Abstract— During the last decades and recession of 2007-2009 witnessed many global financial crises. Consequently, this research represents a proactive study via introducing new modeling tool; in order to diagnose the financial distress and assess its probability of occurrence. The Neuro-Logit is a new approach for diagnosis, prediction and forecasting corporate financial distress. This tool acts as Logit (Logistic Regression Analysis), but the equations are built based on the basic algorithm of ANN (Artificial Neural Network). ANN and Logit are widely used as modeling tools in many business applications; Neuro-Logit model reduces most of ANN and Logit limitations. The sample in this research has been drawn from the available financial statements (Financial Ratio-Based Model) that are belonged to most active non-financial firms in Egyptian stock markets. The observations are quarterly basis observations, covering six-year time period (2004-2009). The overall results show that Neuro-Logit model has superior outcome comparing to legacy Logit model, where the overall classification accuracy rate almost 86% with Type I Error 10.13%, 85.33% harmonic mean between Recall and Precision values and also good Kappa coefficient (0.7169) and Matthew Correlation Coefficient (0.7217). The paper revolves the diagnosing the financial health of the firms, and is an extension for the latest Egyptian model in 2007 which concerns with six-year span 2000-2005. The time span of the paper for model building is three-year (2005-2007) which is covering prior-recession time. The paper can be considered as second trial of supervised financial distress prediction model and the fourth Egyptian model with superior outcome supporting to be recommended in corporate financial failure assessment and diagnosis. Also the research is presenting empirically an innovative modeling approach, where the ANN is used as statistical tool.
Keywords— Corporate Financial Distress Diagnosis, Financial Analysis, Modeling Approach, Artificial Neural Networks and Logistic Regression Analysis