Range Data”, with V. Plakandaras, R. Gupta and T. Papadimitriou, Applied Economics, 47(36), 3874-3890, 2015. https://doi.org/10.1080/00036846.2015.1019039

• “Market Sentiment and Exchange Rate Directional Forecasting”, with V. Plakandaras, T. Papadimitriou and K. Diamantaras, Algorithmic Finance, 4(1-2), 69-79, 2015. https://doi.org/10.3233/AF-150044

2014 (4)

• “Forecasting Energy Markets Using Support Vector Machines”, with T. Papadimitriou and E. Stathakis, Energy Economics, 44, 135-142, 2014. https://doi.org/10.1016/j.eneco.2014.03.017

• “Forecasting Bank Credit Ratings”, with T. Papadimitriou and A. Agrapetidou, The Journal of Risk Finance, 15(2), 195-209, 2014. https://doi.org/10.1108/JRF-11-2013-0076

• “The Slope and the Curvature of the Yield Curve in Recession Forecasting”, with T. Papadimitriou and E. Chrysanthidou, SSRN Working Paper, 2014. https://doi.org/10.2139/ssrn.2409122

• “Forecasting the NOK/USD Exchange Rate with Machine Learning Techniques”, with T. Papadimitriou and V. Plakandaras, SSRN Working Paper, 2014. https://doi.org/10.2139/ssrn.2387710

2013 (2)

• “Forecasting the Insolvency of U.S. Banks Using Support Vector Machines (SVMs) Based on Local Learning Feature Selection”, with T. Papadimitriou, V. Plakandaras and J.C. Mourmouris, International Journal of Computational Economics and Econometrics, 3(1-2), 83-90, 2013. https://doi.org/10.1504/IJCEE.2013.056267

• “Comparison of Simple Sum and Divisia Monetary Aggregates in GDP Forecasting: A Support Vector Machines Approach”, with T. Papadimitriou and E. Takli, Economics Bulletin, 33(2), 1101-1115, 2013.

2011 (1)

• “Directional Forecasting in Financial Time Series Using Support Vector Machines: The USD/Euro Exchange Rate”, with V. Plakandaras and T. Papadimitriou, SSRN Working Paper, 2011. https://doi.org/10.2139/ssrn.2387655

Econometrics

• “Macroeconomic Uncertainty, Growth and Inflation in the Eurozone: A Causal Approach”, with V. Plakandaras, R. Gupta and T. Papadimitriou, Applied Economics Letters, 25(14), 1029-1033, 2018. https://doi.org/10.1080/13504851.2017.1391996

• “Asymmetric Effects of Monetary Policy in the US and Brazil”, with I. Pragidis and B.M. Tabak, The Journal of Economic Asymmetries, 18, e00108, 2018. https://doi.org/10.1016/j.jeca.2018.e00108

• “Fiscal Shocks and Asymmetric Effects: A Comparative Analysis”, with I. Pragidis, V. Plakandaras and T. Papadimitriou, The Journal of Economic Asymmetries, 12(1), 22-33, 2015. https://doi.org/10.1016/j.jeca.2014.10.003

• “Are There Asymmetries in Fiscal Policy Shocks?”, with I. Pragidis, Journal of Economic Studies, 42(2), 303-321, 2015. https://doi.org/10.1108/JES-04-2013-0059

• “Public Debt and Private Consumption in OECD Countries”, with V. Plakandaras and T. Papadimitriou, The Journal of Economic Asymmetries, 11, 1-7, 2014. https://doi.org/10.1016/j.jeca.2014.03.001

• “Divisia Monetary Aggregates, the Great Ratios, and Classical Money Demand Functions”, with A. Serletis, Journal of Money, Credit and Banking, 46(1), 229-241, 2014. https://doi.org/10.1111/jmcb.12103

• “Common Stochastic Trends and the Ricardian Equivalence in the OECD”, with V. Plakandaras and T. Papadimitriou, SSRN Working Paper, 2014. https://doi.org/10.2139/ssrn.2389389

• “Business Cycle Synchronisation in the European Union: The Effect of the Common Currency”, OECD Journal: Journal of Business Cycle Measurement and Analysis, 2013(1), 1-14, 2013.

• “Interest Rates, Leverage, and Money”, with A. Serletis and K. Istiak, Open Economies Review, 24(1), 51-78, 2013. https://doi.org/10.1007/s11079-012-9253-5

• “Optimum Currency Areas within the US and Canada: A Data Analysis Approach”, with E. Chrysanthidou and T. Papadimitriou, Journal of Computational Optimization in Economics and Finance, 5(1), 41, 2013.

• “Episodic Nonlinearity in Leading Global Currencies”, with A. Serletis, A.G. Malliaris and M.J. Hinich, Open Economies Review, 23(2), 337-357, 2012. https://doi.org/10.1007/s11079-010-9194-9

• “GDP Trend Deviations and the Yield Spread: The Case of Eight EU Countries”, with I. Pragidis, Journal of Economics and Finance, 36(1), 226-237, 2012. https://doi.org/10.1007/s12197-011-9176-9

• “Economic Viability and Macroeconomic Impact of the Burgas-Alexandroupolis Pipeline”, with J.C. Mourmouris and T. Papadimitriou, International Journal of Economics and Management Engineering, 2(1), 2012.

• “The Burgas–Alexandroupolis Pipeline: Macroeconomic Impact for Greece”, with T. Papadimitriou, in Greece’s Horizons: Reflecting on the Country’s Assets and Capabilities, Springer, 187-200, 2012. https://doi.org/10.1007/978-3-642-34534-0_15

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.

• Gogas, P., Papadimitriou, T. and Matthaiou, M.A., 2014. A Novel Banking Supervision Method using the Minimum Dominating Set. In Network Models in Economics and Finance, pp. 277-286. Springer International Publishing.

• Papadimitriou, T., Gogas, P., Sarantitis, G. and Matthaiou, M.A., 2014. Analysis of Network Topology Using the Threshold-Minimum Dominating Set. SSRN Working Paper.

• 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 Tabak, B.M., 2013. Complex Networks and Banking Systems Supervision. Physica A: Statistical Mechanics and its Applications, 392(19), pp. 4429-4434.

• 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.