The Impact of Bank-Specific and Macroeconomic Factors on Non-performing Loans in Sri Lankan Commercial Banks

The main purpose of this study is to ascertain the effect of bank-specific and macroeconomic factors on non-performing loans in systemically and non-systemically important commercial banks in Sri Lanka over 10 year’s period from 2004 to 2013.Also,the study examines the impact of civil war that prevailed in the country for 30 years on the ex-post credit risk of the banking sector.The study employed panel data methodology to investigate the effect of bank-specific and macroeconomic factors on non-performing loans.Panel unit root test has been undertaken in order to test the stationary of the variables.Hausman test and Wald coefficient restriction test were used to select the appropriate model out of pooled,random,and fixed effect.A dummy variable panel regression model adopted to study the war effect,considering 2009 as the structural year.Findings revealed that return on assets as a proxy for bank efficiency has a significant negative influence,while non-interest income as a proxy for income diversity is positively correlated with non-performing loans of systemically important banks.Both real gross domestic products and lending rates were highly significant in both bank types.On contrary with literature,growth in bank branches is negatively correlated.Public banks do not account for higher level of non-performing loans compared to their private counterpart.Finally,it was identified that civil war had an effect on the level of non-performing loans in commercial banks.The research would have benefited if the analysis is carried out among classified types of loans offered by commercial banks.Future researchers should involve in identifying the most significant contributing loan type to the non-performing loans and its determinants.This study is one of the few studies which have investigated the causes of non-performing loans in the commercial banking industry in Sri Lanka.The analysis of civil war and its impact on non-performing loans is the first study of that nature to be conducted in the context.

Analyzing China's Non-performing Financial Assets

The non-performing assets of China’s financial institutions totaled RMB l,800 billion. Has this threatened the safety of China’s financial industry? Where do this large amount of nonperforming assets come from?

Non-performing loans(NPLs),liquidity creation,and moral hazard:Case of Chinese banks

This study analyzes the impact of non-performing loans(NPLs)on bank liquidity creation to investigate the existence of moral hazard problem in Chinese banks.It uses data from 197 listed and unlisted Chinese banks,spanning the period 2005 to 2014.Total liquidity creation by Chinese banks is declining,and NPLs ratio has started to increase following a continuous decline between 2005 and 2012.Using one-step system GMM estimation,fixed and random effect model,and pool data analysis,we find that liquidity creation by Chinese banks does not depend on NPLs ratio,i.e.,we did not find the evidence of moral hazard problem in Chinese banks.We repeated the analysis for small and large banks and the results of these sub-samples reinforced our findings for the aggregate sample.

Do Macroeconomic Determinants of Non-Performing Loans Vary with the Income Levels of Countries?

This paper explores the macroeconomic determinants of non-performing loans(NPL) in 19 Asian countries(low to high income economies) using the Generalized Method of Moments estimation approach based on the economic data for the period between 1998 and 2015. The categorization of the economies is based on the average gross national income per capita as set by the World Bank.Specifically, the paper aims to evaluate if the determinants of NPL vary with the income levels of the countries. The results indicate that the NPL is strongly influenced by the inflation rate. The effect is,however, negative in the high-income and the middle-income countries and positive in the low-income countries. The GDP per capita has a dynamic negative relationship with the NPL in the high-income and the low-income countries. The remittance has a significant positive association in the high-income and a significant negative association in the low-income countries. Similarly, the unemployment rate has a positive effect on NPL in the middle-income and the low-income countries. With the rise in the official exchange rate, the NPL level increases in the low-income countries. The overall estimation results suggest that the NPL in Asian banking system depend on some key macroeconomic variables,such as unemployment rate, inflation rate, official exchange rate, remittance received and gross domestic product per capita, and these associations vary with the income level of the countries. Therefore,economic level of a country should be carefully considered while formulating credit policy to minimize credit risks in the banking system.

A dynamic credit risk assessment model with data mining techniques:evidence from Iranian banks

