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Comparing ANN and Logistic Regression for Financial Distress Prediction by Lina lina is a document available to read on EtoBox.

This study compares the effectiveness of Artificial Neural Networks (ANN) and Logistic Regression (LR) in predicting financial distress using data from 12 Algerian companies between 2015 and 2019. The findings indicate that the Elman Neural Network (ENN) achieved a classification accuracy of 100%, outperforming the LR model, which had an accuracy of 83.33% against the Feed-forward Distributed Time Delay Neural Network (FFDTDNN). The research emphasizes that while some ANNs can be more effective than LR, thi

Author

Lina lina

Language

EN