The Efficiency of Bankruptcy Forecast Models in the Hungarian SME Sector
Ékes Kristóf Szeverin, Koloszár László
bankruptcy forecast models, discriminant-analysis and logistical regression, neural networks
The paper examines the efficiency of bankruptcy forecast models in the Hungarian SME sector.
We also try to construct own models using discriminant-analysis, logistical regression’s, and neural
network methods, based on a random sample, what we try to validate on a second sample.
It has been proved that our own model can only be applied on the first sample with an outstanding
result. It has also been proved that complicated statistical solutions themselves are not always
applicable; there is a need for the expertise of an experienced economist.
The research, of course, does not say that the bankruptcy-forecast methods used in literature
have lost their trustworthiness completely. It just draws the attention to the fact that the economic
circumstances of Hungarian SMEs can’t be compared to that of big foreign companies.
Therefore, the results of the indexes developed to the large enterprise sector cannot help accurate
decision making in case of SMEs. Huge narrowing of the complexity of economic characteristics
may lead to false results.
The Efficiency of Bankruptcy Forecast Models in the Hungarian SME Sector [PDF file] [Filesize: 1.08 MB]
Ékes K. S., Koloszár L.(2014). The Efficiency of Bankruptcy Forecast Models in the Hungarian SME Sector. Journal of Competitiveness, 6 (2), 56-73 http://doi.org/10.7441/joc.2014.02.05