Abstract
In the face of rising defaults and limited studies on the prediction of financial distress in Morocco, this article aims to determine the most relevant predictors of financial distress and identify its optimal prediction models in a normal Moroccan economic context over two years. To achieve these objectives, logistic regression and neural networks are used based on financial ratios selected by lasso and stepwise techniques. Our empirical results highlight the significant role of predictors, namely interest to sales and return on assets in predicting financial distress. The results show that logistic regression models obtained by stepwise selection outperform the other models with an overall accuracy of 93.33% two years before financial distress and 95.00% one year prior to financial distress. Results also show that our models classify distressed SMEs better than healthy SMEs with type I errors lower than type II errors.
Highlights
Work on financial distress is a topical issue that has attracted the attention of researchers for several decades
The findings reveal that the lasso technique performs better with neural networks than logistic regression
The results showed that neural networks and logistic regression outperform other techniques in terms of efficiency and accuracy in an open European economic zone
Summary
Work on financial distress is a topical issue that has attracted the attention of researchers for several decades. Financial distress occurs when a company’s current assets can no longer meet its current liabilities (Malécot 1981). The process of financial distress is continuous and dynamic, lasting from a few months to several years, and can lead to bankruptcy (Sun et al 2014). Financial distress can have devastating effects on the company itself and all of its stakeholders (Hafiz et al 2015). Financial distress prediction studies help companies detect financial difficulties earlier, understand the process of financial distress, and prevent the occurrence of bankruptcy (Crutzen and Van Caillie 2007). Since the Z-score model proposed by Altman (1968), a great deal of research has focused on the prediction of corporate financial distress using different prediction models
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