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Banks and other financial institutions are a unique set of business firms whose assets and liabilities, regulatory restrictions, economic functions, and operating make them an important subject of research. Banks performance monitoring, analysis and control needs special analysis in respect to their operation, productivity and performance results f...
Contexts in source publication
Context 1
... sample includes all 6 domestic commercial banks operating in Estonia during this period. Table 4 contains some information on the variables used. The columns of Table 4 show the maximum, minimum, average, standard deviation and coefficient of variation (CV) over bank for four years. ...
Context 2
... 4 contains some information on the variables used. The columns of Table 4 show the maximum, minimum, average, standard deviation and coefficient of variation (CV) over bank for four years. The data in Table 4 allows an increase in productivity, while the value of bank products (loans, deposits and other banking services) has increased more that the bank inputs (labor and offices number). ...
Context 3
... columns of Table 4 show the maximum, minimum, average, standard deviation and coefficient of variation (CV) over bank for four years. The data in Table 4 allows an increase in productivity, while the value of bank products (loans, deposits and other banking services) has increased more that the bank inputs (labor and offices number). Reputedly this could be the result of technical efficiency or technological progress. ...
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Citations
... Where: ROA = Return on Assets EM = Equity Multiplier (02) The above formula could also be expanded into several components based on the composition of the net income of the respective industry. The modified version of DuPont financial ratio analysis is used by Kirikal, Sorg, and Vensel (2011) to investigate the Estonian banking sector performance. Almazari (2012) and Georgios and Georgios (2011) estimated banks' ROE to measure bank performance using Du Pont model. ...
Risk management practices of financial institutions play a significant role in financial stability and thereby strengthen the confidence of stakeholders. The purpose of this study is to examine the impact of banks' risk management capabilities on stock returns. Four basic risk management capability measures are used for this purpose. The data from the financial reports of eight listed commercial banks for the period from 2006 to 2018are used for the analysis. The Du Pont analysis of ROE calculation is used to identify four risk management variables such as interest rate risk management, bank income diversification, credit risk management, and solvency risk management. The standard market model is estimated using two different regressions as regression 01 and regression 02 to capture the impact of firm size (control variable) on the whole model. The findings of regression 01 and regression 02 reveal that market return (and income diversification (NNIM) are significant to predict bank stock returns. However, Interest rate risk management capability (NETIM) credit risk management capability (PROV), solvency risk management capability are insignificant variables under both models. The impact of firm size on the whole model is also insignificant and there is an insignificant positive relationship between bank stock returns and firm size (TA). Therefore, Bank managers can employ effective strategies to increase non-interest income hence it contributes to generate a higher return for the shareholders. Therefore, the study suggests shareholders purchase the stocks of banks which have increased non-interest income and to aware of the market index changes to increase their returns.
... Where: ROA = Return on Assets EM = Equity Multiplier (02) The above formula could also be expanded into several components based on the composition of the net income of the respective industry. The modified version of DuPont financial ratio analysis is used by Kirikal, Sorg, and Vensel (2011) to investigate the Estonian banking sector performance. Almazari (2012) and Georgios and Georgios (2011) estimated banks' ROE to measure bank performance using Du Pont model. ...
Risk management practices of financial institutions play a significant role in financial stability and thereby strengthen the confidence of stakeholders. The purpose of this study is to examine the impact of banks' risk management capabilities on stock returns. Four basic risk management capability measures are used for this purpose. The data from the financial reports of eight listed commercial banks for the period from 2006 to 2018are used for the analysis. The Du Pont analysis of ROE calculation is used to identify four risk management variables such as interest rate risk management, bank income diversification, credit risk management, and solvency risk management. The standard market model is estimated using two different regressions as regression 01 and regression 02 to capture the impact of firm size (control variable) on the whole model. The findings of regression 01 and regression 02 reveal that market return (and income diversification (NNIM) are significant to predict bank stock returns. However, Interest rate risk management capability (NETIM) credit risk management capability (PROV), solvency risk management capability are insignificant variables under both models. The impact of firm size on the whole model is also insignificant and there is an insignificant positive relationship between bank stock returns and firm size (TA). Therefore, Bank managers can employ effective strategies to increase non-interest income hence it contributes to generate a higher return for the shareholders. Therefore, the study suggests shareholders purchase the stocks of banks which have increased non-interest income and to aware of the market index changes to increase their returns.
... Where: ROA = Return on Assets EM = Equity Multiplier (02) The above formula could also be expanded into several components based on the composition of the net income of the respective industry. The modified version of DuPont financial ratio analysis is used by Kirikal, Sorg, and Vensel (2011) to investigate the Estonian banking sector performance. Almazari (2012) and Georgios and Georgios (2011) estimated banks' ROE to measure bank performance using Du Pont model. ...
