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Predicting sovereign debt crises using artificial neural networks: A comparative approach

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Cited by:

  1. Tölö, Eero, 2019. "Predicting systemic financial crises with recurrent neural networks," Bank of Finland Research Discussion Papers 14/2019, Bank of Finland.
  2. Dawood, Mary & Horsewood, Nicholas & Strobel, Frank, 2017. "Predicting sovereign debt crises: An Early Warning System approach," Journal of Financial Stability, Elsevier, vol. 28(C), pages 16-28.
  3. Makram El-Shagi & Gregor Von Schweinitz, 2016. "Qual Var Revisited: Good Forecast, Bad Story," Journal of Applied Economics, Taylor & Francis Journals, vol. 19(2), pages 293-321, November.
  4. Petr Hájek & Michal Střižík & Pavel Praks & Petr Kadeřábek, 2009. "Možnosti využití přístupu latentní sémantiky při předpovídání finančních krizí [Possibilities of Financial Crises Forecasting with Latent Semantic Indexing]," Politická ekonomie, Prague University of Economics and Business, vol. 2009(6), pages 754-768.
  5. Mioara CHIRITA & Daniela SARPE, 2011. "Usefulness of Artificial Neural Networks for Predicting Financial and Economic Crisis," Risk in Contemporary Economy, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, pages 44-48.
  6. Sebastián Nieto-Parra, 2009. "Who Saw Sovereign Debt Crises Coming?," Economía Journal, The Latin American and Caribbean Economic Association - LACEA, vol. 0(Fall 2009), pages 125-169, August.
  7. Patrycja Klusak & Matthew Agarwala & Matt Burke & Moritz Kraemer & Kamiar Mohaddes, 2023. "Rising Temperatures, Falling Ratings: The Effect of Climate Change on Sovereign Creditworthiness," Management Science, INFORMS, vol. 69(12), pages 7468-7491, December.
  8. León, Carlos & Barucca, Paolo & Acero, Oscar & Gage, Gerardo & Ortega, Fabio, 2020. "Pattern recognition of financial institutions’ payment behavior," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
  9. Sevim, Cuneyt & Oztekin, Asil & Bali, Ozkan & Gumus, Serkan & Guresen, Erkam, 2014. "Developing an early warning system to predict currency crises," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1095-1104.
  10. Maximilian Gobel & Tanya Araújo, 2020. "Indicators of Economic Crises: A Data-Driven Clustering Approach," Working Papers REM 2020/0128, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
  11. Arazmuradov, Annageldy, 2016. "Assessing sovereign debt default by efficiency," The Journal of Economic Asymmetries, Elsevier, vol. 13(C), pages 100-113.
  12. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
  13. Jorge M. Uribe, 2023. ""Fiscal crises and climate change"," IREA Working Papers 202303, University of Barcelona, Research Institute of Applied Economics, revised Feb 2023.
  14. Eleftherios Giovanis, 2010. "Application of logit model and self‐organizing maps (SOMs) for the prediction of financial crisis periods in US economy," Journal of Financial Economic Policy, Emerald Group Publishing Limited, vol. 2(2), pages 98-125, June.
  15. Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Self-organizing map analysis of agents' expectations. Different patterns of anticipation of the 2008 financial crisis”," IREA Working Papers 201511, University of Barcelona, Research Institute of Applied Economics, revised Mar 2015.
  16. Elgin, Ceyhun & Uras, Burak R., 2013. "Public debt, sovereign default risk and shadow economy," Journal of Financial Stability, Elsevier, vol. 9(4), pages 628-640.
  17. Tölö, Eero, 2019. "Predicting systemic financial crises with recurrent neural networks," Research Discussion Papers 14/2019, Bank of Finland.
  18. Francesca Caselli & Matilde Faralli & Paolo Manasse & Ugo Panizza, 2021. "On the Benefits of Repaying," IMF Working Papers 2021/233, International Monetary Fund.
  19. repec:zbw:bofrdp:2019_014 is not listed on IDEAS
  20. Tamás Kristóf, 2021. "Sovereign Default Forecasting in the Era of the COVID-19 Crisis," JRFM, MDPI, vol. 14(10), pages 1-24, October.
  21. Mioara CHIRITA, 2012. "Usefulness of Artificial Neural Networks for Predicting Financial and Economic Crisis," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 61-66.
  22. Peter Sarlin & Dorina Marghescu, 2011. "Visual predictions of currency crises using self‐organizing maps," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(1), pages 15-38, January.
