Modeling the NPA of a Large Indian Public Sector Bank as a Function of Total Assets

Abstract

Non-performing assets (NPA) are the loans given by a bank or a financial institution where in the borrower defaults or delays interest and / principal payment. The management of NPAs therefore, is a very important part of credit management of banks and financial institutions in the Country. Currently NPA estimates in India are predominantly obtained from figures published by the Reserve Bank of India (RBI). However it would be helpful for banks and financial institutions to have an estimate of the NPA as soon as loan amounts are disbursed. This study attempted to develop a predictive model for the NPA% at both the gross and net level from the total assets of one of India’s largest public banks. A strong correlation was observed between gross and net NPA% and the total assets suggesting that estimates of gross and net NPA can be made from total assets. Linear and non linear models were fit to predict the NPA% from the total assets. A non linear model linking both Gross and net NPA to total assets provided the best curve fit and the least deviation from actual values. Thus by simply looking at the banks total assets an overall picture of the banks NPA level can be ascertained.

Keywords: NPA Management, total assets, Indian Public Bank, Gross NPA, Net NPA, Linear Model, Non Linear Models.

1. Introduction

Industries and businesses are major drivers of the Indian national economy. Bank finance is an effective mechanism for strengthening industrial activity in the country, particularly when it involves industry segments that cover the small and medium scale enterprises (SME’s) not listed on the countries major stock exchanges (Mallick et al., 2010). However, when industries or businesses experience difficulties related to a weakening economic environment or business slowdown, and viability of the business is called into question industries may fail to meet their obligations towards interest and principal payments of the loans availed by them. Banks may then classify such accounts as distressed assets and eventually as non performing assets (NPAs). The management of NPAs therefore is a very important part of credit management of banks and financial institutions in the country. By looking at NPAs one can monitor the asset quality of the bank as a whole (Meeker and Laura, 1987).

The primary aim of any business is to make profits. Therefore any asset created in the course of conduct of the business should generate income for the business. This applies equally to the business of the banks. Banks, typically offset deposits by gaining higher margins through amounts advanced as loans. Interest payments if not made 180 days after they are due can be classified as NPAs (www.rbi.gov.in). Studies have shown that the terms of credit given to borrowers significantly impacts the amount of NPAs at the bank (Ranjan et al., 2003). If for any reasons such assets created do not generate any income or become difficult to recover, then the very position of the banks on repaying the deposits on the due date would be at stake and in jeopardy. Banks with such asset portfolio would become weak and naturally such weak banks will lose the faith and confidence of the investors. Ultimately unrecoverable amounts are written off as NPA’s, which are a direct function of amounts advanced as loans (Mallick et al., 2010).

The asset quality of the banks can be assessed by monitoring NPAs. When NPAs arise, banks have to make provisions for the same as per the regulatory prescriptions. Therefore it would be prudent for the banks to manage their assets in such a manner that they always remain healthy, generate sufficient income, and capable of meeting obligations on due dates. Currently banks and financial institutions and their investors rely on the Reserve Bank of India (RBI) to publish NPA data. This could potentially take a long time. It would be hence useful to arrive at estimates of NPA as a function of the total assets owned by the bank. This study attempts to assess the NPA level of one of the largest public banks in India as a direct function of the total assets of the bank. While gross NPAs are directly based on the amounts advanced, net NPA’s factor in provisioning allowed for loan losses (www.rbi.gov.in). The purpose of the study is to find out the nature and extent of the relationship between the NPA’s and the total assets of the bank. A relationship between the two can help us measure Gross and Net NPAs directly from total assets. Given the time lag involved in establishing a problem loan as an NPA it would be useful if an NPA estimate can be made as and when the total assets of the bank are known.

2. Literature Review

The accumulation of non-performing assets in banks has assumed great importance as it tends to reflect asset quality as a whole (Meeker and Laura, 1987). There are several factors that contribute to NPA’s at banks and other financial institutions. Keeton and Morris, 1987 were one of the earliest researchers to examine the causes of loan losses. On examining the losses in 2,470 insured commercial banks in the United States (US) from 1979-85 using non performing loans (NPLs) net of charge offs as the primary measure of loan losses, it was observed that local economic conditions along with the weak performance of certain sectors contributed to the variation in loan losses recorded by these banks. The study also showed that commercial banks taking greater risk tend to record higher losses. Sinkey and Greenwalt, 1991 employed a simple log-linear regression model to study loan losses in large commercial banks in the United States from 1984 to 1987. They found that both internal and external factors contribute to the loan-loss rate of these banks. These authors find a significant positive relationship between the loan-loss rate and internal factors such as high interest rates, excessive lending, and volatile funds and also external factors such as depressed regional economic conditions. Using a vector auto regression model Keeton, 1999 analyzed the impact of credit growth and loan delinquencies in the US. The study found a strong relationship between credit growth and impaired assets. Rapid credit growth, which was associated with lower credit standards, contributed to higher loan losses in certain states in the US.

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