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Theophilos Papadimitriou

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Kea Baret & Amélie Barbier-Gauchard & Theophilos Papadimitriou, 2021. "Forecasting the Stability and Growth Pact compliance using Machine Learning," Working Papers hal-03121966, HAL.

    Cited by:

    1. Kea BARET, 2021. "Fiscal rules’ compliance and Social Welfare," Working Papers of BETA 2021-38, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    2. Carlos Fonseca Marinheiro, 2021. "The Expenditure Benchmark: Complex and Unsuitable for Independent Fiscal Institutions," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 63(3), pages 411-431, September.
    3. Kea BARET, 2021. "Fiscal rules’ compliance and Social Welfare," Working Papers of BETA 2021-50, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.

  2. Kea BARET & Theophilos PAPADIMITRIOU, 2019. "On the Stability and Growth Pact compliance: what is predictable with machine learning?," Working Papers of BETA 2019-48, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.

    Cited by:

    1. Philipp Mohl & Gilles Mourre & Sven Langedijk & Martijn Hoogeland, 2021. "Does Media Visibility Make EU Fiscal Rules More Effective?," European Economy - Discussion Papers 155, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.

  3. Gogas, Periklis & Papadimitriou, Theophilos & Sofianos, Emmanouil, 2019. "Money Neutrality, Monetary Aggregates and Machine Learning," DUTH Research Papers in Economics 4-2016, Democritus University of Thrace, Department of Economics.

    Cited by:

    1. Dimitrios Mouchtaris & Emmanouil Sofianos & Periklis Gogas & Theophilos Papadimitriou, 2021. "Forecasting Natural Gas Spot Prices with Machine Learning," Energies, MDPI, vol. 14(18), pages 1-13, September.
    2. Periklis Gogas & Theophilos Papadimitriou & Emmanouil Sofianos, 2022. "Forecasting unemployment in the euro area with machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 551-566, April.
    3. Emmanouil Sofianos & Emmanouil Zaganidis & Theophilos Papadimitriou & Periklis Gogas, 2024. "Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms," Energies, MDPI, vol. 17(6), pages 1-14, March.

  4. Periklis Gogas & Theofilos Papadimitriou & Dimitrios Karagkiozis, 2018. "The Fama 3 and Fama 5 factor models under a machine learning framework," Working Paper series 18-05, Rimini Centre for Economic Analysis.

    Cited by:

    1. Pedro M. Mirete-Ferrer & Alberto Garcia-Garcia & Juan Samuel Baixauli-Soler & Maria A. Prats, 2022. "A Review on Machine Learning for Asset Management," Risks, MDPI, vol. 10(4), pages 1-46, April.
    2. David Mayer-Foulkes, 2018. "Efficient Urbanization for Mexican Development," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(10), pages 1-1, October.

  5. Vasilios Plakandaras & Rangan Gupta & Periklis Gogas & Theophilos Papadimitriou, 2017. "Forecasting the U.S. Real House Price Index," Papers 1707.04868, arXiv.org.

    Cited by:

    1. Vasilios Plakandaras & Rangan Gupta & Constantinos Katrakilidis & Mark E. Wohar, 2017. "Time-Varying Role of Macroeconomic Shocks on House Prices in the US and UK: Evidence from Over 150 Years of Data," Working Papers 201765, University of Pretoria, Department of Economics.
    2. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2017. "The Informational Content of the Term Spread in Forecasting the US Inflation Rate: A Nonlinear Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(2), pages 109-121, March.
    3. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2016. "The Term Premium as a Leading Macroeconomic Indicator," Working Papers 201613, University of Pretoria, Department of Economics.
    4. Kaijian He & Rui Zha & Jun Wu & Kin Keung Lai, 2016. "Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price," Sustainability, MDPI, vol. 8(4), pages 1-11, April.
    5. Hossein Hassani & Mohammad Reza Yeganegi & Rangan Gupta, 2018. "Does Inequality Really Matter in Forecasting Real Housing Returns of the United Kingdom?," Working Papers 201859, University of Pretoria, Department of Economics.
    6. Ti-Ching Peng, 2021. "The effect of hazard shock and disclosure information on property and land prices: a machine-learning assessment in the case of Japan," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 41(1), pages 1-32, February.
    7. George Milunovich, 2020. "Forecasting Australia's real house price index: A comparison of time series and machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1098-1118, November.
    8. Plakandaras, Vasilios & Papadimitriou, Theophilos & Gogas, Periklis, 2019. "Forecasting transportation demand for the U.S. market," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 195-214.
    9. Mehmet Balcilar & Elie Bouri & Rangan Gupta & Mark E. Wohar, 2018. "Mortgage Default Risks and High-Frequency Predictability of the US Housing Market: A Reconsideration," Working Papers 201875, University of Pretoria, Department of Economics.
    10. Aviral Kumar Tiwari & Rangan Gupta & Mark E. Wohar, 2019. "Is the Housing Market in the United States Really Weakly-Efficient?," Working Papers 201934, University of Pretoria, Department of Economics.
    11. Sommervoll, Åvald & Sommervoll, Dag Einar, 2019. "Learning from man or machine: Spatial fixed effects in urban econometrics," Regional Science and Urban Economics, Elsevier, vol. 77(C), pages 239-252.
    12. Vasilios Plakandaras & Elie Bouri & Rangan Gupta, 2019. "Forecasting Bitcoin Returns: Is there a Role for the U.S. – China Trade War?," Working Papers 201980, University of Pretoria, Department of Economics.
    13. McGurk, Zachary, 2020. "US real estate inflation prediction: Exchange rates and net foreign assets," The Quarterly Review of Economics and Finance, Elsevier, vol. 75(C), pages 53-66.
    14. Yu Zhao & Xi Zhang & Zhongshun Shi & Lei He, 2017. "Grain Price Forecasting Using a Hybrid Stochastic Method," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(05), pages 1-24, October.
    15. Rangan Gupta & Chi Keung Marco Lau & Wendy Nyakabawo, 2018. "Predicting Aggregate and State-Level US House Price Volatility: The Role of Sentiment," Working Papers 201866, University of Pretoria, Department of Economics.
    16. Oscar Claveria & Enric Monte & Salvador Torra, 2016. "Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(3), pages 341-357, August.
    17. Narayan, Paresh Kumar & Ahmed, Huson Ali & Narayan, Seema, 2017. "Can investors gain from investing in certain sectors?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 48(C), pages 160-177.
    18. Sinha, Ankur & Kedas, Satishwar & Kumar, Rishu & Malo, Pekka, 2019. "Buy, Sell or Hold: Entity-Aware Classification of Business News," IIMA Working Papers WP 2019-04-02, Indian Institute of Management Ahmedabad, Research and Publication Department.
    19. Sun, Tianyu & Chand, Satish & Sharpe, Keiran, 2018. "Effect of Aging on Urban Land Prices in China," MPRA Paper 89237, University Library of Munich, Germany.

  6. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2016. "The Term Premium as a Leading Macroeconomic Indicator," Working Papers 201613, University of Pretoria, Department of Economics.

    Cited by:

    1. Joao F. Caldeira & Rangan Gupta & Tahir Suleman & Hudson S. Torrent, 2019. "Forecasting the Term Structure of Interest Rates of the BRICS: Evidence from a Nonparametric Functional Data Analysis," Working Papers 201911, University of Pretoria, Department of Economics.
    2. Rangan Gupta & Hylton Hollander & Rudi Steinbach, 2020. "Forecasting output growth using a DSGE-based decomposition of the South African yield curve," Empirical Economics, Springer, vol. 58(1), pages 351-378, January.
    3. Bouri, Elie & Demirer, Riza & Gupta, Rangan & Wohar, Mark E., 2021. "Gold, platinum and the predictability of bond risk premia," Finance Research Letters, Elsevier, vol. 38(C).

  7. Periklis Gogas & Theophilos Papadimitriou & Vasilios Plakandaras & Rangan Gupta, 2015. "The Informational Content of the Term-Spread in Forecasting the U.S. Inflation Rate: A Nonlinear Approach," Working Papers 201548, University of Pretoria, Department of Economics.

    Cited by:

    1. Li, Zheng & Zhou, Bo & Hensher, David A., 2022. "Forecasting automobile gasoline demand in Australia using machine learning-based regression," Energy, Elsevier, vol. 239(PD).
    2. Joao F. Caldeira & Rangan Gupta & Tahir Suleman & Hudson S. Torrent, 2019. "Forecasting the Term Structure of Interest Rates of the BRICS: Evidence from a Nonparametric Functional Data Analysis," Working Papers 201911, University of Pretoria, Department of Economics.
    3. Oguzhan Cepni & Rangan Gupta & Cenk C. Karahan & Brian M. Lucey, 2020. "Oil Price Shocks and Yield Curve Dynamics in Emerging Markets," Working Papers 202036, University of Pretoria, Department of Economics.
    4. Bouri, Elie & Demirer, Riza & Gupta, Rangan & Wohar, Mark E., 2021. "Gold, platinum and the predictability of bond risk premia," Finance Research Letters, Elsevier, vol. 38(C).
    5. Çepni, Oğuzhan & Guney, I. Ethem & Gupta, Rangan & Wohar, Mark E., 2020. "The role of an aligned investor sentiment index in predicting bond risk premia of the U.S," Journal of Financial Markets, Elsevier, vol. 51(C).
    6. Francisco Jareño & Ana Escribano & Zaghum Umar, 2023. "The impact of the COVID-19 outbreak on the connectedness of the BRICS’s term structure," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.

