Business transactions are going to be fast day by day because of dynamic changes in the global environment. Merger and Acquisition is a strategy adopted by the organizations globally to meet the needs of recent dynamic business environment. It has achieved much attention and importance in corporate world. In Pakistan, this strategy has been used widely in banking sector. Therefore, the objective of the study is to evaluate the financial performance of banks in Pakistan after M&A. The financial and accounting data for 10 banks was taken from the Financial Statement Analysis by State Bank of Pakistan. Profitability & Efficiency, Leverage, and Liquidity ratios were used to measure the financial performance, where pre and post ratio analysis was done. Results of the study show that there is no positive improvement in the financial performance of the banks in Pakistan after Merger and Acquisition.

Financial performance of banks in Pakistan after Merger and Acquisition

The purpose of this paper is to explore the effect of monetary policy on bank risk in the banking system in some MENA countries. It explores how some economic and credit indicators affect the level of risk in the banking sector. It combines many factors that could affect banks’ risk appetite such as macroeconomic conditions, banks’ credit size and lending growth. The authors use nonperforming loans as a proxy for banking sector risks. At first, the authors have analyzed the linear relationship between monetary policy and credit risk. As mentioned above, nonlinearity is expected in the underlying relationship, and, thus, they have investigated the nonlinear relationship to deeply analyse the relationship using the dynamic panel threshold model, as stimulated by Kremer et al. (2013). Threshold models have gained a great importance in economics and finance for modelling nonlinear behaviour. Threshold models are useful in showing the turning points in the behaviour of financial and economic indicators. This technique has been applied in this study to study the effect of monetary policy on credit risk.,This paper is divided into the following sections: Section 2 which previews the recent literature; Section 3 which includes some stylized facts about the relationship between credit risk and monetary policy; Section 4 which deals with the model and methodology; Section 5 which handles the data sources and discusses the results, and finally Section 6 which is the conclusion. The paper adopts dynamic panel threshold model of Kremer et al. (2013).,The results show that the relationship between monetary policy and credit risk is positive and significant to a certain threshold, 6.3. If the lending interest rate is higher than 6.3, this increases the credit risk in the banking sector, because increasing the lending interest rate imposes huge burdens on the borrowers, and, therefore, the bad loans and nonperforming loans become more likely. Thus, the MENA countries need to decrease the lending interest rate to be less than 6.3 to reduce the effect of monetary policy on credit risk. Further, these results are qualitatively robust regarding the inclusion of additional control variables, using alternative threshold variables and further endogeneity checks of the credit risk, such as Risk premium and the squared term of the lending interest rate. The results of taking the risk premium and the squared term of the lending interest rate as a threshold served the analysis and confirmed the positive relationship between monetary policy and credit risk above a certain threshold. As for the risk premium, the relationship below the threshold was negative and significant. Other related research points might be a good avenue for the future research such as applying this approach to micro data of banks from different MENA countries. Also, more sophisticated approaches like time-varying panel approach to assess the relationship over the time can be applied.,The importance of this paper lies in the fact that it does not only study the effect of time, but it also focuses on the panel data about some economic and credit indicators in the MENA region for the first time. This is because central banks in the MENA region have common characteristics and congruous level of economic growth. Therefore, to study how the monetary policy affects those countries’ credit risks in their lending policies, this requires careful analysis of how the central banks in this region might behave to control default risks.

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The effect of monetary policy on credit risk: evidence from the MENA region countries

This research analyzes the factors that affect the credit growthof the Vietnamese commercial banking system. This study usedata from the annual reports and financial statements of 16commercial banks listed on the HOSE, HNX, and UPCOMexchanges that have operated continuously from 2011 to 2020.Two linear regression models were employed with two dependentvariables: the credit growth rate and credit size. The findingsindicate that credit size in the previous period, the annualgrowth rate of capital mobilization, bank size, and return onassets (ROA) positively affect the credit growth of Vietnamesecommercial banks while liquidity ratio negatively affects them.Also, the bad debt ratio’s effect on credit growth is unclear. Hence,some suggestions and recommendations are given for commercialbanks to maintain stable, safe, and sustainable credit growth.JEL Classification: G21, E50, E52

Factors Affecting Credit Growth of Vietnamese Commercial Banks

Financial statements of nineteen mature banks have been patronized to examine the impact of macroeconomic indicators and bank-specific determinants on the NPLs ratio through Quantile and Panel Data regression approaches. The impact of macroeconomic indicators on credit risk is statistically estimated for banking network via two directions. First, different quantiles are econometrically calculated, assessed and compared during 2007-12. Second, the Panel Data estimation is utilized in the same way to verify the outcomes of quantile regression and to check the robustness. Results indicate that the impact of real money supply on the banks' NPLs in 25%, 50%, and 75% of data is positive and significant in line with empirical evidence. The coefficients of the other variables (including the ratio of individual banks' performing loans to total deposits, individual banks' performing loans to total loans ratio, as well as GDP would be positively significant as well. The real interest rate has negatively-significantly driven NPLs. The banks' NPLs are generally exacerbated by the impact of higher real money supply over the long run, real interest rate in the money market and upper return in the assets market mainly because of the negative-inflationary transmission effect.

