은행의 이익구조와 이익예측모형에 관한 연구
A Study on the Banks’ Profit Structure and a Profit Prediction Model
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은행의 이익에 대한 예측 가능성을 높이기 위해서는 이익지표의 안정성과 이익지표와 독립변수 간의 유의성이 높아야 한다. 또한 독립변수가 은행의 전략에 따라 통제 가능한 지표이어야 할 것이다. 본 연구에서는 수익성 결정요인에 관한 선행 연구에서 주로 이용한 NIM 및 ROA뿐만 아니라 구조적 이익을 종속변수로 하고 은행별 특성변수만을 독립변수에 포함하여 국내은행의 2002∼2011년 자료를 대상으로 이익모형을 추정하였다. 특히 신용리스크 및 시장리스크를 부담하는 대가로 마진을 창출하는 금융중개서비스의 속성을 감안하여 리스크부담 지표는 선행 연구에서 주로 사용한 손실지표 대신 리스크 본래의 사전적인 의미를 지닌 위험가중자산을 사용하였다. 구조적 이익과 독립변수의 관계를 보면 전기의 위험가중자산비율은 유의하게 양의 방향으로 구조적 이익에 영향을 미치는 것으로 나타나 장기적인 수익기반 확대를 위한 적극적인 리스크 감수 경영의 필요성을 확인할 수 있었다. 시장점유율 확대는 은행시장의 높은 경쟁도를 반영하여 단기적으로는 구조적 이익 증대에 부정적인 것으로 나타났으며 경영효율성 제고는 일반 기업과 마찬가지로 이익률을 높이는 요인으로 나타났다. 비용측면에서는 자기자본과 저금리 예금인 수시입출식예금에 대한 의존도가 확대될수록 높은 이익률을 실현한다는 결과를 도출하였다. 본 연구에서 제시한 이익예측모형에서는 포괄범위가 넓고 안정적인 이익지표와 함께 사후적인 손실지표 대신 사전적인 리스크 지표를 사용하였다는 점에서 은행의 이익 예측이나 관리에서 높은 실용성을 가지고 있는 것으로 판단된다. 다만 어느 정도까지 리스크를 감수할 경우 대손충당금을 감안한 당기순이익에도 긍정적인지 밝혀내지 못한 점을 본 연구의 한계로 본다.
Abstract
To enhance the predictability of banks’ profit, stability of profit indicators and the significance between profit indicators and explanatory variables should be high. This paper first looks at which profit measure is superior for the dependent variable in a profit prediction model. Three types of profit indicators, the NIM and the ROA mainly used in the previous studies, and structural profits, are employed. And, it also tests the relationship between individual bank data and profits. In estimating profit model, only individual bank data are included as explanatory variables as those should be indicators which can be controlled for in accordance with bank strategy. In particular, given the nature of financial intermediation, which generates profits in return for accepting credit and market risks, this study uses risk-weighted assets which have the meaning of ex ante original risk as the indicator of bank’s risk burden rather than a loss indicator used in most studies hitherto. Korea's banking system had been under substantial restructuring since the foreign exchange and banking crises in 1998. Therefore banking business right after the crises had been far from normal for the couple of years. The sample includes 17 banks operating over the period 2002 to 2011 and the dataset is a unbalanced panel one since one of the sample banks was merged in 2006. In regard to the relationship between structural profits and the explanatory variables, previous year’s risks influence structural profits in a significantly positive direction, confirming the need for aggressive risk-taking management to expand the long-term revenue base. Most previous research found the same results, but the data used in the previous research were ex post indicators. The expansion of market share has a negative impact on the increase of structural profits in the short-term due to the intense competition in the banking sector and low level of differentiation in financial products between banks. The enhancement of management efficiency contributes to improving profitability as in ordinary corporations. On the cost side, where there is a high degree of reliance on equity capital and cheap funding from demand deposits, this results in the realization of a high rate of return. Among three different profit indicators, the NIM and structural profits show very stable movement whereas the ROA fluctuated widely from year to year because of volatile loan loss provisioning. Empirical results suggest that structural profit is more appropriate rather than the NIM and the ROA to a profit prediction model. The paper also identifies that risk-weighted assets could be used in a profit model since it is controllable at the time of portfolio decision and is used in the calculation of BIS capital ratio as a denominator. The profit prediction model of this study is believed to be practical for banks’ profit forecasting and management as it uses a stable profit indicator and an ex ante risk indicator rather than ex post loss indicators. This study however does not identify to what extent taking on risk is beneficial for the net income considering loan loss provisioning. We see this as representing a limitation of the study. Even though structural profits increase as a result of higher burden of risk-taking, net income after loan loss provisioning possibly declines as the non-performing loan accumulates. The study would enhance the usefulness of the profit prediction model, if it analyzes a critical value of risk-taking which is able to decrease net income.
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