  8. Antonakakis, Nikolaos & Gogas, Periklis & Papadimitriou, Theophilos & Sarantitis, Georgios, 2015. "International Business Cycle Synchronization since the 1870s: Evidence from a Novel Network Approach," MPRA Paper 67223, University Library of Munich, Germany.

    Cited by:

    1. Schmidbauer, Harald & Rösch, Angi & Uluceviz, Erhan, 2017. "Frequency aspects of information transmission in a network of three western equity markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 933-946.
    2. Sang Hoon Kang & Salim Lahmiri & Gazi Salah Uddin & Jose Arreola Hernandez & Seong-Min Yoon, 2020. "Inflation cycle synchronization in ASEAN countries," Post-Print hal-02779489, HAL.
    3. Amalia Repele & Sébastien Waelti, 2021. "Mapping the Global Business Cycle Network," Open Economies Review, Springer, vol. 32(4), pages 739-760, September.
    4. Tamás Sebestyén & Zita Iloskics, 2020. "Do economic shocks spread randomly?: A topological study of the global contagion network," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-22, September.
    5. Matesanz, David & Ortega, Guillermo J., 2016. "On business cycles synchronization in Europe: A note on network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 287-296.

  9. Periklis Gogas & Rangan Gupta & Stephen M. Miller & Theophilos Papadimitriou & Georgios Antonios Sarantitis, 2015. "Income Inequality: A State-by-State Complex Network Analysis," Working Papers 201534, University of Pretoria, Department of Economics.

    Cited by:

    1. Mehmet Balcilar & Seyi Saint Akadiri & Rangan Gupta & Stephen M. Miller, 2019. "Partisan Conflict and Income Inequality in the United States: A Nonparametric Causality-in-Quantiles Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(1), pages 65-82, February.
    2. Mehmet Balcilar & Seyi Saint Akadiri & Rangan Gupta & Stephen M. Miller, 2017. "Partisan Conflict and Income Distribution in the United States: A Nonparametric Causality-in-Quantiles Approach," Working papers 2017-11, University of Connecticut, Department of Economics.

  10. Sarantitis, Georgios & Papadimitriou, Theophilos & Gogas, Periklis, 2015. "A Network Analysis of the United Kingdom’s Consumer Price Index," DUTH Research Papers in Economics 1-2016, Democritus University of Thrace, Department of Economics.

    Cited by:

    1. Michail Tsagris, 2021. "A New Scalable Bayesian Network Learning Algorithm with Applications to Economics," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 341-367, January.
    2. Emiliano Alvarez & Juan Gabriel Brida & Pablo Mones, 2024. "On the Dynamics of Relative Prices and the Relationship with Inflation: An Empirical Approach," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 339-355, January.
    3. Sun, Qingru & Gao, Xiangyun & Wen, Shaobo & Chen, Zhihua & Hao, Xiaoqing, 2018. "The transmission of fluctuation among price indices based on Granger causality network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 36-49.
    4. Qingru Sun & Xiangyun Gao & Ze Wang & Siyao Liu & Sui Guo & Yang Li, 2020. "Quantifying the risk of price fluctuations based on weighted Granger causality networks of consumer price indices: evidence from G7 countries," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(4), pages 821-844, October.

  11. Theophilos Papadimitriou & Periklis Gogas & Georgios-Antonios Sarantitis, 2014. "Convergence of European Business Cycles: A Complex Networks Approach," Working Paper series 35_14, Rimini Centre for Economic Analysis.

    Cited by:

    1. Michail Tsagris, 2021. "A New Scalable Bayesian Network Learning Algorithm with Applications to Economics," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 341-367, January.
    2. Antonakakis, Nikolaos & Gogas, Periklis & Papadimitriou, Theophilos & Sarantitis, Georgios Antonios, 2016. "International business cycle synchronization since the 1870s: Evidence from a novel network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 286-296.
    3. Gogas, Periklis & Gupta, Rangan & Miller, Stephen M. & Papadimitriou, Theophilos & Sarantitis, Georgios Antonios, 2017. "Income inequality: A complex network analysis of US states," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 423-437.
    4. Mao Takongmo, Charles-O. & Touré, Adam, 2023. "Trade openness and connectedness of national productions: Do financial openness, economic specialization, and the size of the country matter?," Economic Modelling, Elsevier, vol. 125(C).
    5. Amalia Repele & Sébastien Waelti, 2021. "Mapping the Global Business Cycle Network," Open Economies Review, Springer, vol. 32(4), pages 739-760, September.
    6. Plakandaras, Vasilios & Tiwari, Aviral Kumar & Gupta, Rangan & Ji, Qiang, 2020. "Spillover of sentiment in the European Union: Evidence from time- and frequency-domains," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 105-130.
    7. Luis à ngel Hierro & Antonio José Garzón & Helena Domínguez-Torres, 2019. "20 Years of European Monetary Policy. From Doctrinarism to Realpolitik," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 66(3), pages 149-172, December.
    8. Matesanz, David & Ortega, Guillermo J., 2016. "On business cycles synchronization in Europe: A note on network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 287-296.

  12. Vasilios Plakandaras & Theophilos Papadimitriou & Periklis Gogas & Konstantinos Diamantaras, 2014. "Market Sentiment and Exchange Rate Directional Forecasting," Working Paper series 37_14, Rimini Centre for Economic Analysis.

    Cited by:

    1. Sergey Nasekin & Cathy Yi-Hsuan Chen, 2020. "Deep learning-based cryptocurrency sentiment construction," Digital Finance, Springer, vol. 2(1), pages 39-67, September.
    2. Periklis Gogas & Theofilos Papadimitriou & Dimitrios Karagkiozis, 2018. "The Fama 3 and Fama 5 factor models under a machine learning framework," Working Paper series 18-05, Rimini Centre for Economic Analysis.
    3. Christina Christou & Rangan Gupta & Christis Hassapis & Tahir Suleman, 2018. "The role of economic uncertainty in forecasting exchange rate returns and realized volatility: Evidence from quantile predictive regressions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(7), pages 705-719, November.
    4. Syed Jawad Hussain Shahzad & Clement Kweku Kyei & Rangan Gupta & Eric Olson, 2020. "Investor Sentiment and Dollar-Pound Exchange Rate Returns: Evidence from Over a Century of Data Using a Cross-Quantilogram Approach," Working Papers 202008, University of Pretoria, Department of Economics.
    5. Rangan Gupta & Vasilios Plakandaras, 2018. "Efficiency in BRICS Currency Markets using Long-Spans of Data: Evidence from Model-Free Tests of Directional Predictability," Working Papers 201836, University of Pretoria, Department of Economics.
    6. Omotosho, Babatunde S. & Tumala, Mohammed M., 2019. "A Text Mining Analysis of Central Bank Monetary Policy Communication in Nigeria," MPRA Paper 98850, University Library of Munich, Germany.
    7. Rahimi, Fatemeh & Mousavian Anaraki, Seyed Alireza, 2020. "Proposing an Innovative Model Based on the Sierpinski Triangle for Forecasting EUR/USD Direction Changes," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 15(4), pages 423-444, October.
    8. Omotosho, Babatunde S., 2020. "Central Bank Communication during Economic Recessions: Evidence from Nigeria," MPRA Paper 99655, University Library of Munich, Germany.

  13. Theophilos Papadimitriou & Periklis Gogas & Georgios-Antonios Sarantitis, 2014. "European Business Cycle Synchronization: a Complex Network Perspective," Working Paper series 33_14, Rimini Centre for Economic Analysis.

    Cited by:

    1. Arnab Chakrabarti & Rituparna Sen, 2018. "Some Statistical Problems with High Dimensional Financial data," Papers 1808.02953, arXiv.org.

  14. Vasilios Plakandaras & Periklis Gogas & Rangan Gupta & Theophilos Papadimitriou, 2014. "US Inflation Dynamics on Long Range Data," Working Papers 201452, University of Pretoria, Department of Economics.

    Cited by:

    1. Yingying XU & Zhixin LIU & Jaime ORTIZ, 2018. "Actual and Expected Inflation in the U.S.: A Time-Frequency View," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 42-62, December.
    2. S. Arshad & S.A.R. Rizvi & O. Haroon & Fahad Mehmood & Q. Gong, 2021. "Are Oil Prices Efficient?," Post-Print hal-04317811, HAL.
    3. Boubaker Heni & Canarella Giorgio & Miller Stephen M. & Gupta Rangan, 2017. "Time-varying persistence of inflation: evidence from a wavelet-based approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(4), pages 1-18, September.
    4. Refk Selmi & Aviral Kumar Tiwari & Shawkat Hammoudeh, 2018. "Efficiency or speculation? A dynamic analysis of the Bitcoin market," Economics Bulletin, AccessEcon, vol. 38(4), pages 2037-2046.
    5. Vasilios Plakandaras & Rangan Gupta & Mark E. Wohar, 2018. "Persistence of Economic Uncertainty: A Comprehensive Analysis," Working Papers 201810, University of Pretoria, Department of Economics.
    6. Yingying Xu & Zhi-Xin Liu & Hsu-Ling Chang & Adelina Dumitrescu Peculea & Chi-Wei Su, 2017. "Does self-fulfilment of the inflation expectation exist?," Applied Economics, Taylor & Francis Journals, vol. 49(11), pages 1098-1113, March.

