Detection of Economic Crises With Language Models and Comparative Analysis of Simple Time Series Analysis Models and Machine Learning Algorithms on the Stock Market

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dc.contributor.authorKotan, Kurban
dc.contributor.authorKotan, Bayram
dc.contributor.authorKirisoglu, Serdar
dc.date.accessioned2025-10-11T20:48:15Z
dc.date.available2025-10-11T20:48:15Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractThis study investigates the use of natural language processing language representation models as an early warning system for economic crises, and compares the performance of time series analysis and machine learning models in financial markets before and during the economic crises in order to select the best model. The data used in the research was collected based on the economic crises that occurred in Turkey in December 2021. The aim is to identify an economic crises period by using language representation models for economic news between August 2021 and January 2022. After identifying the economic crises period, short term (1 day), medium term (15 days) and long term (30 days) forecasts were made for the index of thirty companies with the highest trading volume (BIST30) of Borsa Istanbul between 01/01/2021 and 31/12/2021 and performance comparisons were made between the models. The aim is to develop an effective smart, automatic crises detection and forecasting model selection application. The CHIT algorithm introduced in the study is a new missing data filling algorithm used in time series forecasting comparisons. Since the CHIT algorithm has a high impact on the model performance, this algorithm is used in the pre-processing step and comparisons are made.en_US
dc.identifier.doi10.1109/ACCESS.2025.3567743
dc.identifier.endpage83273en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-105004699542en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage83254en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2025.3567743
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21828
dc.identifier.volume13en_US
dc.identifier.wosWOS:001489666400025en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIEEE Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectArtificial intelligenceen_US
dc.subjecttime seriesen_US
dc.subjectmachine learningen_US
dc.subjectimputationen_US
dc.subjectnatural language processingen_US
dc.subjectArtificial intelligenceen_US
dc.subjecttime seriesen_US
dc.subjectmachine learningen_US
dc.subjectimputationen_US
dc.subjectnatural language processingen_US
dc.titleDetection of Economic Crises With Language Models and Comparative Analysis of Simple Time Series Analysis Models and Machine Learning Algorithms on the Stock Marketen_US
dc.typeArticleen_US

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