Impact of selected financial and economic variables on stock price of publicly listed banks in the Philippines from 2002-2008

• Main Author: Menaje, Placido M., Jr.
• Format: text
• Language: English
• Published: Animo Repository 2009
• Subjects: Stocks > Prices > Philippines > History
• Online Access: https://animorepository.dlsu.edu.ph/etd_masteral/3805
• Institution: De La Salle University

Stock investing is a serious matter and investors should seek those measures, financial or otherwise, that would lead them to sound business decisions. Past empirical studies made use of various financial and economic variables to determine their impact on stock price but some of the results were not very conclusive. The purpose of this paper was to determine whether diluted earnings per share (DEPS), cash flows per share (CFPS), cash dividend per share (CDPS), inflation rate (IR), and the 3- month Treasury bill rate (BSP) had positive significance on stock price of publicly listed banks in the Philippines. We used panel data that involved 10 publicly listed banks and covered a period of 7 years broken into 28 quarters. We tested various regression models under simple pooled model (naīve), fixed effects method using dummy variables, and random effects method to determine the best-fit model. Based on the statistical results, we concluded that the DEPS, CFPS, CDPS and IR had no significance on stock price. Only the Treasury bill interest rate (BSP) came out to be significantly related to stock price, but in a negative way. Finally, we recommended that those who intend to invest in stocks of banks, in analyzing market opportunities, should not limit themselves to using only the usual financial variables like the ones included in this study but should also include qualitative or unquantifiable factors that make a particular bank uniquely different from another bank. These unobserved internal factors could include management style, client perception, involvement in non-banking activities, etc. Future studies should take into account the lag effect of previous data by making stock return as the dependent variable rather than the stock price. Finally, to encourage research, trainings on new software like Gretl be vigorously pursued.