PREDICTING CORPORATE FINANCIAL CRISIS USING SOM-BASED NEUROFUZZY MODEL

Being aware of the risk in advance necessitates intricate processes but is feasible. Although previous studies have demonstrated high accuracy, their performance still leaves room for improvement. A self-organizing feature map (SOM) based neurofuzzy model is developed in this study to provide another alternative for forecasting corporate financial distress. The model is designed to yield high prediction accuracy, as well as reference rules for evaluating corporate financial status. As a database, the study collects all financial reports from listed construction companies during the latest decade, resulting in over 1000 effective samples. The proportion of "failed" and "non-failed" companies is approximately 1:2. Each financial report is comprised of 25 ratios which are set as the input variable s. The proposed model integrates the concepts of pattern classification, fuzzy modeling and SOM-based optimization to predict corporate financial distress. The results exhibit a high accuracy rate at 85.1%. This model outperforms previous tools. A total of 97 rules are extracted from the proposed model which can be also used as reference for construction practitioners. Users may easily identify their corporate financial status by using these rules.