SME Viability Assessment Methodology: Combining Altman's Z-Score with Big Data

Dimitar Haralampiev Popov (dhpopov@gmail.com)
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Dimitar Haralampiev Popov: University of National and World Economy, Department of Management, Sofia, Bulgaria

Bulgarian Economic Papers from Faculty of Economics and Business Administration, Sofia University St Kliment Ohridski - Bulgaria // Center for Economic Theories and Policies at Sofia University St Kliment Ohridski

Abstract: Due to their important place in an economy, small and medium enterprises (SMEs) viability is the focus of numerous scientific studies, European and national programs. One of the most widely used viability prediction model is Altman's Z-score. Altman's classical models are not suitable for all situations, though. SMEs' large nominal number in an economy presents another challenge to researchers. One possible solution to this issue is to use data mining tools that can lead to new knowledge discovery. Data mining is the result of a natural evolution of information technology. The cross industry standard process for data mining (CRISP-DM) is a methodological framework for researching large amounts of data. This paper aims to outline the characteristics of Altman's Z-score and CRISP-DM, and propose combining them into a methodology for predicting SMEs' viability.

Keywords: Altman Z-score; Data mining; CRISP-DM; SMEs; Bulgaria (search for similar items in EconPapers)
JEL-codes: C38 C55 M10 P12 (search for similar items in EconPapers)
Pages: 15 pages
Date: 2022-06, Revised 2022-06
New Economics Papers: this item is included in nep-ent, nep-for and nep-sbm
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