Philippine financial institutions' counterparty default risk and stock price relationships: An analysis

• Main Authors: Amano, Gerard Constantine, Gomez, Juwan Kenzie, Juan, Gilbert Angelo, Pioquinto, Jan Michael
• Format: text
• Published: Animo Repository 2022
• Subjects: Financial risk management—Philippines; Stocks—Prices—Philippines; Financial institutions—Philippines; Finance and Financial Management
• Online Access: https://animorepository.dlsu.edu.ph/etdb_finman/34; https://animorepository.dlsu.edu.ph/context/etdb_finman/article/1030/viewcontent/Philippine_Financial_Institutions_Counterparty_Default_Risk_and2.pdf
• Institution: De La Salle University

The objective of this study is to analyze the relationships and influence between default risk and the share prices of the selected Philippine Financial Institutions. The selected Philippine Financial Institutions are: Bank of the Philippine Islands, Banco de Oro Unibank, Metropolitan Bank and Trust Company, China Banking Corporation, and Philippine National Bank. Default risk is represented by the following default risk measures: Non-Performing Loans Ratio, Capital Adequacy Ratio, General Loan Loss Provision, and Allowance for Credit Losses. The researchers gathered quarterly data on the selected default risk measures and the stock prices of the selected Philippine Financial Institutions for the time period of 2015 to 2020. The study utilized a quantitative research design and conducted Pearson correlation tests and Multiple Linear regression tests to determine the relationship between default risk and share prices and the influence of default risk on the share prices of the selected Philippine Financial Institutions. The findings of the study show that two (2) out of the four (4) default risk measures exhibited significant relationships with the share prices which are Non-Performing Loans Ratio (NPL) and Allowance for Credit Losses (ACL). The panel regression analysis found both the NPL ratio and ACL have a significant influence on the 95% confidence level with the final model using the Feasible Generalized Least Squares model for panel data.