  15. Theophilos Papadimitriou & Periklis Gogas & Maria Matthaiou & Efthymia Chrysanthidou, 2014. "Yield curve and Recession Forecasting in a Machine Learning Framework," Working Paper series 32_14, Rimini Centre for Economic Analysis.

    Cited by:

    1. Cyrille Lenoel & Garry Young, 2020. "Real-time turning point indicators: Review of current international practices," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2020-05, Economic Statistics Centre of Excellence (ESCoE).
    2. Plakandaras, Vasilios & Gupta, Rangan & Papadimitriou, Theophilos & Gogas, Periklis, 2014. "Forecasting the U.S. Real House Price Index," DUTH Research Papers in Economics 10-2014, Democritus University of Thrace, Department of Economics.
    3. Yasmeen Idilbi-Bayaa & Mahmoud Qadan, 2021. "Forecasting Commodity Prices Using the Term Structure," JRFM, MDPI, vol. 14(12), pages 1-39, December.
    4. Lulin Xu & Zhongwu Li, 2021. "A New Appraisal Model of Second-Hand Housing Prices in China’s First-Tier Cities Based on Machine Learning Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 617-637, February.
    5. Söhnke M. Bartram & Jürgen Branke & Mehrshad Motahari, 2020. "Artificial intelligence in asset management," Working Papers 20202001, Cambridge Judge Business School, University of Cambridge.
    6. Plakandaras, Vasilios & Gogas, Periklis & Papadimitriou, Theophilos & Gupta, Rangan, 2019. "A re-evaluation of the term spread as a leading indicator," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 476-492.
    7. N. Loukeris & I. Eleftheriadis & E. Livanis, 2016. "The Portfolio Heuristic Optimisation System (PHOS)," Computational Economics, Springer;Society for Computational Economics, vol. 48(4), pages 627-648, December.
    8. Oguzhan Cepni & Rangan Gupta & Cenk C. Karahan & Brian M. Lucey, 2020. "Oil Price Shocks and Yield Curve Dynamics in Emerging Markets," Working Papers 202036, University of Pretoria, Department of Economics.
    9. Jaehyuk Choi & Desheng Ge & Kyu Ho Kang & Sungbin Sohn, 2021. "Yield Spread Selection in Predicting Recession Probabilities: A Machine Learning Approach," Papers 2101.09394, arXiv.org, revised Jan 2022.
    10. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
    11. Vrontos, Spyridon D. & Galakis, John & Vrontos, Ioannis D., 2021. "Modeling and predicting U.S. recessions using machine learning techniques," International Journal of Forecasting, Elsevier, vol. 37(2), pages 647-671.
    12. Cheng-Feng Wu & Shian-Chang Huang & Chei-Chang Chiou & Tsangyao Chang & Yung-Chih Chen, 2022. "The Relationship Between Economic Growth and Electricity Consumption: Bootstrap ARDL Test with a Fourier Function and Machine Learning Approach," Computational Economics, Springer;Society for Computational Economics, vol. 60(4), pages 1197-1220, December.
    13. Maas, Benedikt, 2019. "Nowcasting and forecasting US recessions: Evidence from the Super Learner," MPRA Paper 96408, University Library of Munich, Germany.
    14. Knut Lehre Seip & Dan Zhang, 2021. "The Yield Curve as a Leading Indicator: Accuracy and Timing of a Parsimonious Forecasting Model," Forecasting, MDPI, vol. 3(2), pages 1-16, May.
    15. Ranik Raaen Wahlstrøm & Florentina Paraschiv & Michael Schürle, 2022. "A Comparative Analysis of Parsimonious Yield Curve Models with Focus on the Nelson-Siegel, Svensson and Bliss Versions," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 967-1004, March.
    16. Christos Alexakis & Michael Dowling & Konstantinos Eleftheriou & Michael Polemis, 2021. "Textual Machine Learning: An Application to Computational Economics Research," Post-Print hal-03182910, HAL.
    17. Bouri, Elie & Demirer, Riza & Gupta, Rangan & Wohar, Mark E., 2021. "Gold, platinum and the predictability of bond risk premia," Finance Research Letters, Elsevier, vol. 38(C).
    18. Theodore Syriopoulos & Michael Tsatsaronis & Ioannis Karamanos, 2021. "Support Vector Machine Algorithms: An Application to Ship Price Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 55-87, January.
    19. David Mayer-Foulkes, 2018. "Efficient Urbanization for Mexican Development," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(10), pages 1-1, October.
    20. Kian Tehranian, 2023. "Can Machine Learning Catch Economic Recessions Using Economic and Market Sentiments?," Papers 2308.16200, arXiv.org.
    21. Andreas Psimopoulos, 2020. "Forecasting Economic Recessions Using Machine Learning:An Empirical Study in Six Countries," South-Eastern Europe Journal of Economics, Association of Economic Universities of South and Eastern Europe and the Black Sea Region, vol. 18(1), pages 40-99.
    22. Jaehyuk Choi & Desheng Ge & Kyu Ho Kang & Sungbin Sohn, 2023. "Yield spread selection in predicting recession probabilities," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1772-1785, November.

  16. Theophilos Papadimitriou & Periklis Gogas & Vasilios Plakandaras, 2013. "Forecasting the NOK/USD Exchange Rate with Machine Learning Techniques," Working Paper series 59_13, Rimini Centre for Economic Analysis.

    Cited by:

    1. Vasilios Plakandaras & Theophilos Papadimitriou & Periklis Gogas & Konstantinos Diamantaras, 2014. "Market Sentiment and Exchange Rate Directional Forecasting," Working Paper series 37_14, Rimini Centre for Economic Analysis.
    2. Christina Christou & Rangan Gupta & Christis Hassapis & Tahir Suleman, 2018. "The role of economic uncertainty in forecasting exchange rate returns and realized volatility: Evidence from quantile predictive regressions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(7), pages 705-719, November.
    3. Rangan Gupta & Vasilios Plakandaras, 2018. "Efficiency in BRICS Currency Markets using Long-Spans of Data: Evidence from Model-Free Tests of Directional Predictability," Working Papers 201836, University of Pretoria, Department of Economics.
    4. Plakandaras, Vasilios & Gupta, Rangan & Wohar, Mark E., 2017. "The depreciation of the pound post-Brexit: Could it have been predicted?," Finance Research Letters, Elsevier, vol. 21(C), pages 206-213.

  17. Ioannis Praggidis & Periklis Gogas & Vasilios Plakandaras & Theophilos Papadimitriou, 2013. "Fiscal shocks and asymmetric effects: a comparative analysis," Papers 1312.2693, arXiv.org.

    Cited by:

    1. Vicente Esteve & Cecilio Tamarit, 2018. "Public debt and economic growth in Spain, 1851–2013," Cliometrica, Springer;Cliometric Society (Association Francaise de Cliométrie), vol. 12(2), pages 219-249, May.
    2. Clement Olalekan Olaniyi, 2020. "Application of Bootstrap Simulation and Asymmetric Causal Approach to Fiscal Deficit-Inflation Nexus," Global Journal of Emerging Market Economies, Emerging Markets Forum, vol. 12(2), pages 123-140, May.
    3. Ahmad, Ahmad Hassan & Aworinde, Olalekan Bashir & Martin, Christopher, 2015. "Threshold cointegration and the short-run dynamics of twin deficit hypothesis in African countries," The Journal of Economic Asymmetries, Elsevier, vol. 12(2), pages 80-91.

  18. Periklis Gogas & Theophilos Papadimitriou & Anna Agrapetidou, 2013. "Forecasting Bank Credit Ratings," Working Paper series 60_13, Rimini Centre for Economic Analysis.