Giving loans and issuing credit cards are two of the main concerns of banks in that they include the risks of non-payment.According to the Basel 2 guidelines,banks need to develop their own credit risk assessment systems.Some banks have such systems;nevertheless they have lost a large amount of money simply because the models they used failed to accurately predict customers’defaults.Traditionally,banks have used static models with demographic or static factors to model credit risk patterns.However,economic factors are not independent of political fluctuations,and as the political environment changes,the economic environment evolves with it.This has been especially evident in Iran after the 2008-2016 USA sanctions,as many previously reliable customers became unable to repay their debt(i.e.,became bad customers).Nevertheless,a dynamic model that can accommodate fluctuating politicoeconomic factors has never been developed.In this paper,we propose a model that can accommodate factors associated with politico-economic crises.Human judgement is removed from the customer evaluation process.We used a fuzzy inference system to create a rule base using a set of uncertainty predictors.First,we train an adaptive network-based fuzzy inference system(ANFIS)using monthly data from a customer profile dataset.Then,using the newly defined factors and their underlying rules,a second round of assessment begins in a fuzzy inference system.Thus,we present a model that is both more flexible to politico-economic factors and can yield results that are max compatible with real-life situations.Comparison between the prediction made by proposed model and a real non-performing loan indicates little difference between them.Credit risk specialists also approve the results.The major innovation of this research is producing a table of bad customers on a monthly basis and creating a dynamic model based on the table.The latest created model is used for assessing customers henceforth,so the whole process of customer assessment need not be repeated.We assert that this model is a good substitute for the static models currently in use as it can outperform traditional models,especially in the face of economic crisis.

Loan growth and bank solvency:evidence from the Pakistani banking sector

Background:The dramatic loan growth and changes in the Pakistani banking system in mid-2000s have led to significant research attention on borrowers and lenders.This expansion and diversification in financial sector was driven by structural reforms,political stability and significant economic growth.Against this background,this study investigates the loan growth and risk-taking behavior of the banks during the expansionary periods of lending.Method:This study used dynamic two-step system generalized method of moment’s estimation technique,based on data taken from 32 banks in Pakistan over 2006-2014.Result:Loan growth has a significant effect on bank-specific and macroeconomicspecific variables.Loan growth in the previous year raises non-performing loans and decreases the solvency of banks with a time lag of many years.The driving force behind this phenomenon is weak prudential regulation among competitors,the asymmetric information of the borrowers,and,most importantly,that banks underestimate the risk of lending during credit booms.Conclusion:More regulatory measures are required to ensure a strong financial system when the volume of non-performing loan grows significantly.An increase in the capital requirement policy for rapidly growing banks is also needed because the problem of abnormal loan growth cannot be detected at the current time.At the same time,strong supervision is necessary to avoid the adverse consequences of borrower selection.

Financial Supports to the Rejuvenation of the Old Industrial Base of Northeast China

Supporting the reconstruction and reform of the old industrial base in Northeast China is a significant strategy made by the Central Government. With the strategy going into effect, it will be a great opportunity for the economy and finance development in Northeast. Based on the financial facts in Northeast China and the analyses on the key issues concerning finance and the rejuvenation of the old industrial base, countermeasures in five aspects are put forwarded in this paper.

Does abnormal lending behavior increase bank riskiness?Evidence from Turkey

Purpose:This study empirically analyzes whether the rapid growth of loans and risktaking behavior during the expansion of loans affected non-performing loans(NPLs)and the solvency of financial institutions in the Turkish banking system.Design/methodology/approach:Using the GMM Generalized Method of Moments,this study used data on Turkish banks from 2011 to 2017 to test two hypotheses on the effects of loan growth on NPLs and solvency.Findings:This study finds significant results for the effect of loan growth on NPLs and solvency.NPLs rose from the previous year’s loan growth,which tended to reduce solvency.Research limitations/implications:Due to selected research methods,the results may lack generality.Therefore,future studies should test the propositions herein further.Practical implications:The results indicate that careful allocation behavior is required when lending.Additionally,these findings may be helpful to financial managers and decision makers.Originality/value:This study confirms the need to determine how to allocate loans during the loan boom periods.

Research on China’s Commercial Banks Rating and Ranking Based on DEA

China’s commercial banks are confronted with fierce competition from advanced big commercial banks abroad, which have much better performance in non-performing loans (NPLs) than China’s commercial banks. In this case, efficiency rating and ranking of China’s commercial banks are of great importance. We treat the rate of non-performing loans (NPLs) as an undesirable output from the operating process of commercial bank and utilize cross efficiency of DEA (Data Envelopment Analysis) to evaluate and rank China’s commercial banks between 2006 and 2008 horizontally and vertically. In the end we will propose our conclusion.

Sharing China’s Bank Restructuring Bill

This paper addresses the questions related to the cost of China＇s bank restructuring and how it has been financed. We first propose a framework for recognizing losses. Then, we examine the recent major moves by the Chinese Government to repair the country. ＇s bank balance sheets. Finally, we explore the implications of the Chinese Government＇s methods offunding bank restructuring. We find that the Chinese Government has been decisive in confronting the costly task of bank restructuring. So far, Chinese taxpayers have paid most of the bill for bank restructuring.