Failure prediction of government funded start-up firms

  • Received April 21, 2017;
    Accepted June 14, 2017;
    Published August 7, 2017
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  • Article Info
    Volume 14 2017, Issue #2 (cont. 2), pp. 296-306
  • Cited by
    7 articles

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

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.

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    • Table 1. Formulae of financial ratios applied in the analysis
    • Table 2. Descriptive statistics of financial ratios for the whole sample and two types of firms, %
    • Table 3. Results of weighted logistic regression (LR) analysis for periods t+1 and t+2
    • Table 4. Classification accuracies of logistic regression models for periods t+1 and t+2
    • Table 5. Median values of financial ratios through five detected patterns, %
    • Table 6. Contingency between detected patterns and firm statuses
    • Table 7. Results of hypotheses testing
    • Table 8. Rotated factor matrix