Open Access

Assessment applicability of selected models of multiple discriminant analyses to forecast financial situation of Polish wood sector enterprises


Cite

In the last three decades forecasting bankruptcy of enterprises has been an important and difficult problem, used as an impulse for many research projects (Ribeiro et al. 2012). At present many methods of bankruptcy prediction are available. In view of the specific character of economic activity in individual sectors, specialised methods adapted to a given branch of industry are being used increasingly often. For this reason an important scientific problem is related with the indication of an appropriate model or group of models to prepare forecasts for a given branch of industry. Thus research has been conducted to select an appropriate model of Multiple Discriminant Analysis (MDA), best adapted to forecasting changes in the wood industry. This study analyses 10 prediction models popular in Poland. Effectiveness of the model proposed by Jagiełło, developed for all industrial enterprises, may be labelled accidental. That model is not adapted to predict financial changes in wood sector companies in Poland.

The generally known Altman model showed the greatest effectiveness in the identification of enterprises at risk of bankruptcy. However, that model was burdened with one of the greatest errors in the classification of healthy enterprises as sick. The best effectiveness in the identification of enterprises not threatened with bankruptcy was found for forecasts prepared using the Prusak 2 model. However, forecasts based on those models were characterised by erroneous classification of sick companies as healthy. The model best fit to predict the financial situation of Polish wood sector companies was the Poznań model

Pz = 3.562 · X1 + 1.588 · X2 + 4.288 · X3 + 6.719 · X4 - 2.368

where:

X1 - net income / total assets;

X2 - (current assets - stock) / current liabilities;

X3 - fixed capital / total assets

X4 - income from sales / sales revenue).

eISSN:
2199-5907
ISSN:
0071-6677
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Life Sciences, Plant Science, Medicine, Veterinary Medicine