Articlegold2017

Predicting financial distress of agriculture companies in EU

45 citations0/0 files storedAgricultural Economics (Zemědělská ekonomika)

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Financial Distress and Bankruptcy Prediction Corporate Insolvency and Governance Forecasting Techniques and Applications

Abstract

The objective of this paper is the prediction of financial distress (default of payment or insolvency) of 250 agriculture business companies in the EU from which 62 companies defaulted in 2014 with respect to lag of the used attributes. From many types of classification models, there was chosen the Logistic regression, the Support vector machines method with the RBF ANOVA kernel, the Decision Trees and the Adaptive Boosting based on the decision trees to acquire the best results. From the results, it is obvious that with the increasing distance to the bankruptcy, there decreases the average accuracy of the financial distress prediction and there is a greater difference between the active and distressed companies in terms of liquidity, rentability and debt ratios. The Decision trees and Adaptive Boosting offer a better accuracy for the distress prediction than the SVM and logit methods, what is comparable to the previous studies. From the total of 15 accounting variables, there were constructed classification trees by the Decision Trees with the inner feature selection method for the better visualization, what reduces the full data set only to 1 or 2 attributes: ROA and Long-term Debt to Total Assets Ratio in 2011, ROA and Current Ratio in 2012, ROA in 2013 for the discrimination of the distressed companies.

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References

1980Financial Ratios and the Probabilistic Prediction of Bankruptcy 1966Financial Ratios As Predictors of Failure 1984Methodological Issues Related to the Estimation of Financial Distress Prediction Models 2005Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters 200535 years of studies on business failure: an overview of the classic statistical methodologies and their related problems 1985Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress 2006Predicting corporate bankruptcy: where we stand?2005Hybrid genetic algorithms and support vector machines for bankruptcy prediction

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