DETERMINANTS OF BANK CAPITAL STRUCTURE AND ADJUSTMENT SPEED: EVIDENCE FROM INDONESIAN BUBA3 AND BUBA4 BANKS (CASE STUDY: PERIOD 2008-2017)

• Main Author: Perdana Lawrentius (19015228), Randy
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/30212
• Institution: Institut Teknologi Bandung

Modigliani & Miller (1958) theorem proposed that value of the firm is independent from its decision on capital structure. However, in reality, there exist a set of non-ideal conditions. There are frictions in capital market, such as cost and benefit in capital structure preference. Indonesian Banking sector is no different. Whilst there is supervisory intervention in the minimum capital ratio required of 8% to hold, Indonesian Banks show consistent capital ratio measured by capital adequacy ratio 17.43% in average, above 8% from the first quarter 2008 until fourth quarter 2017 for BUBA3 and BUBA4 public listed banks. The tendency of Indonesian Banks held capital ratio above minimum implies such requirement from regulatory might not be binding and its capital structure decision may influenced by other factors aside regulatory supervision. Previous literatures suggest that banks may have their target capital structure. Like any other non-financial firms, profit-maximizing banks may exploit capital structure in friction world and gain benefit in tax shield and asymmetric information. Moreover, aside from gaining benefits in standard friction, banking sector has unique trait from the relative cheaper in holding more debt compared to other non-financial sectors. In harmony, benefits are responded by costs. Such circumstances motivate banks to adjust their capital ratio to the optimal over time based on several fundamental factors observed. <br /> <br /> This study examines the determinants of banks’ capital structure and the adjustment speed of capital adequacy ratio measured as excess of 8% (CARBUFF). Two distinct stages panel data regression analysis is conducted in this study using partial adjustment model to capture banks’ adjustment speed in capital structure. <br /> <br /> Result from this study show that trade-off theory is supported by risk proxy (LLP, LDR and ASSRISK) that significantly affect CARBUFF with positive relationship. However, one of the risk proxy (RWA) show negative relationship imply the existence of moral hazard behavior. Decreases in efficiency, (COIN) lead to decreases in CARBUFF. Profitability (PROF) has the highest coefficient and Size (LNSIZE) affect CARBUFF with positive relationship promote pecking order theory relevance. Earning proxy (ROE) has negative relationship with CARBUFF, with implication of signaling theory and serves as alternative cost of capital. Growth opportunity (MTB) has positive relationship promoting market timing theory. In the intercept analysis, BTPN has the highest capital ratio held while BBTN has the lowest capital ratio held. The reason behind is BTPN’s seek to increase their credit growth and prepare capital for its merge strategy with one of BUBA3 bank. Whereas, BBTN has mediocre profitability performance thus lack of internal source of funding. This study also proves that there is evidence that banks have optimal capital structure based on observed fundamental conditions and make adjustment toward their target capital ratio with speed of 25% per quarter. This speed is relative faster than other non-financial firms. <br /> <br /> This study recommends managers to mainly observe the risk proxy for their decision in capital structure as banks naturally possess high debt proportion. Profits generated may serve as internal source of funding. Nevertheless, managers can take advantage on growth opportunity associated with asymmetric information to obtain lower cost on external financing. Managers can also perform signaling theory by taking more debt in order to convey messages that the bank is well-profitable and has bright future prospects. Investors are also recommended to observe the risk and return behavior from this study result. Further research recommendation can include other variable factors like business cycle and another dynamic approach in estimating the model.