    Cited by:

    1. Li, Jing-Ping & Mirza, Nawazish & Rahat, Birjees & Xiong, Deping, 2020. "Machine learning and credit ratings prediction in the age of fourth industrial revolution," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    2. Zhivaikina, A. & Peresetsky, A., 2017. "Russian Bank Credit Ratings and Bank License Withdrawal 2012-2016," Journal of the New Economic Association, New Economic Association, vol. 36(4), pages 49-80.
    3. Bojing Feng & Wenfang Xue & Bindang Xue & Zeyu Liu, 2020. "Every Corporation Owns Its Image: Corporate Credit Ratings via Convolutional Neural Networks," Papers 2012.03744, arXiv.org.
    4. Valdir Domeneghetti & Fabiano Guasti Lima, 2019. "Strategic direction re-evaluation of bank ratings in Brazil," Economics Bulletin, AccessEcon, vol. 39(2), pages 1336-1347.
    5. Pompella, Maurizio & Dicanio, Antonio, 2017. "Ratings based Inference and Credit Risk: Detecting likely-to-fail Banks with the PC-Mahalanobis Method," Economic Modelling, Elsevier, vol. 67(C), pages 34-44.
    6. GABAN Lucian & RUS IonuÈ› - Marius & FETITA Alin, 2017. "A Model Of Rating Of Eastern European Banks," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 69(3), pages 42-56, August.
    7. Golbayani, Parisa & Florescu, Ionuţ & Chatterjee, Rupak, 2020. "A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    8. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Efterpi Doumpa & Maria Stefanidou, 2020. "Forecasting Credit Ratings of EU Banks," IJFS, MDPI, vol. 8(3), pages 1-15, August.
    9. Parisa Golbayani & Ionuc{t} Florescu & Rupak Chatterjee, 2020. "A comparative study of forecasting Corporate Credit Ratings using Neural Networks, Support Vector Machines, and Decision Trees," Papers 2007.06617, arXiv.org.
    10. John A. Ruddy, 2021. "An Analysis of Bank Financial Strength Ratings and Credit Rating Data," Risks, MDPI, vol. 9(9), pages 1-16, August.
    11. Oliver Takawira & John W. Muteba Mwamba, 2020. "Determinants of Sovereign Credit Ratings: An Application of the Naïve Bayes Classifier," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 8(4), pages 279-299.

  19. Gogas, Periklis & Papadimitriou, Theophilos & Plakandaras, Vasilios, 2013. "Forecasting the insolvency of U.S. banks using Support Vector Machines (SVM) based on Local Learning Feature Selection," DUTH Research Papers in Economics 2-2013, Democritus University of Thrace, Department of Economics.

    Cited by:

    1. Kolari, James W. & López-Iturriaga, Félix J. & Sanz, Ivan Pastor, 2019. "Predicting European bank stress tests: Survival of the fittest," Global Finance Journal, Elsevier, vol. 39(C), pages 44-57.
    2. Theophilos Papadimitriou & Periklis Gogas & Anna Agrapetidou, 2022. "The resilience of the U.S. banking system," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 2819-2835, July.
    3. Santosh Kumar Shrivastav & P. Janaki Ramudu, 2020. "Bankruptcy Prediction and Stress Quantification Using Support Vector Machine: Evidence from Indian Banks," Risks, MDPI, vol. 8(2), pages 1-22, May.
    4. Gogas, Periklis & Papadimitriou, Theophilos & Agrapetidou, Anna, 2018. "Forecasting bank failures and stress testing: A machine learning approach," International Journal of Forecasting, Elsevier, vol. 34(3), pages 440-455.

  20. Periklis Gogas & Theophilos Papadimitriou & Elvira Takli, 2013. "Comparison of Simple Sum and Divisia Monetary Aggregates in GDP Forecasting: A Support Vector Machines Approach," Working Paper series 04_13, Rimini Centre for Economic Analysis.

    Cited by:

    1. Plakandaras, Vasilios & Gupta, Rangan & Papadimitriou, Theophilos & Gogas, Periklis, 2014. "Forecasting the U.S. Real House Price Index," DUTH Research Papers in Economics 10-2014, Democritus University of Thrace, Department of Economics.
    2. Seitz, Franz & Albuquerque, Bruno & Baumann, Ursel, 2015. "The Information Content Of Money And Credit For US Activity," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113066, Verein für Socialpolitik / German Economic Association.
    3. William Barnett & Biyan Tang, 2015. "Chinese Divisia Monetary Index and GDP Nowcasting," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201506, University of Kansas, Department of Economics, revised Nov 2015.
    4. Makram El-Shagi & Kiril Tochkov, 2021. "Divisia Monetary Aggregates for Russia: Money Demand, GDP Nowcasting, and the Price Puzzle," CFDS Discussion Paper Series 2021/1, Center for Financial Development and Stability at Henan University, Kaifeng, Henan, China.
    5. Pragidis, Ioannis & Gogas, Periklis & Plakandaras, Vasilios & Papadimitriou, Theophilos, 2015. "Fiscal shocks and asymmetric effects: A comparative analysis," The Journal of Economic Asymmetries, Elsevier, vol. 12(1), pages 22-33.
    6. Albuquerque, Bruno & Baumann, Ursel & Seitz, Franz, 2016. "What does money and credit tell us about real activity in the United States?," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 328-347.
    7. Muhammad Anees Khan & Kumail Abbas & Mazliham Mohd Su’ud & Anas A. Salameh & Muhammad Mansoor Alam & Nida Aman & Mehreen Mehreen & Amin Jan & Nik Alif Amri Bin Nik Hashim & Roslizawati Che Aziz, 2022. "Application of Machine Learning Algorithms for Sustainable Business Management Based on Macro-Economic Data: Supervised Learning Techniques Approach," Sustainability, MDPI, vol. 14(16), pages 1-14, August.

  21. Gogas, Periklis & Papadimitriou, Theophilos & Plakandaras, Vasilios, 2013. "Public Debt and Private Consumption in OECD countries," DUTH Research Papers in Economics 1-2013, Democritus University of Thrace, Department of Economics, revised 20 Feb 2014.

    Cited by:

    1. Teboho Jeremiah Mosikari & Joel Hinaunye Eita, 2017. "Empirical test of the Ricardian Equivalence in the Kingdom of Lesotho," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1351674-135, January.
    2. Lorenzo Esposito & Giuseppe Mastromatteo, 2019. "Defaultnomics: Making Sense of the Barro-Ricardo Equivalence in a Financialized World," Economics Working Paper Archive wp_933, Levy Economics Institute.
    3. Teboho Jeremiah Mosikari & Mmamontsho Charlotte Senosi & Joel Hinaunye Eita, 2016. "Manufactured exports and economic growth in Southern African Development Community (SADC) region: A panel cointegration approach," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 12(5), pages 266-278, OCTOBER.
    4. Ian P. Cassar & Kurt Davison & Christian Xuereb, 2018. "Does the Ricardian Equivalence Theorem Capture the Consumption Behavior of Maltese Households?," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(12), pages 1-77, December.
    5. Mario Coccia, 2018. "National debts and government deficits within European Monetary Union: Statistical evidence of economic issues," Papers 1806.07830, arXiv.org.
    6. Maria Malmierca-Ordoqui & Luis A. Gil-Alana & Lorenzo Bermejo, 2024. "Private and public debt convergence: a fractional cointegration approach," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 51(1), pages 161-183, February.
    7. Coccia, Mario, 2017. "Asymmetric paths of public debts and of general government deficits across countries within and outside the European monetary unification and economic policy of debt dissolution," The Journal of Economic Asymmetries, Elsevier, vol. 15(C), pages 17-31.

  22. Theophilos Papadimitriou & Periklis Gogas & Benjamin M. Tabak, 2013. "Complex Networks and Banking Systems Supervision," Working Papers Series 306, Central Bank of Brazil, Research Department.

    Cited by:

    1. Solange Maria Guerra & Benjamin Miranda Tabak & Rodrigo Andrés De Souza Penaloza & Rodrigo César De Castro Mirand, 2014. "Systemic Risk Measures," Anais do XLI Encontro Nacional de Economia [Proceedings of the 41st Brazilian Economics Meeting] 124, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    2. Gogas, Periklis & Papadimitriou, Theophilos & Matthaiou, Maria, 2014. "A novel Banking Supervision Method using the Minimum Dominating Set," DUTH Research Papers in Economics 1-2014, Democritus University of Thrace, Department of Economics.
    3. Souza, Sergio Rubens Stancato de & Silva, Thiago Christiano & Tabak, Benjamin Miranda & Guerra, Solange Maria, 2016. "Evaluating systemic risk using bank default probabilities in financial networks," Journal of Economic Dynamics and Control, Elsevier, vol. 66(C), pages 54-75.
    4. He, Fang & Chen, Xi, 2016. "Credit networks and systemic risk of Chinese local financing platforms: Too central or too big to fail?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 158-170.
    5. Sensoy, Ahmet & Tabak, Benjamin M., 2014. "Dynamic spanning trees in stock market networks: The case of Asia-Pacific," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 387-402.
    6. Thiago C. Silva & Diego R. Amancio & Benjamin M. Tabak, 2020. "Modeling Supply-Chain Networks with Firm-to-Firm Wire Transfers," Papers 2001.06889, arXiv.org, revised Aug 2020.
    7. Theophilos Papadimitriou & Periklis Gogas & Georgios Sarantitis, 2016. "Convergence of European Business Cycles: A Complex Networks Approach," Computational Economics, Springer;Society for Computational Economics, vol. 47(2), pages 97-119, February.
    8. Chabot, Miia & Bertrand, Jean-Louis, 2021. "Complexity, interconnectedness and stability: New perspectives applied to the European banking system," Journal of Business Research, Elsevier, vol. 129(C), pages 784-800.
    9. Kocheturov, A. & Batsyn, M. & Pardalos, P., 2015. "Dynamics of Cluster Structures in Stock Market Networks," Journal of the New Economic Association, New Economic Association, vol. 28(4), pages 12-30.
    10. Zappa, Paola & Vu, Duy Q., 2021. "Markets as networks evolving step by step: Relational Event Models for the interbank market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    11. Antonakakis, Nikolaos & Gogas, Periklis & Papadimitriou, Theophilos & Sarantitis, Georgios Antonios, 2016. "International business cycle synchronization since the 1870s: Evidence from a novel network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 286-296.
    12. Silva, Thiago Christiano & de Souza, Sergio Rubens Stancato & Tabak, Benjamin Miranda, 2016. "Network structure analysis of the Brazilian interbank market," Emerging Markets Review, Elsevier, vol. 26(C), pages 130-152.
    13. de Carvalho, Pablo Jose Campos & Gupta, Aparna, 2018. "A network approach to unravel asset price comovement using minimal dependence structure," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 119-132.
    14. Machado, Vicente da Gama & Portugal, Marcelo Savino, 2014. "Measuring inflation persistence in Brazil using a multivariate model," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 68(2), June.
    15. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    16. Gogas, Periklis & Papadimitriou, Theophilos & Matthaiou, Maria-Artemis, 2016. "Bank supervision using the Threshold-Minimum Dominating Set," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 23-35.
    17. Sergio R. S. Souza & Benjamin M. Tabak & Solange M. Guerra, 2013. "Insolvency and Contagion in the Brazilian Interbank Market," Working Papers Series 320, Central Bank of Brazil, Research Department.
    18. Rodrigo César de Castro Miranda & Benjamin Miranda Tabak, 2013. "Contagion Risk within Firm-Bank Bivariate Networks," Working Papers Series 322, Central Bank of Brazil, Research Department.
    19. Apergis, Emmanuel & Apergis, Iraklis & Apergis, Nicholas, 2019. "A new macro stress testing approach for financial realignment in the Eurozone," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 61(C), pages 52-80.
    20. Barbi, A.Q. & Prataviera, G.A., 2019. "Nonlinear dependencies on Brazilian equity network from mutual information minimum spanning trees," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 876-885.