Risk management practices of financial institutions play a significant role in financial stability and thereby strengthen the confidence of stakeholders. The purpose of this study is to examine the impact of banks' risk management capabilities on stock returns. Four basic risk management capability measures are used for this purpose. The data from the financial reports of eight listed commercial banks for the period from 2006 to 2018are used for the analysis. The Du Pont analysis of ROE calculation is used to identify four risk management variables such as interest rate risk management, bank income diversification, credit risk management, and solvency risk management. The standard market model is estimated using two different regressions as regression 01 and regression 02 to capture the impact of firm size (control variable) on the whole model. The findings of regression 01 and regression 02 reveal that market return (and income diversification (NNIM) are significant to predict bank stock returns. However, Interest rate risk management capability (NETIM) credit risk management capability (PROV), solvency risk management capability are insignificant variables under both models. The impact of firm size on the whole model is also insignificant and there is an insignificant positive relationship between bank stock returns and firm size (TA). Therefore, Bank managers can employ effective strategies to increase non-interest income hence it contributes to generate a higher return for the shareholders. Therefore, the study suggests shareholders purchase the stocks of banks which have increased non-interest income and to aware of the market index changes to increase their returns.
... Changes in productivity may be measured by an output-oriented or input-oriented approach. According to Kirikal, Sõrg and Vensel (2004), one way to measure the change in productivity is to see how much more output has been produced, using a given level of inputs and the present state of technology, relative to what could be produced under a given reference technology using the same level of inputs. An alternative is to measure change in productivity by examining the reduction in input use, which is feasible given the need to produce a given level of output under a reference technology. ...
Measuring banks’ performance and productivity needs special attention, as for Albania, the banking industry remains the central part of the financial system. This paper estimates the productivity change in Albanian banking sector, using the Malmquist productivity index. The dataset includes the end of year data of 16 banks that operate in Albania, for the period 2006-2017. This paper aims to bring new insights on the productivity of Albanian banking sector, as this is a rarely treated topic in literature. The purpose of this paper is to measure Malmquist productivity index and its components, efficiency change and technological change for banks in Albania. The results show that Albanian banking sector for the period under analysis, experienced a decrease in productivity, which mainly is due to the lack of technical improvements. On the other side, efficiency has improved during this period, and this result is driven by improvements in pure efficiency and scale efficiency. The medium size banks have shown a total factor productivity increase, compared to large and small banks.
... This approach is used for studying operational efficiency, and assumes that the output is given by the customers' demand and the objective of the bank is to minimize the input consumption in delivering these banking services. In this line, Kirikal et al. (2004), in measuring the Malmquist Indexes of productivity change in Estonian banking, chose as output variable the deposits, loans and other bank services and as input the number of employees and number of offices. On the same line, Kablan (2009) in measuring bank efficiency in developing country, the case of WAEMU found three inputs: labour, physical capital and financial capital to produce deposits collection, loans distribution and securities investment. ...
The objective of this study is to analyze the productivity growth of Togolese banks. The study period (2000–2008) corresponds to the post-financial liberalization in the West African Economic and Monetary Union (WAEMU) zone and the third phase of a changed banking and financial environment. We develop a new combined Malmquist index based on ‘P-index II’ in order to estimate the total factor productivity (TFP) growth and its components. The empirical results show that the TFP has considerably increased for the whole industry, in which technical change is found to be a more important source of productivity growth to Togolese banks compared to efficiency change. In line with this, it is indicated that the financial liberalization does not improve the technical efficiencies of banks in Togo on a whole.
... Technical change (TECH) is a measure of the change in the hospital production technology; it measures the shift in technology use between years t and t + 1. TECH is greater than, equal to or less than one when the technological best practice is improving, unchanged, or deteriorating, respectively [31]. ...
Background: Strengthening health institutions increases the productivity of health spending. Institutions like hospitals which use a large proportion of the health budget are natural targets for productivity improvements, which need to be tracked over time to enable corrective action. This paper measure changes in technical and scale efficiency of hospitals in Uganda, and evaluates changes in productivity over a five year period in order to analyze changes in efficiency and technology.
... The use of ROE as a tool for financial measurement can be well found in much of the research, showing bank performance (Lindblom and VonKoch, 2002). Kirikal et al. (2004) studied Estonian banking performance using the Malmquist productivity index and DuPont analysis. They studied the banks' performance monitoring, analysis and control needs in respect to their operation, productivity and performance results from the viewpoint of different audiences, like investors/owners, regulators, customers/clients, and management themselves. ...
