The empirical research aims to assess the changes of credit risk related banks‘ performance indicators, the macroeconomic changes in Lithuania and other European Union (EU) countries, and to find the interrelations between these factors. The research consists of 8 parts that are based on the statistical analysis of Lithuanian and EU indicators. The research structure and expected results are shown in Figure 2.1.
Figure 2.1. The empirical research structure and expected results
Analysis object Expected results
1. Lithuanian commercial banking system
Main aggregated financial indicators of Lithuanian banks
2. NPLs problem in Lithuanian commercial banks
The changes of NPLs related financial results of Lithuanian banks
3. Lithuanian macroeconomic indicators and business cycle
The changes of macroeconomic environment of Lithuanian banks
4. Bankruptcy predictions in micro- level considering the sectorial risk
The statistical enterprises bankruptcy prediction model
5. Lithuanian business and households indebtedness
The critical debt burden ratios causing NPLs problem in economic recession
6. Non-performing loans problem in European Union
The similarities and differences of NPLs in EU countries
7. Macroeconomic indicators in EU countries
The impact of macroeconomic changes on NPLs in EU
8. Non-performing loans predictions in EU countries
The statistical model enabling to foresee the NPLs growth in EU
Further the research structure, statistical data and analysis methods are explained.
1. Lithuanian commercial banking system. This chapter aims to
present the Lithuanian commercial banking system, to estimate the changes of main balance-sheet entries: the assets, loan portfolio and deposits.
2. The problem of non-performing loans and the changes of
financial condition in Lithuanian banks. The statistics of non-performing
loans (NPLs), the credit risk related financial indicators of interest income, net interest income, net profit, impairment of loans, return on assets, return on equity will be analyzed. The analysis results will show the deterioration of main financial rates of Lithuanian banks when the proportion of non- performing loans increased. The stock market data analysis also will measure the decrease of listed bank’s shares prices after the deterioration of banks’ loan portfolio quality and financial results.
3. The changes of commercial banks macroeconomic
environment in Lithuania. The business cycle in Lithuanian economy will
be substantiated by the macroeconomic indicators of GDP, exports, imports and gross capital formation (investments). As the solvency and credit risk of business enterprises depend on their financial condition, the main financial indicators of revenue, net income and net profitability will be analyzed. The number of profitable and loss-making companies, the bankruptcy statistics will reveal the changes of Lithuanian credit risk in different stages of business cycle. The analysis of creditors’ claims statistics will measure the risk for banks to loose the lent money in case of a company’s bankruptcy. The households’ credit risk related economic indicators of compensation of employees, consumption expenditures of households, the average wages, unemployment and inflation rates will indicate how the problems of NPLs in Lithuanian banks increased after the deterioration of these rates. The changes of realty price index will be interrelated with the economic recession and impairment of loans. The research suggests that in the credit risk management of banks the public finance indicators of general government revenue and expenses, budget deficit, public debt are also good predictors of NPLs growth. The correlation analysis of mentioned variables will prove their interdependence and strong impact on NPLs in banks.
4. Enterprises credit risk assessment model considering the
industry sectors sensitivity to the macroeconomic changes. Analyzing
the set of bankrupted and profitable Lithuanian enterprises the statistical bankruptcy prediction model will be developed. The multivariate adaptive regression splines and logistic regression methods will be employed for the analysis of enterprises’ financial ratios. Extending the credit risk determinants from the enterprise’s micro-level, the industry sectors
statistical data will be analyzed to measure the sensitivity of these sectors to the fluctuations of business cycle. In general these interrelations will be estimated by the canonical analysis and polynomial regression methods. The variables of the number of bankrupted companies, revenue, income before taxes, profitability of main activity, the proportion of loss-making enterprises in every sector will allow to attribute the sectorial bankruptcy risk ranks that can help banks to assess the credit risk of such loan applicants expecting the macroeconomic changes in the country. The comparative analysis of Lithuanian districts will be implemented aiming to highlight the relative differences of industry sectors’ credit risk. The average net profitability, return on assets, current ratio, quick ratio and debt ratio values in the last year before the companies bankruptcy will be analyzed what can help banks to foresee the risk of a particular company to bankrupt if these financial ratios differ in the industry sectors. The cluster analysis will classify the sectors into 3 groups and the critical average financial ratios warning about the enterprises’ bankruptcy will be calculated.
5. Business and households indebtedness indicators as factors of
NPLs problem in banks. In this chapter the Lithuanian enterprises and
households indebtedness as the important factor of the ability to repay debts will be analyzed. The statistics of loan portfolio dynamics in Lithuanian banks will be presented and the relative indebtedness indicators of companies and households will be calculated. The overall loan portfolio, business, households loans, GDP, Lithuanian enterprises’ revenue and compensation of employees indicators will be used to calculate the relative indebtedness ratios. The estimation of ratios changes will allow to highlight the critical over-indebtedness levels in the peak point of Lithuanian business cycle that later turned into the oppressive debt burden for business and households causing the extensive growth of NPLs in Lithuanian banks. The reasons of this situation also will be suggested accenting the problem of irresponsible borrowing that was evident in Lithuania until the economic recession.
6. Non-performing loans problem in European Union. The non-
performing loans statistics of European Union in this chapter will be presented, what allows to understand the situation in the EU countries. The Lithuanian NPLs will be analyzed in the context of EU average values. The interrelations of GDP to one inhabitant and NPLs in banks will be estimated to measure the strength of EU countries economy and the NPLs problems in banks. The dynamics of the aggregated European Union banks’ assets, loan portfolio, deposits, interest income, net interest income and net profit will be presented. The capital adequacy ratios of EU and Lithuanian banks will
be analyzed comparing them to estimate the strength of commercial banking systems in the EU and to evaluate the ability of banks to absorb the unexpected losses.
7. The dependence of non-performing loans problem on
macroeconomic conditions in European Union. In this chapter the EU
countries will be classified into four groups of low, lower medium, higher medium and high NPLs in banks. The average macroeconomic indicators to 1 inhabitant (GDP, exports, investments, compensation of employees, consumption expenditures of households and general government) will be compared between these groups aiming to prove that the economic strength of a country is the very important determinant of NPLs in commercial banks. The EU countries will be selected that suffered from the highest NPLs and the dynamics of macroeconomic indicators in these years will be estimated. This will allow to conclude how the deterioration of macroeconomic conditions in a country increases the non-performing loans amount in its banks.
8. The NPLs in European Union countries prediction model. The
statistical model for the prediction of NPLs changes will be developed in this chapter. The set of macroeconomic indicators will be formed and their basic indices reflecting the changes of macroeconomic rates will be used as the independent variables. Because the changes of NPLs in EU countries can be different, the countries in the first stage of analysis will be separated into three groups. The logistic regression, factor analysis and probit regression methods will be employed for this purpose. The countries classification accuracy will be measured. The discriminant analysis models will be developed to classify the EU countries according to the expected low or high growth of the non-performing loans. The model’s prediction ability will be tested analyzing the out-of-sample data.
The statistical data of Statistics Lithuania, Bank of Lithuania, EUROSTAT, European Central Bank and World Bank will be used in the empirical research.