Défaillance et politique d’entreprises : modélisation financière déployée sous un modèle logistique appliqué aux PME marocaines

Kherrazi, Soufiane; Ahsina, Khalifa

doi:10.1016/j.rgo.2016.03.006

Cited by 12 publications

(7 citation statements)

References 18 publications

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“…Indeed, the variables belonging to the profitability and solvency categories have already shown their ability to discriminate between sound and failing firms in several previous studies (Altman 1968;Amor et al 2009;Charalambakis and Garrett 2018;Gregova et al 2020;Jamel and Derbali 2019;Matoussi et al 1999;Mselmi et al 2017;Ohlson 1980). The same observation was made for the Moroccan context using the logistic regression- Kherrazi and Ahsina (2016) and Jamel and Derbali (2019) found on a sample of SMEs in the Gharb-Chrarda-Beni Hssen region that insufficient permanent funds and lack of commercial profitability help to explain the failure of SMEs in the region. Moreover, Nokairi (2018) confirms that negative profitability and lack of solvency are among the main characteristics of failing Moroccan companies.…”

Section: Discussionsupporting

confidence: 56%

“…Some authors suggest an empirical and/or selective choice of indicators. In this context, we have selected the accounting and financial indicators that have been the most tested in previous empirical studies (Ben Jabeur 2017; Crutzen and Van Caillie 2010;Kherrazi and Ahsina 2016;Kliestik et al 2018;Matoussi et al 1999;Mselmi et al 2017) and that have shown high predictive power and a strong capacity to discriminate between healthy and failing firms. However, our results will be influenced by the Moroccan context, which has its own economic, financial and managerial characteristics.…”

Section: Variable Analysismentioning

confidence: 99%

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Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach

Zizi

Oudgou

Moudden

2020

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This paper aims to identify the determinants and predictors of Small and Medium-sized Enterprises (SMEs)’ financial failure. Within this framework, we have opted for a quantitative method based on a sample of healthy and failing SMEs of a Moroccan bank. The main results of the different optimal models are obtained by the stepwise method of estimating logistic regression. These results show, in a normal economic context, that the variables that discriminate between healthy and failing SMEs are the main predictors of financial failure. Autonomy ratio, interest to sales, asset turnover, days in accounts receivable, and duration of trade payables are the variables that increase the probability of financial failure, while repayment capacity and return on assets reduce the probability of failure. These variables present an overall classification rate of healthy and failing SMEs of 91.11% three years before failure and of 84.44% two years and one year before failure.

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Section: Discussionsupporting

confidence: 56%

Section: Variable Analysismentioning

confidence: 99%

Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach

Zizi

Oudgou

Moudden

2020

Risks

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“…In Morocco, Kherrazi and Ahsina (2016) used a binomial logistic regression model to identify the determinants of SMEs failure in the Gharb-Chrarda-Beni-Hssen region. The results of the model showed that the failure of SMEs in the region is related to the lack of commercial profitability and the lack of permanent funds.…”

Section: Literature Reviewmentioning

confidence: 99%

An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression

Zizi

Alaoui

Goumi

et al. 2021

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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.

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“…However, it is distinguished by the approach adopted to apprehend the failure as well as the context studied. As such, it should be recalled that prediction of the failure of Moroccan companies listed on the Casablanca stock exchange is insufficiently researched since the majority of studies on financial difficulties relate to companies not registered on the Casablanca coast (Taouab, 2014;Kherrazi & Ahsina, 2016;El-Ansari & Benabdellah, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Failure and Value Creation: A Discriminant Analysis Applied to Moroccan Industrial Companies Listed on the Casablanca Stock Exchange

Derbali

2020

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This article purposes to assess the contribution of shareholder value creation as an explanatory variable for the failure of Moroccan industrial companies listed on the Casablanca stock exchange. It also offers a scoring that reflects the probabilities of the financial difficulties to occur. There were used data for 30 Moroccan industrial companies listed on the Casablanca stock exchange during the period of study from 2010 to 2018. Methodologically, a linear discriminant analysis was employed. The empirical results of the discriminant analysis applied during the period of 2010-2018 show that value creation, liquidity and the size of the company are the most determining variables in classifying companies according to the degree of financial difficulties. Thus, the results received make it possible to create a relationship among recurrent scientific knowledge about forecasting the companies’ failure and empirical findings in the context of Moroccan industrial companies listed on the Casablanca stock exchange.

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Défaillance et politique d’entreprises : modélisation financière déployée sous un modèle logistique appliqué aux PME marocaines

Cited by 12 publications

References 18 publications

Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach

Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach

An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression

Failure and Value Creation: A Discriminant Analysis Applied to Moroccan Industrial Companies Listed on the Casablanca Stock Exchange

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