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

Abstract

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 · X 1 + 1.588 · X 2 + 4.288 · X 3 + 6.719 · X 4 ) -2.368 where: X 1 -net income/total assets; X 2 -(current assets -stock)/current liabilities; X 3 -fixed capital/total assets X 4 -income from sales/sales revenue).

Forecasting bankruptcy in the wood industry

“…To interpret the effectiveness of predictions made applying FMWE, the results can and should be compared with MBM results. Adamowicz and Noga (2017) used the same methodology and the same source data as those used to assess FMWE prediction efficiency to provide forecasts and assess the efficiency of 10 most popular MBMs in Poland. The obtained results concerned the same period and the same population of companies in the T1 and T2 samples and those authors compared them with those of the presented study.…”

“…The average SP exceeded 84%. Adamowicz and Noga (2017), on the basis of the analyses concerning the efficiency of classifying wood industry enterprises to the FB or HF group (T1), found the average SP of the MBM predictions to be 74%. It seems that the average efficiency of the FMWE predictions was higher than that of the average MBM prediction.…”

“…In turn, EII for FMWE predictions amounted to 27%, 7% and 87%, respectively. Adamowicz and Noga (2017), when analyzing 10 most popular bankruptcy prediction models in Poland concluded that the highest SP was recorded for predictions for the model Pz = 3.562 × net income/total assets + 1.588 × (current assets − stocks)/current liabilities + 4.288 × fixed capital/total assets + 6.7198 × income from sales/sales revenue − 2.368. It should be emphasized here that the average SP in the model was 82% and was lower than SP for FMWE by 2%.…”

“…In the context of the discussed topic, this work, which reviewed the efficiency of 10 bankruptcy prediction models in wood industry in Poland is of particular importance. Adamowicz and Noga (2017) Prusak's (2) and (x) Wierzba's models. These prediction models were subjected to the efficiency assessment test, i.e.…”

Identification of financial ratios applicable in the construction of a prediction model for bankruptcy of wood industry enterprises

“…In essence, the early warning system is a financial analysis technique, which identifies the achievement analysis of an enterprise in view of its industry with the help of financial ratios (Koyuncugil and Ozgulbas 2012). Most papers published to date on the construction of EWS models are based on an adequate selection of financial ratios (e.g., Adamowicz and Noga 2017;Abumustafa 2006;Altman et al 1968;Altman et al 1977;Barniv and Hathorn 1997;Barniv and McDonald 1992;Beaver 1966;Berg et al 2004;Bredart 2014;Brockett and Cooper 1990;Brockett et al 2006;Chen 2014;Davis and Karim 2008;Deakin 1972;Edison 2003;El-Shazly 2003;Katz 2006;Koyuncugil and Ozgulbas 2007, 2008, 2009a, 2009bKyong et al 2006;Laitinen and Chong 1999;Lee and Urrutia 1996;Lestano et al 2004;Lin et al 2010Lin et al , 2012Pantalone and Platt 1987;Salas and Saurina 2002;Taffler and Tisshaw 1977;Trieschmann and Pinches 1973;Wu 2007;Yang et al 2001;Zavgren 1985;Zmijewski 1984). In general, financial ratios (FRs), recognized as one of the most important factors affecting bankruptcy prediction, are used to develop prediction models (Altman 1968;Beaver 1966;…”