Financial ratios as a measure of bank failure probabilities: A study on the rural banks in the Philippines using the logistic regression from the period 2003-2006

• Main Authors: Gamalinda, Jeffry Thomas, Macatangay, Glenn John T., Pendon, Ronald Angelo
• Format: text
• Language: English
• Published: Animo Repository 2009
• Subjects: Banks and banking > Philippines; Bank failures > Philippines
• Online Access: https://animorepository.dlsu.edu.ph/etd_bachelors/17484
• Institution: De La Salle University

Considerable amounts of study have been devoted to bankruptcy prediction which includes the studies of Beaver, Altman, and other researchers in the field of accounting and finance. It has been a great challenge for those who are engaged in the said study to determine the cause of bankruptcy and ways on how to prevent it. Equally important, the accuracy of the statistical models that will be used is also taken into account on the challenge imposed. In the study, the researchers examined the following: (1) is Logistic Regression accurate in predicting rural bank failures in the Philippines, (2) which among the ratios has the largest weight in determining the probability of bankruptcy. The paper used financial statements of closed and non-closed rural banks in the Philippines which covered from the period 2003-2006 in examining the problem of the research. Several models were used in predicting bankruptcy of companies using financial statement ratios. Most researchers used the LOGIT and the Multiple Discriminant Analysis or the Altman Z Score in determining bankruptcies. But only Logistic Regression was used by this paper ind650 determining bankruptcy. The researchers, using the statements-of-condition of rural banks and by means of the Logistic Regression, obtained the value that will be used in computing for the z-score of the failed banks. They found out that the following ratios affects the probability of bank failures in rural banks: 1) Dept to equity, 2) Liquid asset ratio, 3) Current ratio, 4) Total reserves to total assets, 5) Loans to deposits, 6) Loans to net worth. Furthermore, the researchers found out that the Logistic regression (Logit) was accurate in predicting rural bank failures since more than 50% of the total closed banks were predicted to be failed banks.