The Impact of Macroeconomic Indicators on the Nonperforming Loans (Case of Iran)

The banks' response to their changes in leverage ratios is examined and evaluated in this paper. This reaction can be interpreted as if the coefficient of total debts to equity (lev1) and total assets to equity (lev2) are positive as anticipated in the banking network of Iran. The paper uses data from 31 Iranian banks' annual databases during the course of 2006-13 in order to estimate an empirical panel data model of banks' balance sheet adjustment. We identify the leverage ratio degree to show that both equity and liabilities tend to adjust to move leverage positively without considering the state of the Iranian economy. On the other hand, the index of leverage coefficient conditioned by the state of the economy is negative which replicates that banks tend to experience a negative impact of leverage on the return to equity as a result of cost push due to higher ratio of assets to equity in the bust and inappropriate return on investment. Furthermore, the non-performing loans ratio coefficient is negative and significant which proves that one percent increase in the nonperforming loans has led to a less than one percent decrease in the return on equity ratio as expected, but the total loans to total deposits ratio depicts a negative-significant coefficient which denotes the higher non-performing loans have caused that loans ratio increase will not necessarily give rise to higher returns for the banks. Besides, the leverage ratio (lev2) is positive as expected and banks gain higher returns through higher leverage. However, the leverage measure's coefficient conditioned by the state of the economy (dummy) is negatively significant owing to cost push from lower return on investment and higher ratio of assets to equity in the bust.

Degree of Leverage Ratio Analysis in the Iranian Banking Network

This study examines the effect of regulatory policy on efficiency under prudential framework among banks listed in the Iranian Securities and Exchange Organization over the period 2003 to 2015. Arellano-Bond estimation method has been patronized to investigate the effect of regulatory policies on efficiency. Results indicate that regulatory policy indicator indexing reserve requirement on investment deposits has had a positive relationship with the efficiency ratio, Sum of Assets Circulation Ratio (SACR), although it is not meaningful. On the other hand, the legal reserve ratio is positively and meaningfully affecting efficiency in financial institutions. Leverage ratio is negatively and meaningfully affecting the efficiency ratio (SACR) which highlights the point that as the financial institutions heighten their leverage ratio, it will lead to lower sum of asset circulation ratio as the best indicator of efficiency under activity proportions. Higher risk will lead to higher Financial Cost to Net Profit ratio (FCNP) interpreted as lower efficiency in financial institutes. Furthermore, regulatory policy denoted by the legal reserves ratio with one lag interval negatively-significantly influences the financial cost ratio.

The Effect of Regulatory Policy on Efficiency under Prudential Framework among Listed Iranian Banks

Development of E-banking has modified the structure of banking business and banking performance, efficiency, risk and challenges which have also been articulately recognized based on the international best practices. E-banking brazenly expedites and streamlines financial transactions via enhancing technology and expanding the bank services in comparison with conventional banking. Accordingly, online access to the banks' products, financial statements, payment services and even credit scoring has considerably improved bank-customer relationships in the context of lending and borrowing practice, deposit composition, investment opportunities, trade finance options as well as account management diversification. The impact of recent E-banking developments on banking is statistically tested for the banking network of Iran using Dynamic Panel Data approach. Results indicate that the ratio of ATMs and Electronic Cards to bank assets positively and significantly influence profitability ratios. Meanwhile, the ratio of online branches and Pin Pads to the banks' assets negatively and significantly affects profitability although the transaction volumes have surged for the banks under study.

E-banking and Soundness Indicators for the Banking Network of Iran

This paper applies sample selection method to examine the relationship between profitability and capital adequacy ratio of Iranian banks. The reason why sample selection model estimation is assumed to be utilized would be banks which do not comply with the minimum capital adequacy ratio requirement would not denote their actual profitability in the profit/loss statement. Even though this information is reflected in their annual financial statement, given the relationship among the financial statement variables, it could not be justified that banks which are profitable would be able to comply with the capital adequacy ratio. In other words, the bank which does not meet the capital adequacy requirement would not be profitable. Based on the accounting and economic rules which are transparently depicted and applied in the Basel Accords, loss of the banks results in a sudden decrease in banks’ equity. Applying a sample selection model illustrates that there are two decision and selection equations. Main covariates such as demand deposit to total assets, loans to total assets, saving deposits to total deposits and nonperforming loans to total assets prove to be negatively significant.

Mohammad Valipour Pasha

Papers

The Impact of Macroeconomic Indicators on the Nonperforming Loans (Case of Iran)

Degree of Leverage Ratio Analysis in the Iranian Banking Network

The Effect of Regulatory Policy on Efficiency under Prudential Framework among Listed Iranian Banks

E-banking and Soundness Indicators for the Banking Network of Iran

Can Capital Adequacy Ratio Specify Banks' Profitability? A Case Study of Iran