Abstract: The Indian banking industry is going through a period of intense change, where liberalized business environment has affected the banking business by way of increasing competition, rising customer expectations, shrinking spreads and increasing disintermediation. Ongoing changes in the structure of Indian banking are clearly visible. This paper investigates the levels and determinants of efficiency of schedule commercial banks of this vital sector of the Indian economy by using firm-level data. For this purpose, a two stage data envelopment analysis has been used. In the first stage, super technical efficiency analysis of 89 sample firms has been undertaken. This study specifies two outputs: total loans and other earning assets and three inputs: labour, fixed capital and total customers and short term funding and the prices are personnel expenses to average number of personnel for labour, total capital expenses to total fixed assets for fixed capital and interest expenses to average total customers and short term funding for the years 1980–81 to 2012–13. In the second stage, the efficiency scores obtained from the first stage are regressed on external environmental factors like fiscal deficits, private investment and the share of foreign banks using a censored regression model, viz. Tobit model. In this context, the term environment is used to describe factors that could influence the efficiency of a firm, where such factors are not traditional inputs and are not under the control of management (17). The results confirm that the varying market condition and the presence of foreign banks will contribute positively to economic growth.Abstract: The Indian banking industry is going through a period of intense change, where liberalized business environment has affected the banking business by way of increasing competition, rising customer expectations, shrinking spreads and increasing disintermediation. Ongoing changes in the structure of Indian banking are clearly visible. This paper inves...Show More
Abstract: Because of the nature of the financial and economic activities and they are practically accompanied with a degree of risk., banks are usually dealing with many risks, including operational, marketing, interest rate, etc. Since, credit risk has significant effects on financial banks activities in terms of loaning profits, the risk of repayment individual loans has been investigated in this research work. Two well-known regression models of Probit and Logistic have been developed based on nine extracted factors which have been investigated during the offering of loans according to the possibility of late or non-repayment. In order to minimize inter-correlation and extracting high-independency factors, the statistical technique of Principal Component Analysis (PCA), categorized as a data reduction technique, has been utilized and three factors out of nine have been omitted. One of Tejarat bank branches in the Iranian Northern Province of Guilan has been selected as case study to gather experimental data for assessing the credit risk of individual bank investors. The results of model validation revealed that the implementation of PCA method can improve the accuracy of models’ outputs and Probit regression model has better results rather than Logit one.Abstract: Because of the nature of the financial and economic activities and they are practically accompanied with a degree of risk., banks are usually dealing with many risks, including operational, marketing, interest rate, etc. Since, credit risk has significant effects on financial banks activities in terms of loaning profits, the risk of repayment indiv...Show More