An Integrated LSTM Neural Networks Approach to Sustainable Balanced Scorecard-Based Early Warning System

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dc.authoridKUNCAN, Melih/0000-0002-9749-0418
dc.authoridKaplan, Kaplan/0000-0001-8036-1145
dc.contributor.authorAyvaz, Ednan
dc.contributor.authorKaplan, Kaplan
dc.contributor.authorKuncan, Melih
dc.date.accessioned2024-12-24T19:28:32Z
dc.date.available2024-12-24T19:28:32Z
dc.date.issued2020
dc.departmentSiirt Üniversitesi
dc.description.abstractDevelopments in the economic environment in the 2000s have become increasingly dynamic and complex. Rapid developments in this kind of economic environment threaten and restrain the sustainability of enterprises. Enterprises need to respond quickly to these burdens and threats to survive and sustain their operations efficaciously in a competitive market in the long run. In order to reduce possible uncertainties in the future and to anticipate economic crises, early risk warning systems should be developed. However, it is seen that management accounting researches are very limited or insufficient on the demand of enterprises for coping with such crises. The aim of this study is to diminish the deficiency in the strategic cost management and prediction of economic crises. Sustainable Balanced Scorecard (SBSC), which was developed as a strategic cost management tool, is constructed in a dynamic way by integrating the early warning system developed for enterprises with an innovative approach into SBSC. Additionally, early warning system model is developed in a manner that successfully predicts economic crises with long short time memory (LSTM) networks using economic macro variables in micro field. As a result of the integration of risk early warning system with SBSC, economic crises will be predicted and necessary strategies will be developed to cope with problems of the crises. Furthermore, predicting economic crises will be turned into opportunities or cause enterprises to make measures with minimum losses. In this model, crisis periods are successfully predicted two crises of 2002 and 2008 with 95.41& x0025; accuracy with macroeconomic data between 1998 and 2011.
dc.identifier.doi10.1109/ACCESS.2020.2973514
dc.identifier.endpage37966
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85081678625
dc.identifier.scopusqualityQ1
dc.identifier.startpage37958
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2020.2973514
dc.identifier.urihttps://hdl.handle.net/20.500.12604/7108
dc.identifier.volume8
dc.identifier.wosWOS:000525545900039
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIEEE-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectSustainable balanced scorecard (SBSC)
dc.subjectearly warning system
dc.subjecteconomic crises
dc.subjectmachine learning
dc.subjectlong short time memory (LSTM)
dc.titleAn Integrated LSTM Neural Networks Approach to Sustainable Balanced Scorecard-Based Early Warning System
dc.typeArticle

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