ระบบสัญญาณเตือนภัยล่วงหน้าทางการเงิน : การเปรียบเทียบระหว่างวิธีการถดถอยโลจิสติกส์ และโครงข่ายประสาทเทียม - กัมพล กมลรัตน์ธาดา (จุฬาลงกรณ์มหาวิทยาลัย, 2010)
U.S. Economic Crisis in 2008 has been marked by a failure in most business sectors. One of the industries substantially affected from this economic downturn is a banking business. From this fact, the purpose of this research wants to investigate the financial ratio factors that are able to forecast the insolvency of a banking business. The result could be useful to the government and private sectors for an auditing financial viability and performance of a banking business. Therefore, the importance of this research is the study of the important factors that indicate and urge in advance for the sign of financial failure.This research uses 21 financial ratios to set up a financial early warning system by using logistic regression approach and neural networks to classify the group of financial viability bank versus the group of financial insolvency bank. This research also compares the accuracy and validity of a financial early warning model. The result shows that the financial early warning model using neural networks to forecast bank insolvency is more accurate and valid than a predicting model using logistic regression approach. In conclusion, this research is useful and should be applicable to use as a financial early warning model to further indicate the insolvency of a banking industry.