Oliver Lukason

This study aims to detect failure processes on the example of old bankrupted European manufacturing firms. Two study designs are applied, namely the original six variables from Laitinen’s (1991) model and an extended dataset with eleven variables for a five-year timespan before declared bankruptcy. On both occasions, two different failure processes are detected which indicate elements of either quickly or gradually failing firms. Clear contingencies between detected processes and firms’ countries of origin exist. There is some evidence that firms of different sizes follow varying failure processes, but this does not apply when discriminating between exporters and non-exporters

This study aims to create a prediction model that would forecast the bankruptcy of government funded start-up firms (GFSUs). Also, the financial development patterns of GFSUs are outlined. The dataset consists of 417 Estonian GFSUs, of which 75 have bankrupted before becoming five years old and 312 have survived for five years. Six financial ratios have been calculated for one (t+1) and two (t+2) years after firms have become active. Weighted logistic regression analysis is applied to create the bankruptcy prediction models and consecutive factor and cluster analyses are applied to outline the financial patterns. Bankruptcy prediction models obtain average classification accuracies, namely 63.8% for t+1 and 67.8% for t+2. The bankrupt firms are distinguished with a higher accuracy than the survived firms, with liquidity and equity ratios being the useful predictors of bankruptcy. Five financial patterns are detected for GFSUs, but bankrupt GFSUs do not follow any distinct patterns that would be characteristic only to them.