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

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.

A K-Means Clustering Algorithm for Early Warning of Financial Risks in Agricultural Industry

“…Literature [16] and literature [17] are relatively typical models with feature analysis and financial early warning function of agricultural industry. In order to explore the comparative results of this model with literature [16] and [17] model, data enrichment was performed on the second training data set above to obtain the third training data set. Proposed model, literature [16] model, and literature [17] model were successively trained to obtain the comparison figure (see Figure 6).…”

“…In order to explore the comparative results of this model with literature [16] and [17] model, data enrichment was performed on the second training data set above to obtain the third training data set. Proposed model, literature [16] model, and literature [17] model were successively trained to obtain the comparison figure (see Figure 6).…”

“…Literature [17] takes the second place, and it takes about 1,000 training times for the value of the objective function to stabilize. Literature [16] is at least 1,000 times. In terms of objective function value, when the training of each model is completed, the objective function value of the model in this study is the smallest, with an average value of 0.055.…”

“…In terms of objective function value, when the training of each model is completed, the objective function value of the model in this study is the smallest, with an average value of 0.055. In literature [17], the mean value of objective function is 0.081 and in literature [16] it is 0.067. e model in this study has the best adaptability to the third training data set and the highest early warning accuracy.…”

The Effect of Good Corporate Governance Mechanisms, Financial Ratio, and Financial Distress: A Study on Financial Companies

“…The higher the leverage ratio, the higher the risk of the company experiencing financial distress; this is in line with the signal theory, which reveals that the high level of leverage owned by the company is a negative signal for stakeholders because the level of debt high, the company has an increased risk of experiencing financial distress. Profitability [27]. Profitability describes the company's ability to earn profits from the company's activities.…”