Financial distress prediction: A logistic regression analysis on publicly listed industrial firms in the Philippines

• Main Authors: Avila, Mari Luis Augustin T., Ngo, Louise Kyle V., Tan, Dave Johann Y.
• Format: text
• Language: English
• Published: Animo Repository 2021
• Subjects: Bankruptcy > Philippines; Business failures > Philippines; Economics; Finance
• Online Access: https://animorepository.dlsu.edu.ph/etdb_econ/12; https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1010&context=etdb_econ
• Institution: De La Salle University

When a firm is unable to meet its financial obligations, it falls under the vulnerable state of financial distress. If left unaddressed, this may lead to the eventual bankruptcy of the firm. Thus, it is of great significance if investors and creditors can predict this state in order for them to prevent losses. This paper analyzes the significance, predictive accuracy, and the marginal effects of accounting, market, and macroeconomic variables in predicting financial distress using a logistic regression analysis for an unbalanced panel dataset consisting of 1,226 company-year observations of publicly listed industrial firms in the Philippines. We build a model using data from the firm’s financial statements, PSE monthly reports, and the Bangko Sentral ng Pilipinas. Our empirical results show that among all the variables, liquidity is the most significant and has the greatest impact in determining the probability of financial distress. Furthermore, we find that the consolidated model, which contains all the types of variables, yields the best fitting and most accurate model in predicting financial distress when compared to the nested models.