Research

Publications

Full list of publications, most recent first. Source: Google Scholar profile.

Journal Articles (78)

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

2. E Sofianos, T Betti, T Papadimitriou, A Barbier‐Gauchard, P Gogas (2026). “Using DSGE and Machine Learning to forecast public debt for France”. Journal of Forecasting 45(5), 2173-2185. https://doi.org/10.1002/for.70144

3. P Gogas, T Papadimitriou, P Goumenidis, A Kontos, N Giannakis (2025). “Identification of Investment-Ready SMEs: A Machine Learning Framework to Enhance Equity Access and Economic Growth”. Forecasting 7(3), 51. https://doi.org/10.3390/forecast7030051

4. C Alexakis, P Gogas, G Petrella, M Polemis, F Salvadè (2025). “Investigating the investment readiness of European SMEs: A machine learning approach”. International Review of Financial Analysis 105, 104439. https://doi.org/10.1016/j.irfa.2025.104439

5. A Dimitriadou, P Gogas, T Papadimitriou (2025). “Tourism and uncertainty: a machine learning approach”. Current Issues in Tourism 28(14), 2278-2298. https://doi.org/10.1080/13683500.2024.2370380

6. J Francis, P Tsiartas, J Hreinsson, M Andersson, J Hermansson, P Gogas, et al. (2025). “Semen HPV and IVF: insights from infection prevalence to embryologic outcomes”. Journal of Assisted Reproduction and Genetics 42(6), 2053-2066. https://doi.org/10.1007/s10815-025-03513-6

7. E Sofianos, C Alexakis, P Gogas, T Papadimitriou (2025). “Machine learning forecasting in the macroeconomic environment: the case of the US output gap”. Economic Change and Restructuring 58(1), 9. https://doi.org/10.1007/s10644-024-09849-w

8. F Gkatzoglou, T Papadimitriou, P Gogas (2024). “Fuel Price Networks in the EU”. Economies 12(5), 102. https://doi.org/10.3390/economies12050102

9. E Sofianos, E Zaganidis, T Papadimitriou, P Gogas (2024). “Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms”. Energies 17(6), 1296. https://doi.org/10.3390/en17061296

10. T Poufinas, P Gogas, T Papadimitriou, E Zaganidis (2023). “Machine Learning in Forecasting Motor Insurance Claims”. Risks 11(9), 164. https://doi.org/10.3390/risks11090164

11. M Alexiadou, E Sofianos, P Gogas, T Papadimitriou (2023). “Cryptocurrencies and Long-Range Trends”. International Journal of Financial Studies 11(1), 40. https://doi.org/10.3390/ijfs11010040

12. P Gogas, T Papadimitriou (2023). “Machine learning in renewable energy”. Energies 16(5), 2260. https://doi.org/10.3390/en16052260

13. G Toulias, E Sofianos, P Gogas (2023). “Bitcoin, Sentiment Analysis and the Efficient Market Hypothesis: A Machine Learning Approach”. Empirical Economics Letters 22(1). https://doi.org/10.2139/ssrn.4610497

14. T Lefkou, E Sofianos, P Gogas (2023). “Banking Sector Resilience to Major Crises”. Journal of Finance and Economics 11(1), 1-13. https://doi.org/10.12691/jfe-11-1-1

15. V Plakandaras, P Gogas, T Papadimitriou, I Tsamardinos (2022). “Credit Card Fraud Detection with Automated Machine Learning Systems”. Applied Artificial Intelligence 36(1), 2086354. https://doi.org/10.1080/08839514.2022.2086354

16. T Papadimitriou, P Gogas, AF Athanasiou (2022). “Forecasting Bitcoin Spikes: A GARCH-SVM Approach”. Forecasting 4(4), 752-766. https://doi.org/10.3390/forecast4040041

17. E Sofianos, P Gogas, T Papadimitriou (2022). “Mind the gap: forecasting euro-area output gaps with machine learning”. Applied Economics Letters 29(19), 1824-1828. https://doi.org/10.1080/13504851.2021.1963403

18. T Papadimitriou, P Gogas, F Gkatzoglou (2022). “The Convergence Evolution in Europe from a Complex Networks Perspective”. Journal of Risk and Financial Management 15(10), 457. https://doi.org/10.3390/jrfm15100457

