- Tytuł:
- The Use of Discriminant Analysis to Predict the Bankruptcy of Companies Listed on the NewConnect Market
- Autorzy:
- Mosionek-Schweda, Magdalena
- Powiązania:
- https://bibliotekanauki.pl/articles/517385.pdf
- Data publikacji:
- 2014
- Wydawca:
- Instytut Badań Gospodarczych
- Tematy:
-
discriminant models
Altman's model
NewConnect
bankruptcy - Opis:
- The aim of this article is to analyze and evaluate the usability of discriminant models in predicting bankruptcy for companies listed on NewConnect. This market was established in 2007 and operates as an alternative trading system next to Warsaw Stock Exchange S.A., which in practice means that its regulatory regime in relation to issuers and listed companies is not as strict as the one applicable to the main market, therefore shares of small and medium-size businesses, including start-ups, can be listed on NewConnect. In this paper, discriminant models are used to analyse the financial situation of four companies removed from trading on NewConnect due to bankruptcy, Perfect Line S.A., Promet S.A., InwazjaPC S.A. and Budostal-5 S.A. The analysis is based on three models: Altman's model for emerging markets, as well as two models of the highest predictive ability according to P. Antonowicz's research, Z7INEPAN model developed in the Polish Academy of Sciences and E. Mączyńska's model, developed by Polish scientists and adapted to the Polish economy. The results confirm that these models are a valuable tool in assessing the financial condition of enterprises and allow for bankruptcy forecasting. Their application to companies listed on NewConnect, however, may be limited due to the specific profile of these entities as most of these enterprises are in fact newly formed and therefore the existing empirical data may prove insufficient.
- Źródło:
-
Equilibrium. Quarterly Journal of Economics and Economic Policy; 2014, 9, 3; 87-105
1689-765X
2353-3293 - Pojawia się w:
- Equilibrium. Quarterly Journal of Economics and Economic Policy
- Dostawca treści:
- Biblioteka Nauki
Artykuł