  23. Papadimitriou, Theophilos & Gogas, Periklis & Plakandaras, Vasilios, 2013. "Forecasting daily and monthly exchange rates with machine learning techniques," DUTH Research Papers in Economics 3-2013, Democritus University of Thrace, Department of Economics, revised 07 Apr 2015.

    Cited by:

    1. Biswas, Rita & Li, Xiao & Piccotti, Louis R., 2023. "Do macroeconomic variables drive exchange rates independently?," Finance Research Letters, Elsevier, vol. 52(C).
    2. Mohammad Abdullah & Mohammad Ashraful Ferdous Chowdhury & Ajim Uddin & Syed Moudud‐Ul‐Huq, 2023. "Forecasting nonperforming loans using machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1664-1689, November.
    3. Erdinc Akyildirim & Oguzhan Cepni & Shaen Corbet & Gazi Salah Uddin, 2023. "Forecasting mid-price movement of Bitcoin futures using machine learning," Annals of Operations Research, Springer, vol. 330(1), pages 553-584, November.
    4. Rho Caterina & Fernández Raúl & Palma Brenda, 2021. "A Sentiment-based Risk Indicator for the Mexican Financial Sector," Working Papers 2021-04, Banco de México.
    5. Plakandaras, Vasilios & Gogas, Periklis & Papadimitriou, Theophilos & Gupta, Rangan, 2019. "A re-evaluation of the term spread as a leading indicator," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 476-492.
    6. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2016. "The Term Premium as a Leading Macroeconomic Indicator," Working Papers 201613, University of Pretoria, Department of Economics.
    7. Christophe Amat & Tomasz Michalski & Gilles Stoltz, 2018. "Fundamentals and exchange rate forecastability with simple machine learning methods," Working Papers halshs-01003914, HAL.
    8. Vasilios Plakandaras & Theophilos Papadimitriou & Periklis Gogas & Konstantinos Diamantaras, 2014. "Market Sentiment and Exchange Rate Directional Forecasting," Working Paper series 37_14, Rimini Centre for Economic Analysis.
    9. Sun, Shaolong & Wang, Shouyang & Wei, Yunjie, 2019. "A new multiscale decomposition ensemble approach for forecasting exchange rates," Economic Modelling, Elsevier, vol. 81(C), pages 49-58.
    10. He Jiang, 2023. "Forecasting global solar radiation using a robust regularization approach with mixture kernels," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 1989-2010, December.
    11. Rangan Gupta & Tahir Suleman & Mark E. Wohar, 2019. "Exchange rate returns and volatility: the role of time-varying rare disaster risks," The European Journal of Finance, Taylor & Francis Journals, vol. 25(2), pages 190-203, January.
    12. Rangan Gupta & Vasilios Plakandaras, 2018. "Efficiency in BRICS Currency Markets using Long-Spans of Data: Evidence from Model-Free Tests of Directional Predictability," Working Papers 201836, University of Pretoria, Department of Economics.
    13. Plakandaras, Vasilios & Gupta, Rangan & Wohar, Mark E., 2017. "The depreciation of the pound post-Brexit: Could it have been predicted?," Finance Research Letters, Elsevier, vol. 21(C), pages 206-213.
    14. Bangzhu Zhu & Shunxin Ye & Ping Wang & Julien Chevallier & Yi‐Ming Wei, 2022. "Forecasting carbon price using a multi‐objective least squares support vector machine with mixture kernels," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 100-117, January.
    15. Theophilos Papadimitriou & Periklis Gogas & Athanasios Fotios Athanasiou, 2020. "Forecasting S&P 500 spikes: an SVM approach," Digital Finance, Springer, vol. 2(3), pages 241-258, December.
    16. Plakandaras, Vasilios & Ji, Qiang, 2022. "Intrinsic decompositions in gold forecasting," Journal of Commodity Markets, Elsevier, vol. 28(C).
    17. Paolo Fornaro & Henri Luomaranta, 2020. "Nowcasting Finnish real economic activity: a machine learning approach," Empirical Economics, Springer, vol. 58(1), pages 55-71, January.
    18. Tasadduq Imam, 2021. "Model selection for one‐day‐ahead AUD/USD, AUD/EUR forecasts," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 1808-1824, April.
    19. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou, 2021. "Gold Against the Machine," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 5-28, January.

  24. Plakandaras, Vasilios & Papadimitriou, Theophilos & Gogas, Periklis, 2012. "Directional forecasting in financial time series using support vector machines: The USD/Euro exchange rate," DUTH Research Papers in Economics 5-2012, Democritus University of Thrace, Department of Economics.

    Cited by:

    1. Kea BARET & Theophilos PAPADIMITRIOU, 2019. "On the Stability and Growth Pact compliance: what is predictable with machine learning?," Working Papers of BETA 2019-48, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    2. Christina Christou & Rangan Gupta & Christis Hassapis & Tahir Suleman, 2018. "The role of economic uncertainty in forecasting exchange rate returns and realized volatility: Evidence from quantile predictive regressions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(7), pages 705-719, November.
    3. Pragidis, Ioannis & Gogas, Periklis & Plakandaras, Vasilios & Papadimitriou, Theophilos, 2015. "Fiscal shocks and asymmetric effects: A comparative analysis," The Journal of Economic Asymmetries, Elsevier, vol. 12(1), pages 22-33.
    4. Rangan Gupta & Vasilios Plakandaras, 2018. "Efficiency in BRICS Currency Markets using Long-Spans of Data: Evidence from Model-Free Tests of Directional Predictability," Working Papers 201836, University of Pretoria, Department of Economics.

Articles

  1. Periklis Gogas & Theophilos Papadimitriou, 2022. "Emerging Trends in Energy Economics," Energies, MDPI, vol. 15(14), pages 1-2, July.

    Cited by:

    1. Piotr F. Borowski, 2022. "Mitigating Climate Change and the Development of Green Energy versus a Return to Fossil Fuels Due to the Energy Crisis in 2022," Energies, MDPI, vol. 15(24), pages 1-16, December.
    2. Wioletta Czemiel-Grzybowska, 2022. "Conceptualization and Mapping of Predictors of Technological Entrepreneurship Growth in a Changing Economic Environment (COVID-19) from the Polish Energy Sector," Energies, MDPI, vol. 15(18), pages 1-14, September.

  2. Periklis Gogas & Theophilos Papadimitriou & Emmanouil Sofianos, 2022. "Forecasting unemployment in the euro area with machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 551-566, April.

    Cited by:

    1. Mustafa Yurtsever, 2023. "Unemployment rate forecasting: LSTM-GRU hybrid approach," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 57(1), pages 1-9, December.
    2. Sanusi, Olajide I. & Safi, Samir K. & Adeeko, Omotara & Tabash, Mosab I., 2022. "Forecasting agricultural commodity price using different models: a case study of widely consumed grains in Nigeria," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 8(2), June.

  3. Emmanouil Sofianos & Periklis Gogas & Theophilos Papadimitriou, 2022. "Mind the gap: forecasting euro-area output gaps with machine learning," Applied Economics Letters, Taylor & Francis Journals, vol. 29(19), pages 1824-1828, November.

    Cited by:

    1. Mustafa Yurtsever, 2023. "Unemployment rate forecasting: LSTM-GRU hybrid approach," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 57(1), pages 1-9, December.

  4. Anna Agrapetidou & Paulos Charonyktakis & Periklis Gogas & Theophilos Papadimitriou & Ioannis Tsamardinos, 2021. "An AutoML application to forecasting bank failures," Applied Economics Letters, Taylor & Francis Journals, vol. 28(1), pages 5-9, January.