19. T Papadimitriou, P Gogas, A Agrapetidou (2022). “The resilience of the US banking system”. International Journal of Finance & Economics 27(3), 2819-2835. https://doi.org/10.1002/ijfe.2300

20. P Gogas, T Papadimitriou, MA Matthaiou (2022). “Supervision of banking networks using the multivariate Threshold-Minimum Dominating Set (mT-MDS)”. Journal of Risk and Financial Management 15(6), 253. https://doi.org/10.3390/jrfm15060253

21. P Michailidis, A Dimitriadou, T Papadimitriou, P Gogas (2022). “Forecasting Hospital Readmissions with Machine Learning”. Healthcare 10(6), 981. https://doi.org/10.3390/healthcare10060981

22. P Gogas, T Papadimitriou (2022). “Emerging Trends in Energy Economics”. Energies 15(14), 5212. https://doi.org/10.3390/en15145212

23. A Agrapetidou, P Charonyktakis, P Gogas, T Papadimitriou, I Tsamardinos (2021). “An AutoML application to forecasting bank failures”. Applied Economics Letters 28(1), 5-9. https://doi.org/10.1080/13504851.2020.1725230

24. P Gogas, T Papadimitriou, E Sofianos (2021). “Forecasting Unemployment in the Euro‐Area with Machine Learning”. Journal of Forecasting 41(3), 551-566. https://doi.org/10.1002/for.2824

25. D Mouchtaris, E Sofianos, P Gogas, T Papadimitriou (2021). “Forecasting Natural Gas Spot Prices with Machine Learning”. Energies 14(18), 5782. https://doi.org/10.3390/en14185782

26. P Gogas, T Papadimitriou (2021). “Machine learning in economics and finance”. Computational Economics 57(1), 1-4. https://doi.org/10.1007/s10614-021-10094-w

27. V Plakandaras, P Gogas, T Papadimitriou (2021). “Gold against the machine”. Computational Economics 57(1), 5-28. https://doi.org/10.1007/s10614-020-10019-z

28. E Stathakis, T Papadimitriou, P Gogas (2021). “Forecasting price spikes in electricity markets”. Review of Economic Analysis(REA) 13(1), 65-87. https://doi.org/10.15353/rea.v13i1.1822

29. T Papadimitriou, P Gogas, F Gkatzoglou (2020). “The evolution of the cryptocurrencies market: A complex networks approach”. Journal of Computational and Applied Mathematics 376, 112831. https://doi.org/10.1016/j.cam.2020.112831

30. V Plakandaras, P Gogas, T Papadimitriou, E Doumpa, M Stefanidou (2020). “Forecasting Credit Ratings of EU Banks”. International Journal of Financial Studies 8(3), 49. https://doi.org/10.3390/ijfs8030049

31. T Papadimitriou, P Gogas, AF Athanasiou (2020). “Forecasting S&P 500 spikes: an SVM approach”. Digital Finance 2, 241-258. https://doi.org/10.1007/s42521-020-00024-0

32. A Dimitriadou, P Gogas, T Papadimitriou, V Plakandaras (2019). “Oil Market Efficiency under a Machine Learning Perspective”. Forecasting 1(1), 157-168. https://doi.org/10.3390/forecast1010011

33. V Plakandaras, P Gogas, T Papadimitriou, R Gupta (2019). “A re-evaluation of the term spread as a leading indicator”. International Review of Economics & Finance 64, 476-492. https://doi.org/10.1016/j.iref.2019.07.002

34. V Plakandaras, T Papadimitriou, P Gogas (2019). “Forecasting transportation demand for the US market”. Transportation Research Part A: Policy and Practice 126, 195-214. https://doi.org/10.1016/j.tra.2019.06.008

35. P Gogas, T Papadimitriou, E Sofianos (2019). “Money Neutrality, Monetary Aggregates and Machine Learning”. Algorithms 12(7), 137. https://doi.org/10.3390/a12070137

36. V Plakandaras, P Gogas, T Papadimitriou (2018). “The effects of geopolitical uncertainty in forecasting financial markets: A machine learning approach”. Algorithms 12(1), 1. https://doi.org/10.3390/a12010001

