Comparing determinants of Non performing Loans across developed under developed and developing countries

Project Title

Comparing determinants of Non performing Loans across developed under developed and developing countries

Project Area of Specialization Artificial IntelligenceProject Summary

Due to the increasing trend of nonperforming loans (NPLs) in many Developed, developing and underdeveloped economies we will analyse and compare the influence of macroeconomics variables and bank specific factors on non-performing loans and the implications for financial stability in the region. The variations in the economic circumstances are believed to have a critical role to play in determining the level of nonperforming loans. In the project we will attempts to study the macroeconomic determinants which effects NPLs quality in global economies by analyzing and regress the Panel data over the period 2008-2018. We will use data of 15 countries, based on the criteria of  HDI( Human Development Index) as the developed, underdeveloped and developing nations.  We will also highlight the policies to tackle the NPLs and suggest a multi-pronged strategy for the speedy recovery of NPLs in banking sectors.

Project Objectives

In our project will contributes that the effect  of NPLs and macro financial  feedback in two ways. Firstly, we will emphasize on the relationship between non-performing loans and financial sector development. Secondly the influence of macro-economic variables such as Unemployment, GDP growth, Deflation are negatively associated with NPLs. A mixed research approach and explanatory design will be adopted in carrying out this research.

The main causes of NPL are high-interest rate, Low GDP, Poor credit appraisal, Inflation, unemployment and improper lending disbursement to agriculture sector. NPL have negative impact on the economy and financial institutions.

The core objective is to analyze that the effect of increment of NPLs  may weakens the macro economic performance.  This study will suggest the diversified strategy to speedy recovers the NPLs in underdeveloped, developing and developed economies.

The data for the analysis will be taken from World Development Index (WDI) published by World Bank.

Project Implementation Method

We will use panel unit root tests to determine the stationarity of the variables. The panel unit root tests using both augmented Dickey–Fuller and Phillips–Perron tests reveal that all variables are stationary. Given our granular dataset covering 15 rising economies within the World over 11 years, we tend to use the dynamic panel model so as to expand the empirical analysis and to assess the determinants of NPLs in several World economies, this econometric model is applied wherever the lagged dependent variable is used as an explanatory variable and to capture the effect of other omitted explanatory indicators. The inclusion of the lagged dependent variable as a regressor is usually utilized in dynamic panel data, which is just like the methodology of Louzis et al. (2012), Castro (2013), Makri et al. (2014), Ghosh (2015), and Dimitrios et al. (2016). Significant NPLs as the dependent variable and macroeconomic and bank-specific fixed effects as an independent varaibles, we use the following representation 

NPLi,t = ao+ b NPLi,t-1 + a1 Xi,t+a2 Mi,t + ?i,t

Ui + ?i,t = ?i,t , has the standard error element structure

where, ao is a constant term. The subscripts i=1, ….,n and t=1, …,T denote the cross-sectional units and time dimension of panel, respectively. The regressors are classified into three groups: where NPLi,t-1 is the lagged dependent variable , Xi,t is the vector of bank-specific variables(DoCr) and Mi,t  is the vector of country specific macroeconomic variables(Unemp, Inf, GDoSv, GDP_gr, NDoCr, REER, X, FDI, Li, PR,EoM). a1 and a2 are the coefficients of the regression to be estimated. Ui refers to unobserved bank specific and macroeconomic effects (heterogeneity) and ?i,t is an independently and identically distributed error term.

The above-mentioned equation is estimated with the dynamic specification using Generalized Method of Moments (GMM) proposed by Arellano and Bond (1991). Under dynamic estimation, the macroeconomic variables and the bank-specific variables are assumed as exogenous (control variables) and endogenous determinants, respectively

Benefits of the Project

In our analysis we are going to compare  the underdeveloped, developing and developed group of countries to find the feedback of macroeconomic variables and bank specific factors on the non-performing loans and their effects on economic and financial sector such as distort allocation of credit, worsen market confidence and slow economic growth and we will suggest some policies that are adopted by  developed economies for reducing NPLs to underdeveloped and developing ones as reducing NPLs will have a positive impact on the economy. The countries which experiences inflow of fresh credit grow and actively seek to resolve NPLs do comparably well but if the NPL problem is ignored, the economic performance may  suffers.

Technical Details of Final Deliverable

In our project we will be using a Software Stata13 to regress the model using estimated dynamic specification using Generalized Method of Moments (GMM) proposed by Arellano and Bond (1991) because  the macroeconomic variables and the bank-specific variables are assumed as exogenous (control variables) and endogenous determinants, respectively 

All the test for data cleaing and processing will by estimated using Stata13 and for creating such model we will need data from different official websites. 

Final Deliverable of the Project Software SystemCore Industry FinanceOther IndustriesCore Technology Artificial Intelligence(AI)Other TechnologiesSustainable Development Goals Decent Work and Economic Growth, Industry, Innovation and InfrastructureRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	39030
Stata13 Software (License)	Equipment	1	30030	30030
Required Data acquiring from countries official websites	Miscellaneous	1	9000	9000