The banking sector plays a crucial role in the economic growth of a nation. The purpose of this study is to examine the long-term association between banks’ performance and the economic growth of a developing economy: India. The study used a panel of data of 20 public sector banks for the period 2009 to 2019. It applied the Pedroni and Kao test of co-integration, panel vector error correction model (VECM) dynamic, panel fully-modified ordinary least squires OLS (FMOLS), and dynamic OLS (DOLS) to estimate the relationship of interest margin return on assets, bank investment, and lending capacity of the bank with gross domestic product (GDP) of the country. The identification and incorporation of these bank-related variables are the innovations of this study. The results indicate that the bank-related variables are co-integrated with economic growth. Further analysis indicates a significant relationship between interest margin and return on assets with economic growth. In addition, lending capacity and investment activities are not significantly associated with economic growth, leading to the policy recommendation to improve upon these two factors in order to achieve higher growth rates.

https://www.mdpi.com/2227-7099/9/1/38/pdf

Banks’ Performance and Economic Growth in India: A Panel Cointegration Analysis

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. 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. Loan growth has a significant effect on bank-specific and macroeconomic-specific 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. 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.

https://link.springer.com/content/pdf/10.1186%2Fs40854-016-0043-8.pdf

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

http://prr.hec.gov.pk/jspui/bitstream/123456789/9546/1/Muhammad_Ilyas_Management_Science_HSR_2018_AWKU_mardan_08.01.2018.pdf

Impact of Corporate Governance of Earnings Management and Cost of Capital: Evidence From Pakistan

In this paper, endogenous and exogenous factors that affect the credit growth rate of some Western Balkan countries (Bosnia and Herzegovina, Croatia and Serbia) and the credit policy in Turkey will be investigated through a multiple regression analysis. The credit growth rate will be used as the dependent variable while the rate of the non-performing loans along with the growth rate of the deposit, the return of equity and the real growth rate of the gross domestic product will be used as independent variables. In this paper the STATA 13.0 software package will be used. This data analysis will include a quarterly basis data for the period: 2007q1 – 2017q2 due to its higher significance. The result of the regression analysis showed that there is a reverse relationship between the rate of the non-performing loans and the credit growth rate for all the observed countries. The high share of problematic loans in total loans relatively reflects in a negative way the overall tendency of the banks towards taking risks and credit growth. It reduces the profitability of the banking sector and increases the systemic risk as well. The basic results of the regression analysis also showed a positive relationship between economic growth and the credit growth of banks. On the one hand, the economic growth of the region insufficiently follows the credit growth due to the stagnation of the real sector, and the recovery of the economy on the other hand. Similarly, there is a positive relationship between the growth rate of the deposits and credit growth, since the deposits sources are the basis for performing credit nomination. Except for Croatia where a negative correlation was recorded, there is a positive relationship in terms of the return on equity and credit growth.

Credit growth and non-performing loans: evidence from Turkey and some Balkan countries

Shareholders' wealth maximization is the ultimate objective of firms. Economic value added (EVA) is a financial metric linked to shareholders' wealth maximization. Prior studies revealing the effic...

https://www.tandfonline.com/doi/pdf/10.1080/23311975.2019.1653544

Asymmetric impact of economic value-added dynamics on market value of stocks in Pakistan stock exchange, a new evidence from panel co-integration, FMOLS and DOLS

Bank credit plays an important role in the economy of any nation. The current study examined the association among bank credit to private sector and economic growth in Pakistan. Economic growth was taken as dependent variable, while bank credit to private sector, interest rate, inflation, investment to GDP and government consumptions were taken as independent variables. Secondary data were collected from World Bank Indicator, ranging for the period 1973 to 2013. Descriptive research and correlation were used to check the normality of data. Unit root test was used to check the stationarity of variables. Co-integration VECUM and Granger Casuality test were statistically used to test the variable relationship and casuality effect of the variable. Regression analysis was used to analyze the impact of bank credit on economic growth. The findings of the study showed that bank credit had extensive relationship with economic progression; in short term the relationship was also significant. Regression analysis showed that there was adverse impact of bank credit on economic growth in Pakistan. However, problem associated with bank credit facility is the constraint and regulation imposed by SBP on the percentage of credit to be given to the Entrepreneurs. For solitary in the meantime bank lending has a casual influence on economic growth, there is a policy need to give devotion to liberalization the monetary sector.

/pdf/impact-of-bank-lending-on-economics-growth-in-pakistan-an-3vhgcqgxty.pdf

Impact of Bank Lending on Economics Growth in Pakistan: An Empirical Study of Lending to Private Sector

The importance of unsupervised clustering methods is well established in the statistics and machine learning literature. Many sophisticated unsupervised classification techniques have been made available to deal with a growing number of datasets. Due to its simplicity and efficiency in clustering a large dataset, the k-means clustering algorithm is still popular and widely used in the machine learning community. However, as with other clustering methods, it requires one to choose the balanced number of clusters in advance. This paper’s primary emphasis is to develop a novel method for finding the optimum number of clusters, k, using a data-driven approach. Taking into account the cluster symmetry property, the k-means algorithm is applied multiple times to a range of k values within which the balanced optimum k value is expected. This is based on the uniqueness and symmetrical nature among the centroid values for the clusters produced, and we chose the final k value as the one for which symmetry is observed. We evaluated the proposed algorithm’s performance on different simulated datasets with controlled parameters and also on real datasets taken from the UCI machine learning repository. We also evaluated the performance of the proposed method with the aim of remote sensing, such as in deforestation and urbanization, using satellite images of the Islamabad region in Pakistan, taken from the Sentinel-2B satellite of the United States Geological Survey. From the experimental results and real data analysis, it is concluded that the proposed algorithm has better accuracy and minimum root mean square error than the existing methods.

/pdf/model-selection-using-k-means-clustering-algorithm-for-the-6ulak45b.pdf

Ishfaq Ali

Papers

Impact of Bank Lending on Economics Growth in Pakistan: An Empirical Study of Lending to Private Sector

Model Selection Using K-Means Clustering Algorithm for the Symmetrical Segmentation of Remote Sensing Datasets