37. P Gogas, I Pragidis, BM Tabak (2018). “Asymmetric effects of monetary policy in the US and Brazil”. The Journal of Economic Asymmetries 18, e00108. https://doi.org/10.1016/j.jeca.2018.e00108

38. P Gogas, T Papadimitriou, A Agrapetidou (2018). “Forecasting bank failures and stress testing: A machine learning approach”. International Journal of Forecasting 34(3), 440-455. https://doi.org/10.1016/j.ijforecast.2018.01.009

39. V Plakandaras, R Gupta, P Gogas, T Papadimitriou (2018). “Macroeconomic uncertainty, growth and inflation in the Eurozone: a causal approach”. Applied Economics Letters 25(14), 1029-1033. https://doi.org/10.1080/13504851.2017.1391996

40. P Gogas, R Gupta, SM Miller, T Papadimitriou, G Sarantitis (2017). “Income inequality: A state-by-state complex network analysis”. Physica A: Statistical Mechanics and its Applications 483, 423-437.

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

42. P Gogas, T Papadimitriou, MA Matthaiou (2016). “Bank supervision using the Threshold-Minimum Dominating Set”. Physica A: Statistical Mechanics and its Applications 451, 23-35. https://doi.org/10.1016/j.physa.2015.12.149

43. N Antonakakis, P Gogas, T Papadimitriou, GA Sarantitis (2016). “International business cycle synchronization since the 1870s: Evidence from a novel network approach”. Physica A: Statistical Mechanics and its Applications 447, 286-296. https://doi.org/10.1016/j.physa.2015.12.029

44. T Papadimitriou, P Gogas, GA Sarantitis (2016). “Convergence of European Business Cycles: A Complex Networks Approach”. Computational Economics 47(2), 97-119. https://doi.org/10.1007/s10614-014-9474-3

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

46. GA Sarantitis, T Papadimitriou, P Gogas (2016). “A Network Analysis of the United Kingdom’s Consumer Price Index”. Computational Economics 51(2), 173-193. https://doi.org/10.1007/s10614-016-9625-9

47. V Plakandaras, T Papadimitriou, P Gogas (2015). “Forecasting daily and monthly exchange rates with machine learning techniques”. Journal of Forecasting 34(7), 560-573. https://doi.org/10.1002/for.2354

48. V Plakandaras, P Gogas, R Gupta, T Papadimitriou (2015). “US inflation dynamics on long-range data”. Applied Economics 47(36), 3874-3890. https://doi.org/10.1080/00036846.2015.1019039

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

50. I Pragidis, P Gogas, V Plakandaras, T Papadimitriou (2015). “Fiscal shocks and asymmetric effects: A comparative analysis”. The Journal of Economic Asymmetries 12(1), 22-33. https://doi.org/10.1016/j.jeca.2014.10.003

51. P Gogas, I Pragidis (2015). “Are there asymmetries in fiscal policy shocks?”. Journal of Economic Studies 42(2), 303-321. https://doi.org/10.1108/jes-04-2013-0059

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

53. V Plakandaras, R Gupta, P Gogas, T Papadimitriou (2015). “Forecasting the US real house price index”. Economic Modelling 45, 259-267. https://doi.org/10.1016/j.econmod.2014.10.050

54. V Plakandaras, T Papadimitriou, P Gogas, K Diamantaras (2015). “Market sentiment and exchange rate directional forecasting”. Algorithmic Finance 4(1-2), 69-79. https://doi.org/10.3233/af-150044

55. T Papadimitriou, P Gogas, E Stathakis (2014). “Forecasting energy markets using support vector machines”. Energy Economics 44, 135-142. https://doi.org/10.1016/j.eneco.2014.03.017

56. P Gogas, V Plakandaras, T Papadimitriou (2014). “Public debt and private consumption in OECD countries”. The Journal of Economic Asymmetries 11, 1-7. https://doi.org/10.1016/j.jeca.2014.03.001

57. P Gogas, T Papadimitriou, A Agrapetidou (2014). “Forecasting bank credit ratings”. The Journal of Risk Finance 15(2), 195-209. https://doi.org/10.1108/jrf-11-2013-0076

