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Exploring Machine Learning Techniques for Early Detection of Macroeconomic Crisis

2026-01-02Documento de conferência Acesso embargado
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dc.contributor.authorDiachkov, Dmytro
dc.contributor.authorAshofteh, Afshin
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.coverage.spatialCham, Switzerland
dc.date.accessioned2026-01-14T19:47:42Z
dc.date.available2026-01-14T19:47:42Z
dc.date.embargoedUntil2027-01-02
dc.date.issued2026-01-02
dc.descriptionDiachkov, D., & Ashofteh, A. (2026). Exploring Machine Learning Techniques for Early Detection of Macroeconomic Crisis. In Á. Rocha, F. García Peñalvo, C. J. Costa, & R. Gonçalves (Eds.), Proceedings of 20th Iberian Conference on Information Systems and Technologies (CISTI 2025) (Vol. 2, pp. 764-775). (Lecture Notes in Networks and Systems; Vol. 1717). Springer. https://doi.org/10.1007/978-3-032-10721-3_65 --- This research was supported by Portuguese national science funds made available through the FCT under project UIDB/04152/2020-Centro de Investigação em Gestão de Informação (MagIC).
dc.description.abstractThe early detection of macroeconomic crises has become a critical area of focus in financial and economic research, particularly due to the increasing complexity and interconnectedness of global markets. Traditional econometric models which mostly rely on prespecified relationships and assumptions, while foundational, often struggle to capture the rapid shifts and nonlinear dynamics characteristic of modern financial systems, leading to delayed or less accurate crisis predictions. This systematic review explores the potential of machine learning (ML) as a robust alternative for early warning systems (EWS) capable of addressing these limitations. ML models such as neural networks, support vector machines, and ensemble models show promising improvements in predictive accuracy, adaptability, and scalability, effectively handling vast and diverse datasets. These capabilities enable ML-based models to identify early indicators of systemic risk across global financial networks, supporting proactive responses to emerging crises. However, challenges persist, particularly around the interpretability and consistency of ML models, which can affect stakeholder trust and complicate applications in regulatory and policy settings. Further research into integrating interpretability and addressing data quality concerns could enhance the role of ML in creating reliable, policy-ready early warning frameworks. The findings underscore ML’s potential to supplement or transform traditional approaches to economic stability, providing tools to support more timely and accurate financial risk assessment and crisis mitigation strategies.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent12
dc.format.extent433392
dc.identifier.doi10.1007/978-3-032-10721-3_65
dc.identifier.isbn978-3-032-10720-6
dc.identifier.isbn978-3-032-10721-3
dc.identifier.issn2367-3370
dc.identifier.otherPURE: 148961029
dc.identifier.otherPURE UUID: a824aec0-22d8-469c-9960-d01a9895a30c
dc.identifier.othercrossref: 10.1007/978-3-032-10721-3_65
dc.identifier.otherScopus: 105027164647
dc.identifier.otherWOS: 001737866200065
dc.identifier.urihttp://hdl.handle.net/10362/199175
dc.identifier.urlhttps://www.scopus.com/pages/publications/105027164647
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001737866200065
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer
dc.relationhttps://doi.org/10.54499/UID/04152/2025
dc.relationhttps://doi.org/10.54499/UID/PRR/04152/2025
dc.subjectMachine Learning
dc.subjectEarly Warning Systems
dc.subjectMacroeconomic Crisis
dc.subjectFinancial Stability
dc.subjectPredictive Modeling
dc.subjectControl and Systems Engineering
dc.subjectSignal Processing
dc.subjectComputer Networks and Communications
dc.titleExploring Machine Learning Techniques for Early Detection of Macroeconomic Crisisen
dc.typeconference object
degois.publication.firstPage764
degois.publication.lastPage775
degois.publication.titleProceedings of 20th Iberian Conference on Information Systems and Technologies (CISTI 2025)
degois.publication.title20th Iberian Conference on Information Systems and Technologies
degois.publication.volume2
dspace.entity.typePublication
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