    Cited by:

    1. Teddy Lazebnik & Tzach Fleischer & Amit Yaniv-Rosenfeld, 2023. "Benchmarking Biologically-Inspired Automatic Machine Learning for Economic Tasks," Sustainability, MDPI, vol. 15(14), pages 1-9, July.

  5. Periklis Gogas & Theophilos Papadimitriou, 2021. "Machine Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 1-4, January.

    Cited by:

    1. Bilgin, Rumeysa, 2023. "The Selection Of Control Variables In Capital Structure Research With Machine Learning," SocArXiv e26qf, Center for Open Science.
    2. Ahmad El Majzoub & Fethi A. Rabhi & Walayat Hussain, 2023. "Evaluating interpretable machine learning predictions for cryptocurrencies," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 30(3), pages 137-149, July.
    3. Heyam H. Al-Baity, 2023. "The Artificial Intelligence Revolution in Digital Finance in Saudi Arabia: A Comprehensive Review and Proposed Framework," Sustainability, MDPI, vol. 15(18), pages 1-16, September.
    4. Sergio Mariotti, 2021. "Forging a new alliance between economics and engineering," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 48(4), pages 551-572, December.
    5. Nwafor, Chioma Ngozi & Nwafor, Obumneme Zimuzor, 2023. "Determinants of non-performing loans: An explainable ensemble and deep neural network approach," Finance Research Letters, Elsevier, vol. 56(C).
    6. Toan Luu Duc Huynh, 2023. "When Elon Musk Changes his Tone, Does Bitcoin Adjust Its Tune?," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 639-661, August.
    7. Afaq Khattak & Hamad Almujibah & Ahmed Elamary & Caroline Mongina Matara, 2022. "Interpretable Dynamic Ensemble Selection Approach for the Prediction of Road Traffic Injury Severity: A Case Study of Pakistan’s National Highway N-5," Sustainability, MDPI, vol. 14(19), pages 1-18, September.
    8. Ajitha Kumari Vijayappan Nair Biju & Ann Susan Thomas & J Thasneem, 2024. "Examining the research taxonomy of artificial intelligence, deep learning & machine learning in the financial sphere—a bibliometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 849-878, February.
    9. Steve J. Bickley & Benno Torgler, 2021. "Behavioural Economics, What Have we Missed? Exploring “Classical” Behavioural Economics Roots in AI, Cognitive Psychology, and Complexity Theory," CREMA Working Paper Series 2021-21, Center for Research in Economics, Management and the Arts (CREMA).
    10. Emmanouil Sofianos & Emmanouil Zaganidis & Theophilos Papadimitriou & Periklis Gogas, 2024. "Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms," Energies, MDPI, vol. 17(6), pages 1-14, March.

  6. Dimitrios Mouchtaris & Emmanouil Sofianos & Periklis Gogas & Theophilos Papadimitriou, 2021. "Forecasting Natural Gas Spot Prices with Machine Learning," Energies, MDPI, vol. 14(18), pages 1-13, September.

    Cited by:

    1. Renchu Guan & Aoqing Wang & Yanchun Liang & Jiasheng Fu & Xiaosong Han, 2022. "International Natural Gas Price Trends Prediction with Historical Prices and Related News," Energies, MDPI, vol. 15(10), pages 1-14, May.
    2. Sun-Feel Yang & So-Won Choi & Eul-Bum Lee, 2023. "A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices," Energies, MDPI, vol. 16(11), pages 1-39, May.
    3. Yadong Pei & Chiou-Jye Huang & Yamin Shen & Mingyue Wang, 2023. "A Novel Model for Spot Price Forecast of Natural Gas Based on Temporal Convolutional Network," Energies, MDPI, vol. 16(5), pages 1-15, February.
    4. Periklis Gogas & Theophilos Papadimitriou, 2022. "Emerging Trends in Energy Economics," Energies, MDPI, vol. 15(14), pages 1-2, July.

  7. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou, 2021. "Gold Against the Machine," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 5-28, January.

    Cited by:

    1. Perry Sadorsky, 2021. "Predicting Gold and Silver Price Direction Using Tree-Based Classifiers," JRFM, MDPI, vol. 14(5), pages 1-21, April.
    2. Lu, Xinjie & Ma, Feng & Xu, Jin & Zhang, Zehui, 2022. "Oil futures volatility predictability: New evidence based on machine learning models11All the authors contribute to the paper equally," International Review of Financial Analysis, Elsevier, vol. 83(C).

  8. Theophilos Papadimitriou & Periklis Gogas & Athanasios Fotios Athanasiou, 2020. "Forecasting S&P 500 spikes: an SVM approach," Digital Finance, Springer, vol. 2(3), pages 241-258, December.

    Cited by:

    1. Firuz Kamalov & Linda Smail & Ikhlaas Gurrib, 2021. "Forecasting with Deep Learning: S&P 500 index," Papers 2103.14080, arXiv.org.

  9. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Efterpi Doumpa & Maria Stefanidou, 2020. "Forecasting Credit Ratings of EU Banks," IJFS, MDPI, vol. 8(3), pages 1-15, August.

    Cited by:

    1. Ahmed, Shamima & Alshater, Muneer M. & Ammari, Anis El & Hammami, Helmi, 2022. "Artificial intelligence and machine learning in finance: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 61(C).

  10. Plakandaras, Vasilios & Papadimitriou, Theophilos & Gogas, Periklis, 2019. "Forecasting transportation demand for the U.S. market," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 195-214.

    Cited by:

    1. Weifan Zhong & Lijing Du, 2023. "Predicting Traffic Casualties Using Support Vector Machines with Heuristic Algorithms: A Study Based on Collision Data of Urban Roads," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
    2. Cheng, Long & Yang, Junjian & Chen, Xuewu & Cao, Mengqiu & Zhou, Hang & Sun, Yu, 2020. "How could the station-based bike sharing system and the free-floating bike sharing system be coordinated?," Journal of Transport Geography, Elsevier, vol. 89(C).

  11. Plakandaras, Vasilios & Gogas, Periklis & Papadimitriou, Theophilos & Gupta, Rangan, 2019. "A re-evaluation of the term spread as a leading indicator," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 476-492.

    Cited by:

    1. Joseph G. Haubrich, 2020. "Does the Yield Curve Predict Output?," Working Papers 20-34, Federal Reserve Bank of Cleveland.
    2. Çepni, Oğuzhan & Guney, I. Ethem & Gupta, Rangan & Wohar, Mark E., 2020. "The role of an aligned investor sentiment index in predicting bond risk premia of the U.S," Journal of Financial Markets, Elsevier, vol. 51(C).

  12. Gogas, Periklis & Papadimitriou, Theophilos & Agrapetidou, Anna, 2018. "Forecasting bank failures and stress testing: A machine learning approach," International Journal of Forecasting, Elsevier, vol. 34(3), pages 440-455.

    Cited by:

    1. Jean-Armand Gnagne & Kevin Moran, 2020. "Forecasting Bank Failures in a Data-Rich Environment," Working Papers 20-13, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    2. Plakandaras, Vasilios & Gogas, Periklis & Papadimitriou, Theophilos & Gupta, Rangan, 2019. "A re-evaluation of the term spread as a leading indicator," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 476-492.
    3. Andrés Giovanni Camacho Ardila & Federico Hernández Álvarez & Luis Ignacio Román de la Sancha, 2023. "Ciclos en el Sector Bancario Mexicano: un Índice Coincidente (CP1G7) vía ACP," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 18(4), pages 1-25, Octubre -.
    4. Kea BARET, 2021. "Fiscal rules’ compliance and Social Welfare," Working Papers of BETA 2021-38, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    5. Małgorzata Iwanicz-Drozdowska & Krzysztof Jackowicz & Maciej Karczmarczyk, 2021. "“The Crooked Smile of TCR†: Banks’ Solvency and Restructuring Costs in the European Banking Industry," SAGE Open, , vol. 11(3), pages 21582440211, September.
    6. Yi Cao & Xiaoquan Liu & Jia Zhai & Shan Hua, 2022. "A two‐stage Bayesian network model for corporate bankruptcy prediction," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 455-472, January.
    7. Theophilos Papadimitriou & Periklis Gogas & Anna Agrapetidou, 2022. "The resilience of the U.S. banking system," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 2819-2835, July.
    8. Kea BARET & Theophilos PAPADIMITRIOU, 2019. "On the Stability and Growth Pact compliance: what is predictable with machine learning?," Working Papers of BETA 2019-48, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    9. Buckmann, Marcus & Haldane, Andy & Hüser, Anne-Caroline, 2021. "Comparing minds and machines: implications for financial stability," Bank of England working papers 937, Bank of England.
    10. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.
    11. Anton Gerunov, 2023. "Modern Approaches To Forecasting Firm Default Rates Over The Short To Medium Term: An Application To A Panel Of Polish Companies," Yearbook of the Faculty of Economics and Business Administration, Sofia University, Faculty of Economics and Business Administration, Sofia University St Kliment Ohridski - Bulgaria, vol. 22(1), pages 5-15, October.
    12. Manthoulis, Georgios & Doumpos, Michalis & Zopounidis, Constantin & Galariotis, Emilios, 2020. "An ordinal classification framework for bank failure prediction: Methodology and empirical evidence for US banks," European Journal of Operational Research, Elsevier, vol. 282(2), pages 786-801.
    13. Veganzones, David & Séverin, Eric & Chlibi, Souhir, 2023. "Influence of earnings management on forecasting corporate failure," International Journal of Forecasting, Elsevier, vol. 39(1), pages 123-143.
    14. Polyzos, Stathis & Samitas, Aristeidis & Katsaiti, Marina-Selini, 2020. "Who is unhappy for Brexit? A machine-learning, agent-based study on financial instability," International Review of Financial Analysis, Elsevier, vol. 72(C).
    15. Cullen F. Goenner, 2020. "Uncertain times and early predictions of bank failure," The Financial Review, Eastern Finance Association, vol. 55(4), pages 583-601, November.
    16. Caifeng Liu & Wenfeng Pan & Hongcheng Zhou, 2023. "RCML: A Novel Algorithm for Regressing Price Movement during Commodity Futures Stress Testing Based on Machine Learning," JRFM, MDPI, vol. 16(6), pages 1-12, May.
    17. Petropoulos, Anastasios & Siakoulis, Vasilis & Stavroulakis, Evangelos & Vlachogiannakis, Nikolaos E., 2020. "Predicting bank insolvencies using machine learning techniques," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1092-1113.
    18. Katsafados, Apostolos G. & Androutsopoulos, Ion & Chalkidis, Ilias & Fergadiotis, Manos & Leledakis, George N. & Pyrgiotakis, Emmanouil G., 2020. "Textual Information and IPO Underpricing: A Machine Learning Approach," MPRA Paper 103813, University Library of Munich, Germany.
    19. Periklis Gogas & Theophilos Papadimitriou & Maria-Artemis Matthaiou, 2022. "Supervision of Banking Networks Using the Multivariate Threshold-Minimum Dominating Set (mT-MDS)," JRFM, MDPI, vol. 15(6), pages 1-13, June.
    20. Jean Armand Gnagne & Kevin Moran, 2018. "Monitoring Bank Failures in a Data-Rich Environment," Cahiers de recherche 1815, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    21. Bracke, Philippe & Datta, Anupam & Jung, Carsten & Sen, Shayak, 2019. "Machine learning explainability in finance: an application to default risk analysis," Bank of England working papers 816, Bank of England.
    22. Li Xian Liu & Shuangzhe Liu & Milind Sathye, 2021. "Predicting Bank Failures: A Synthesis of Literature and Directions for Future Research," JRFM, MDPI, vol. 14(10), pages 1-24, October.
    23. Kristóf, Tamás & Virág, Miklós, 2022. "EU-27 bank failure prediction with C5.0 decision trees and deep learning neural networks," Research in International Business and Finance, Elsevier, vol. 61(C).
    24. Samitas, Aristeidis & Kampouris, Elias & Kenourgios, Dimitris, 2020. "Machine learning as an early warning system to predict financial crisis," International Review of Financial Analysis, Elsevier, vol. 71(C).
    25. Suss, Joel & Treitel, Henry, 2019. "Predicting bank distress in the UK with machine learning," Bank of England working papers 831, Bank of England.

  13. Athanasia Dimitriadou & Periklis Gogas & Theophilos Papadimitriou & Vasilios Plakandaras, 2018. "Oil Market Efficiency under a Machine Learning Perspective," Forecasting, MDPI, vol. 1(1), pages 1-12, October.

    Cited by:

    1. Mangku Purnomo & Fenna Otten & Heiko Faust, 2018. "Indonesian Traditional Market Flexibility Amidst State Promoted Market Competition," Social Sciences, MDPI, vol. 7(11), pages 1-17, November.
    2. Yu-Wei Chen & Chui-Yu Chiu & Mu-Chun Hsiao, 2021. "An Auxiliary Index for Reducing Brent Crude Investment Risk—Evaluating the Price Relationships between Brent Crude and Commodities," Sustainability, MDPI, vol. 13(9), pages 1-45, April.

  14. Georgios Antonios Sarantitis & Theophilos Papadimitriou & Periklis Gogas, 2018. "A Network Analysis of the United Kingdom’s Consumer Price Index," Computational Economics, Springer;Society for Computational Economics, vol. 51(2), pages 173-193, February.
    See citations under working paper version above.
  15. Gogas, Periklis & Gupta, Rangan & Miller, Stephen M. & Papadimitriou, Theophilos & Sarantitis, Georgios Antonios, 2017. "Income inequality: A complex network analysis of US states," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 423-437.

    Cited by:

    1. Benra, Felipe & Nahuelhual, Laura, 2019. "A trilogy of inequalities: Land ownership, forest cover and ecosystem services distribution," Land Use Policy, Elsevier, vol. 82(C), pages 247-257.

  16. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2017. "The Informational Content of the Term Spread in Forecasting the US Inflation Rate: A Nonlinear Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(2), pages 109-121, March.
    See citations under working paper version above.
  17. Antonakakis, Nikolaos & Gogas, Periklis & Papadimitriou, Theophilos & Sarantitis, Georgios Antonios, 2016. "International business cycle synchronization since the 1870s: Evidence from a novel network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 286-296.
    See citations under working paper version above.
  18. Theophilos Papadimitriou & Periklis Gogas & Vasilios Plakandaras, 2016. "Testing Exchange Rate Models in a Small Open Economy: an SVR Approach," Bulletin of Applied Economics, Risk Market Journals, vol. 3(2), pages 9-29.

    Cited by:

    1. Christina Christou & Rangan Gupta & Christis Hassapis & Tahir Suleman, 2018. "The role of economic uncertainty in forecasting exchange rate returns and realized volatility: Evidence from quantile predictive regressions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(7), pages 705-719, November.
    2. Rangan Gupta & Vasilios Plakandaras, 2018. "Efficiency in BRICS Currency Markets using Long-Spans of Data: Evidence from Model-Free Tests of Directional Predictability," Working Papers 201836, University of Pretoria, Department of Economics.

  19. Gogas, Periklis & Papadimitriou, Theophilos & Matthaiou, Maria-Artemis, 2016. "Bank supervision using the Threshold-Minimum Dominating Set," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 23-35.

    Cited by:

    1. Lin, Geng & Guan, Jian & Feng, Huibin, 2018. "An ILP based memetic algorithm for finding minimum positive influence dominating sets in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 199-209.

  20. Theophilos Papadimitriou & Periklis Gogas & Georgios Sarantitis, 2016. "Convergence of European Business Cycles: A Complex Networks Approach," Computational Economics, Springer;Society for Computational Economics, vol. 47(2), pages 97-119, February.
    See citations under working paper version above.
  21. Periklis Gogas & Theophilos Papadimitriou & Efthymia Chrysanthidou, 2015. "Yield Curve Point Triplets in Recession Forecasting," International Finance, Wiley Blackwell, vol. 18(2), pages 207-226, June.

    Cited by:

    1. Eric Hillebrand & Huiyu Huang & Tae-Hwy Lee & Canlin Li, 2018. "Using the Entire Yield Curve in Forecasting Output and Inflation," Econometrics, MDPI, vol. 6(3), pages 1-27, August.
    2. Oguzhan Cepni & Rangan Gupta & Cenk C. Karahan & Brian M. Lucey, 2020. "Oil Price Shocks and Yield Curve Dynamics in Emerging Markets," Working Papers 202036, University of Pretoria, Department of Economics.
    3. Vasilios Plakandaras & Juncal Cunado & Rangan Gupta & Mark E. Wohar, 2016. "Do Leading Indicators Forecast U.S. Recessions? A Nonlinear Re-Evaluation Using Historical Data," Working Papers 201685, University of Pretoria, Department of Economics.
    4. Vrontos, Spyridon D. & Galakis, John & Vrontos, Ioannis D., 2021. "Modeling and predicting U.S. recessions using machine learning techniques," International Journal of Forecasting, Elsevier, vol. 37(2), pages 647-671.
    5. Bouri, Elie & Demirer, Riza & Gupta, Rangan & Wohar, Mark E., 2021. "Gold, platinum and the predictability of bond risk premia," Finance Research Letters, Elsevier, vol. 38(C).