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

59. T Papadimitriou, P Gogas, BM Tabak (2013). “Complex networks and banking systems supervision”. Physica A: Statistical Mechanics and its Applications 392(19), 4429-4434. https://doi.org/10.1016/j.physa.2013.05.013

60. P Gogas, T Papadimitriou, G Sarantitis (2013). “Testing purchasing power parity in a DFA rolling Hurst framework: the case of 23 OECD countries”. Applied Financial Economics 23(17), 1399-1406. https://doi.org/10.1080/09603107.2013.829196

61. P Gogas (2013). “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.

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

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

64. T Papadimitriou, P Gogas, V Plakandaras, JC Mourmouris (2013). “Forecasting the insolvency of US banks using Support Vector Machines (SVMs) based on local learning feature selection”. International Journal of Computational Economics and Econometrics 3(1-2), 83-90. https://doi.org/10.1504/ijcee.2013.056267

65. P Gogas, T Papadimitriou, E Takli (2013). “Comparison of simple sum and Divisia monetary aggregates in GDP forecasting: a support vector machines approach”. Economics Bulletin 33(2), 1101-1115.

66. A Serletis, AG Malliaris, MJ Hinich, P Gogas (2012). “Episodic nonlinearity in leading global currencies”. Open Economies Review 23(2), 337-357. https://doi.org/10.1007/s11079-010-9194-9

67. P Gogas, JC Mourmouris, T Papadimitriou (2012). “Economic Viability and Macroeconomic Impact of the Burgas-Alexandroupolis Pipeline”. International Journal of Economics and Management Engineering 2(1).

68. P Gogas, I Pragidis (2012). “GDP trend deviations and the yield spread: the case of eight EU countries”. Journal of Economics and Finance 36(1), 226-237. https://doi.org/10.1007/s12197-011-9176-9

69. D Chionis, P Gogas, I Pragidis (2010). “Predicting European Union recessions in the euro era: The yield curve as a forecasting tool of economic activity”. International Advances in Economic Research 16(1), 1-10. https://doi.org/10.1007/s11294-009-9247-2

70. P Gogas, A Serletis (2010). “Forecast evaluation in daily commodities futures markets”. International Journal of Financial Markets and Derivatives 1(2), 155-168. https://doi.org/10.1504/ijfmd.2010.032466

71. P Gogas, A Serletis (2009). “Forecasting in inefficient commodity markets”. Journal of Economic Studies 36(4), 383-392. https://doi.org/10.1108/01443580910973592

72. DO Cajueiro, P Gogas, BM Tabak (2009). “Does financial market liberalization increase the degree of market efficiency? The case of the Athens stock exchange”. International Review of Financial Analysis 18(1-2), 50-57. https://doi.org/10.1016/j.irfa.2008.11.004

73. A Serletis, P Gogas (2007). “The Feldstein-Horioka puzzle in an ARIMA framework”. Journal of Economic Studies 34(3), 194-210. https://doi.org/10.1108/01443580710772768

74. A Serletis, P Gogas (2004). “Long-horizon regression tests of the theory of purchasing power parity”. Journal of Banking & Finance 28(8), 1961-1985. https://doi.org/10.1016/j.jbankfin.2003.07.006

75. A Serletis, P Gogas (2000). “Purchasing power parity, nonlinearity and chaos”. Applied Financial Economics 10(6), 615-622. https://doi.org/10.1080/096031000437962

76. A Serletis, P Gogas (1999). “The North American natural gas liquids markets are chaotic”. The Energy Journal 20(1), 83-103.

77. A Serletis, P Gogas (1997). “Chaos in East European black market exchange rates”. Research in Economics 51(4), 359-385. https://doi.org/10.1006/reec.1997.0050

78. P Gogas (1997). “On the construction of personal, corporate and effective overall marginal tax rates for Canada (1977-1992)”. The Saskatchewan Economic Journal 1(1), 23-32.