  23. Ayşe Özmen & Gerhard-Wilhelm Weber & Zehra Çavuşoğlu & Özlem Defterli, 2013. "The new robust conic GPLM method with an application to finance: prediction of credit default," Journal of Global Optimization, Springer, vol. 56(2), pages 233-249, June.
  24. Markus Holopainen & Peter Sarlin, 2015. "Toward robust early-warning models: A horse race, ensembles and model uncertainty," Papers 1501.04682, arXiv.org, revised Apr 2016.
  25. repec:zbw:bofitp:2011_018 is not listed on IDEAS
  26. Eleftherios Giovanis, 2012. "Study of Discrete Choice Models and Adaptive Neuro-Fuzzy Inference System in the Prediction of Economic Crisis Periods in USA," Economic Analysis and Policy, Elsevier, vol. 42(1), pages 79-96, March.
  27. Bandiera, Luca & Cuaresma, Jesus Crespo & Vincelette, Gallina A., 2010. "Unpleasant surprises : sovereign default determinants and prospects," Policy Research Working Paper Series 5401, The World Bank.
  28. Moreno Badia, Marialuz & Medas, Paulo & Gupta, Pranav & Xiang, Yuan, 2022. "Debt is not free," Journal of International Money and Finance, Elsevier, vol. 127(C).
  29. Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024. "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1431-1471, November.
  30. Barbara Jarmulska, 2022. "Random forest versus logit models: Which offers better early warning of fiscal stress?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 455-490, April.
  31. Lean Yu & Xinxie Li & Ling Tang & Zongyi Zhang & Gang Kou, 2015. "Social credit: a comprehensive literature review," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-18, December.
  32. Raffaele Marchi & Alessandro Moro, 2024. "Forecasting Fiscal Crises in Emerging Markets and Low-Income Countries with Machine Learning Models," Open Economies Review, Springer, vol. 35(1), pages 189-213, February.
  33. Kim Ristolainen, 2015. "Were the Scandinavian Banking Crises Predictable? A Neural Network Approach," Discussion Papers 99, Aboa Centre for Economics.
  34. Sarlin, Peter & Peltonen, Tuomas A., 2013. "Mapping the state of financial stability," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 26(C), pages 46-76.
  35. Peter Sarlin & Dorina Marghescu, 2011. "Neuro‐Genetic Predictions Of Currency Crises," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(4), pages 145-160, October.
  36. Carlos León & José Fernando Moreno & Jorge Cely, 2016. "Whose Balance Sheet is this? Neural Networks for Banks’ Pattern Recognition," Borradores de Economia 959, Banco de la Republica de Colombia.
  37. Fuat SEKMEN & Murat KURKCU, 2014. "An Early Warning System for Turkey: The Forecasting Of Economic Crisis by Using the Artificial Neural Networks," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 4(4), pages 529-543, April.
  38. Fu, Junhui & Zhou, Qingling & Liu, Yufang & Wu, Xiang, 2020. "Predicting stock market crises using daily stock market valuation and investor sentiment indicators," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
  39. Sarlin, Peter & Peltonen, Tuomas A., 2013. "Mapping the state of financial stability," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 26(C), pages 46-76.
  40. Sarlin, Peter & Peltonen, Tuomas A., 2011. "Mapping the state of financial stability," BOFIT Discussion Papers 18/2011, Bank of Finland Institute for Emerging Economies (BOFIT).
  41. Jian Min & Jiaojiao Zhu & Jian-Bo Yang, 2020. "The Risk Monitoring of the Financial Ecological Environment in Chinese Outward Foreign Direct Investment Based on a Complex Network," Sustainability, MDPI, vol. 12(22), pages 1-26, November.
  42. Lanbiao Liu & Chen Chen & Bo Wang, 2022. "Predicting financial crises with machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 871-910, August.
  43. Kim Ristolainen, 2018. "Predicting Banking Crises with Artificial Neural Networks: The Role of Nonlinearity and Heterogeneity," Scandinavian Journal of Economics, Wiley Blackwell, vol. 120(1), pages 31-62, January.
  44. Bitetto, Alessandro & Cerchiello, Paola & Mertzanis, Charilaos, 2023. "Measuring financial soundness around the world: A machine learning approach," International Review of Financial Analysis, Elsevier, vol. 85(C).
  45. Kinsella, Stephen, 2019. "Visualising economic crises using accounting models," Accounting, Organizations and Society, Elsevier, vol. 75(C), pages 1-16.
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