  22. Pragidis, Ioannis & Gogas, Periklis & Plakandaras, Vasilios & Papadimitriou, Theophilos, 2015. "Fiscal shocks and asymmetric effects: A comparative analysis," The Journal of Economic Asymmetries, Elsevier, vol. 12(1), pages 22-33.
    See citations under working paper version above.
  23. Plakandaras, Vasilios & Gupta, Rangan & Gogas, Periklis & Papadimitriou, Theophilos, 2015. "Forecasting the U.S. real house price index," Economic Modelling, Elsevier, vol. 45(C), pages 259-267.
    See citations under working paper version above.
  24. Plakandaras, Vasilios & Papadimitriou, Theophilos & Gogas, Periklis & Diamantaras, Konstantinos, 2015. "Market sentiment and exchange rate directional forecasting," Algorithmic Finance, IOS Press, vol. 4(1-2), pages 69-79.
    See citations under working paper version above.
  25. Vasilios Plakandaras & Periklis Gogas & Rangan Gupta & Theophilos Papadimitriou, 2015. "US inflation dynamics on long-range data," Applied Economics, Taylor & Francis Journals, vol. 47(36), pages 3874-3890, August.
    See citations under working paper version above.
  26. Periklis Gogas & Theophilos Papadimitriou & Maria Matthaiou & Efthymia Chrysanthidou, 2015. "Yield Curve and Recession Forecasting in a Machine Learning Framework," Computational Economics, Springer;Society for Computational Economics, vol. 45(4), pages 635-645, April.
    See citations under working paper version above.
  27. Vasilios Plakandaras & Theophilos Papadimitriou & Periklis Gogas, 2015. "Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(7), pages 560-573, November.
    See citations under working paper version above.
  28. Gogas, Periklis & Plakandaras, Vasilios & Papadimitriou, Theophilos, 2014. "Public debt and private consumption in OECD countries," The Journal of Economic Asymmetries, Elsevier, vol. 11(C), pages 1-7.
    See citations under working paper version above.
  29. Periklis Gogas & Theophilos Papadimitriou & Anna Agrapetidou, 2014. "Forecasting bank credit ratings," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 15(2), pages 195-209, March.
    See citations under working paper version above.
  30. Papadimitriou, Theophilos & Gogas, Periklis & Stathakis, Efthimios, 2014. "Forecasting energy markets using support vector machines," Energy Economics, Elsevier, vol. 44(C), pages 135-142.

    Cited by:

    1. Thrampoulidis, Emmanouil & Mavromatidis, Georgios & Lucchi, Aurelien & Orehounig, Kristina, 2021. "A machine learning-based surrogate model to approximate optimal building retrofit solutions," Applied Energy, Elsevier, vol. 281(C).
    2. Li, Zheng & Zhou, Bo & Hensher, David A., 2022. "Forecasting automobile gasoline demand in Australia using machine learning-based regression," Energy, Elsevier, vol. 239(PD).
    3. Plakandaras, Vasilios & Gupta, Rangan & Papadimitriou, Theophilos & Gogas, Periklis, 2014. "Forecasting the U.S. Real House Price Index," DUTH Research Papers in Economics 10-2014, Democritus University of Thrace, Department of Economics.
    4. Emilio Colombo & Matteo Pelagatti, 2019. "Statistical Learning and Exchange Rate Forecasting," DISEIS - Quaderni del Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo dis1901, Università Cattolica del Sacro Cuore, Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo (DISEIS).
    5. F. Cordoni, 2020. "A comparison of modern deep neural network architectures for energy spot price forecasting," Digital Finance, Springer, vol. 2(3), pages 189-210, December.
    6. Wang, Delu & Wang, Yadong & Song, Xuefeng & Liu, Yun, 2018. "Coal overcapacity in China: Multiscale analysis and prediction," Energy Economics, Elsevier, vol. 70(C), pages 244-257.
    7. Emilio, Colombo & Gianfranco, Forte & Roberto, Rossignoli, 2016. "Still crazy after all these years: the returns on carry trade," Working Papers 327, University of Milano-Bicocca, Department of Economics, revised 07 Feb 2016.
    8. Simon Pezzutto & Gianluca Grilli & Stefano Zambotti & Stefan Dunjic, 2018. "Forecasting Electricity Market Price for End Users in EU28 until 2020—Main Factors of Influence," Energies, MDPI, vol. 11(6), pages 1-18, June.
    9. Yixi Xue & Jie Ren & Xiaohang Bi, 2019. "Impact of Influencing Factors on CO 2 Emissions in the Yangtze River Delta during Urbanization," Sustainability, MDPI, vol. 11(15), pages 1-19, August.
    10. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    11. Baruník, Jozef & Malinská, Barbora, 2016. "Forecasting the term structure of crude oil futures prices with neural networks," Applied Energy, Elsevier, vol. 164(C), pages 366-379.
    12. Alexander Ryota Keeley, Kenichi Matsumoto, Kenta Tanaka, Yogi Sugiawan, and Shunsuke Managi, 2020. "The Impact of Renewable Energy Generation on the Spot Market Price in Germany: Ex-Post Analysis using Boosting Method," The Energy Journal, International Association for Energy Economics, vol. 0(Special I).
    13. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
    14. Emilio Colombo & Gianfranco Forte & Roberto Rossignoli, 2017. "Carry trade returns with Support Vector Machines," DISEIS - Quaderni del Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo dis1705, Università Cattolica del Sacro Cuore, Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo (DISEIS).
    15. Claudio Monteiro & Ignacio J. Ramirez-Rosado & L. Alfredo Fernandez-Jimenez, 2018. "Probabilistic Electricity Price Forecasting Models by Aggregation of Competitive Predictors," Energies, MDPI, vol. 11(5), pages 1-25, April.
    16. Simon Pezzutto & Reza Fazeli & Matteo De Felice & Wolfram Sparber, 2016. "Future development of the air-conditioning market in Europe: an outlook until 2020," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 5(6), pages 649-669, November.
    17. Zeng, Bo & Li, Chuan, 2016. "Forecasting the natural gas demand in China using a self-adapting intelligent grey model," Energy, Elsevier, vol. 112(C), pages 810-825.
    18. Rubaszek Michal & Karolak Zuzanna & Kwas Marek & Uddin Gazi Salah, 2020. "The role of the threshold effect for the dynamics of futures and spot prices of energy commodities," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(5), pages 1-20, December.
    19. Mengistu, Mulu Getachew & Simane, Belay & Eshete, Getachew & Workneh, Tilahun Seyoum, 2016. "Factors affecting households' decisions in biogas technology adoption, the case of Ofla and Mecha Districts, northern Ethiopia," Renewable Energy, Elsevier, vol. 93(C), pages 215-227.
    20. Jikhan Jeong, 2020. "Identifying Consumer Preferences from User- and Crowd-Generated Digital Footprints on Amazon.com by Leveraging Machine Learning and Natural Language Processing," 2020 Papers pje208, Job Market Papers.
    21. Zuzanna Karolak, 2021. "Energy prices forecasting using nonlinear univariate models," Bank i Kredyt, Narodowy Bank Polski, vol. 52(6), pages 577-598.
    22. Wang, Bin & Wang, Jun, 2020. "Energy futures and spots prices forecasting by hybrid SW-GRU with EMD and error evaluation," Energy Economics, Elsevier, vol. 90(C).
    23. Zeng, Sheng & Su, Bin & Zhang, Minglong & Gao, Yuan & Liu, Jun & Luo, Song & Tao, Qingmei, 2021. "Analysis and forecast of China's energy consumption structure," Energy Policy, Elsevier, vol. 159(C).
    24. Leehter Yao & Fazida Hanim Hashim & Chien-Chi Lai, 2020. "Dynamic Residential Energy Management for Real-Time Pricing," Energies, MDPI, vol. 13(10), pages 1-15, May.
    25. Chuntian Cheng & Bin Luo & Shumin Miao & Xinyu Wu, 2016. "Mid-Term Electricity Market Clearing Price Forecasting with Sparse Data: A Case in Newly-Reformed Yunnan Electricity Market," Energies, MDPI, vol. 9(10), pages 1-22, October.
    26. Manickavasagam, Jeevananthan & Visalakshmi, S. & Apergis, Nicholas, 2020. "A novel hybrid approach to forecast crude oil futures using intraday data," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    27. Cheng, Fangzheng & Li, Tian & Wei, Yi-ming & Fan, Tijun, 2019. "The VEC-NAR model for short-term forecasting of oil prices," Energy Economics, Elsevier, vol. 78(C), pages 656-667.
    28. Wen-Ze Wu & Tao Zhang & Chengli Zheng, 2019. "A Novel Optimized Nonlinear Grey Bernoulli Model for Forecasting China’s GDP," Complexity, Hindawi, vol. 2019, pages 1-10, October.
    29. Duan, Huiming & Pang, Xinyu, 2021. "A multivariate grey prediction model based on energy logistic equation and its application in energy prediction in China," Energy, Elsevier, vol. 229(C).

  31. Periklis Gogas & Theophilos Papadimitriou & Elvira Takli, 2013. "Comparison of simple sum and Divisia monetary aggregates in GDP forecasting: a support vector machines approach," Economics Bulletin, AccessEcon, vol. 33(2), pages 1101-1115.
    See citations under working paper version above.
  32. Theophilos Papadimitriou & Periklis Gogas & Vasilios Plakandaras & John C. 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, Inderscience Enterprises Ltd, vol. 3(1/2), pages 83-90.
    See citations under working paper version above.
  33. Papadimitriou, Theophilos & Gogas, Periklis & Tabak, Benjamin M., 2013. "Complex networks and banking systems supervision," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4429-4434.
    See citations under working paper version above.

Chapters

  1. Theophilos Papadimitriou & Periklis Gogas & Georgios Antonios Sarantitis, 2014. "European Business Cycle Synchronization: A Complex Network Perspective," Springer Optimization and Its Applications, in: Valery A. Kalyagin & Panos M. Pardalos & Themistocles M. Rassias (ed.), Network Models in Economics and Finance, edition 127, pages 265-275, Springer.
    See citations under working paper version above.Sorry, no citations of chapters recorded.
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