Book Chapters (6)

1. E Sofianos, T Papadimitriou, P Gogas (2023). “Forecasting inflation spikes with machine learning”. Encyclopedia of Monetary Policy, Financial Markets and Banking, Elsevier. https://doi.org/10.2139/ssrn.4610424

2. T Papadimitriou, P Gogas, GA Sarantitis (2014). “European Business Cycle Synchronization: A Complex Network Perspective”. Network Models in Economics and Finance, 265-275. https://doi.org/10.1007/978-3-319-09683-4_13

3. T Papadimitriou, P Gogas, G Sarantitis (2014). “Business Cycle Convergence: A Survey of Methods and Models”. Mathematics Without Boundaries: Surveys in Interdisciplinary Research, 447-469.

4. P Gogas, T Papadimitriou, MA Matthaiou (2014). “A novel Banking Supervision Method using the Minimum Dominating Set”. Network Models in Economics and Finance, 277-286.

5. P Gogas, T Papadimitriou (2012). “The Burgas–Alexandroupolis Pipeline: Macroeconomic Impact for Greece”. Greece's Horizons: Reflecting on the Country's Assets and Capabilities, 187-200. https://doi.org/10.1007/978-3-642-34534-0_15

6. P Gogas, A Serletis (2005). “The revenue smoothing hypothesis in an ARIMA framework: Evidence from the United States”. in C. Diebolt, C. Kyrtsou et al.(eds.), New Trends in Macroeconomics, Springer.

Conference Papers (5)

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

2. E Stathakis, P Gogas, T Papadimitriou (2015). “Using multiclass Support Vector Machines to forecast the occurrence of price spikes in the German electricity market”. International Conference on Business & Economics of the Hellenic Open University.

3. P Gogas, T Papadimitriou, M Matthaiou (2015). “Efficient Supervision of Banking Networks using the Threshold-Minimum Dominating Set (T-MDS): The case of Brazil”. International Conference on Business & Economics of the Hellenic Open University.

4. P Gogas, T Papadimitriou, GA Sarantitis (2015). “Network Analysis for Central Banks”. International Conference on Business & Economics of the Hellenic Open University.

5. T Papadimitriou, P Gogas, GA Sarantitis (2013). “An analysis of the US Gross State Product co-movement using the Minimum Dominating Set”. 2013 IEEE Global Conference on Signal and Information Processing, 1145-1145. https://doi.org/10.1109/globalsip.2013.6737108

Working Papers and Preprints (14)

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

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

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

4. A Dimitriadou, A Agrapetidou, P Gogas, T Papadimitriou (2021). “Credit Rating Agencies: Evolution or Extinction?”. SSRN Working Paper. https://doi.org/10.2139/ssrn.3935710

5. P Gogas, T Papadimitriou, D Karagkiozis (2018). “The Fama 3 and Fama 5 factor models under a machine learning framework”. Rimini Centre for Economic Analysis.

6. E Stathakis, T Papadimitriou, P Gogas (2017). “Forecasting electricity price spikes using Support Vector Machines”. SSRN Working Paper. https://doi.org/10.2139/ssrn.2990407

7. V Plakandaras, P Gogas, T Papadimitriou, R Gupta (2016). “The Term Premium as a Leading Macroeconomic Indicator”. University of Pretoria, Department of Economics.

8. T Papadimitriou, P Gogas, G Sarantitis, M Matthaiou (2014). “Analysis of Network Topology Using the Threshold-Minimum Dominating Set”. SSRN Working Paper. https://doi.org/10.2139/ssrn.2435488

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

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

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

12. T Papadimitriou, P Gogas, G Sarantitis (2014). “Analyzing the co-movements of the US Gross State Product Growth with the use of the Minimum Dominating Set”. SSRN Working Paper. https://doi.org/10.2139/ssrn.2386565

13. V Plakandaras, P Gogas, T Papadimitriou (2011). “Directional Forecasting in financial time series using Support Vector machines: the USD/Euro exchange rate”. SSRN Working Paper. https://doi.org/10.2139/ssrn.2387655

14. P Gogas, I Pragidis (2010). “Does the interest risk premium predict housing prices”. DUTH Research Papers in Economics 1-2010, Democritus University of Thrace.

Theses (2)

1. P Gogas (2000). “Purchasing Power Parity, balanced growth, and volatility forecasting: An application of recent developments in time series analysis”. University of Calgary.

2. P Gogas (1996). “The Canadian Laffer Curve”. Master's Thesis, University of Saskatchewan.