... Sturm and Williams (2004) studied Australian banking industry performance between 1988 and 2001. Kirikal et al. (2004) examined the performance of the banking industry in Estonia between 1994 and 2002. Primorac and Troskot (2005) There are numerous studies that use the Malmquist total factor productivity index to examine Turkey's banking sector in different years. ...
The objective of this study is to make a detailed analysis of the total factor productivity of the banks operating in the Turkish banking industry from 2003 to 2014. We used the panel data of 42 banks and applied the Malmquist Total Factor Productivity Index method separately for each year. We then calculated the pure efficiency change, scale efficiency change, technical efficiency change, technological change and total factor productivity change indices. We found that between 2003 and 2014, there was a 5.2% increase in the total factor productivity of banks on average, and this increase was the result of a 2.5% increase in technical efficiency change index and a 2.7% increase in the technological change index.
DOI: 10.5901/mjss.2015.v6n5p148
... EFFCH is greater than one, equal to or less than one if a hospital is moving closer to, unchanging or diverging from the production frontier. TECH is greater than, equal to or less than one when the technological best practice is improving, unchanged, or deteriorating, respectively [33]. [34]. ...
Background: The Botswana national health policy states that the Ministry of Health shall from time to time review and revise its organisation and management structures to respond to new developments and challenges in order to achieve and sustain a high level of efficiency in the provision of health care. Even though the government clearly views assuring efficiency in the health sector as one of its leadership and governance responsibilities, to date no study has been undertaken to measure the technical efficiency of hospitals which consume the majority of health sector resources. The specific objectives of this study were to quantify the technical and scale efficiency of hospitals in Botswana and to evaluate changes in productivity over a three year period in order to analyse changes in efficiency and technology use. Methods: The DEAP software was used to analyse technical efficiency along with the DEA based Malmquist productivity index which was applied to a sample of 21 non teaching hospitals in the Republic of Botswana over a period of three years (2006 to 2008). Results: The analysis revealed that 16 (76.2%), 16 (76.2%) and 13 (61.9%) of the 21 hospitals were run inefficiently in 2006, 2007, and 2008 with average variable returns to scale (VRS) technical efficiency scores of 70.4 per cent, 74.2 per cent and 76.3 per cent respectively. On average, the Malmquist total factor productivity (MTFP) decreased by 1.5 per cent. Whilst hospital efficiency increased by 3.1 per cent, technical change (innovation) regressed by 4.5 per cent. Efficiency change was thus attributed to an improvement in pure efficiency of 4.2 per cent and a decline in scale efficiency of 1 per cent. The MTFP change was highest in 2008 (MTFP=1.008) and lowest in 2007 (MTFP=0.963). Conclusions: The results indicate significant inefficiencies within the sample for the years under study. In 2008, taken together, the inefficient hospitals would have had to increase the number of outpatient visits by 117,627 (18%) and inpatient days by 49,415 (13%) in order to reach full efficiency. Alternatively, inefficiencies could have been reduced by transferring 264 clinical staff and 39 beds to health clinics, health posts, and mobile posts. The transfer of excess clinical staff to those facilities which are closest to the communities may also contribute to accelerating progress towards the Millennium Development Goals related to child and maternal health. Nine (57.1%) of the 21 hospitals experienced MTFP deterioration during the three years. We found the sources of inefficiencies to be either adverse change in pure efficiency, scale efficiency, and/or technical efficiency. In line with the report Health financing: A strategy for the African Region, which was adopted by the fifty sixth WHO Regional Committee for Africa, it might be helpful for Botswana to consider institutionalising efficiency monitoring of health facilities within health management information systems.
... Murdick et al. (1990) stated that productivity measures the amount of outputs, which are the services, provided or the results of service provision, against the inputs, which are the resources consumed in the provision of services. Kirikal (2004) stated that profitability is based on net income or the total revenue minus all expenses. Typically, in banking, Return on Assets (ROA) and Return on Equity (ROE) are profitability indicators in addition to the profit itself. ...
A P e e r R e v i e w e d I n t e r n a t i o n a l J o u r n a l o f A s i a n R e s e a r c h C o n s o r t i u m ABSTRACT Currently, with the growing demands of profitability and efficiency, especially with the emergence of the new information technology (IT), it is essential to know the impact of new technologies on bank's performances. Consequently, an empirical analysis was conducted from a panel data of two public banks for the period 1998-2009 to examine impact of IT investments on profitability and productivity of Indian public sector banks. For this purpose, the study used two statistical tools in terms of correlation and regression analysis. The results indicates that investments on IT contributed to increase amount of deposits and return on assets (ROA) as profitability , profit per employees as productivity indicator and decrease the Net NPA ratio and staff cost. Finally, the study showed that public banks tried to adopt cost reduction and assets quality strategies to compete in the Indian bank market.