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Evaluating the profitability and relative efficiency of large banks: application of data envelopment analysis and panel estimation

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Evaluating the profitability and relative efficiency of large banks: application of data envelopment analysis and panel estimation
Answered Same Day Jul 03, 2021


Sumit answered on Sep 23 2021
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The generally study of banking secto
Modern banking business has radically changed over the last decade. The most often
mentioned reasons of substantial changes in the banking industry are: 1) Globalization led to
decline of ba
iers to entry in the banking industry; 2) Development of the information
technologies; 3) Tightening of the legislation rules; 4) Changes in the consumer needs and
preferences (Vaithilingam et al. 2006; Greuning, Bratanovic 2009). As the result banks face an
increasing competitive pressure from the foreign financial institutions and non-banking
financial services providers. To survive in the competitive struggle and to achieve the
overwhelming goal of shareholder wealth maximization, banks are forced to extend the range
of traditional banking functions. Nowadays, banks are not only intermediaries and liquidity
providers, but they also act as information channels, risk managers, drivers of innovations, etc.
Due to the critically important role of financial institutions in the national economy bank
performance is a frequently discussed topic in an academic environment. In turn, performance
of banks is a wide concept concerning such issues, as competition, concentration, efficiency,
productivity, and profitability (Bikker, Bos 2008; Heffernan 2005). The wide range of
performance-related themes yielded a great diversity on the banking research agenda.
However, there is no consensus among researchers regarding the most appropriate method to
measure bank efficiency. Traditional single ratios, such as return on equity, do not provide
eliable results due to the complex operational environment of banks (Yang 2009). Applying
atio analysis, it is possible to examine “only a part of the organization’s activities” (Arshinova
2011). Efficiency measuring techniques based on the frontier approach allow to overcome this
problem by incorporation of multiple inputs and outputs.
The cu
ent paper highlights the issue of measuring efficiency performance of banks of the
largest banks in the world (List given below) using Two stage research process. In the first stage
Data Envelopment Analysis (DEA) is used to ascertain the scores of Bank Efficiency using CCR
Model. In the second stage the scores obtained through the DEA are used to link to bank
efficiency using regression techniques (Random Effect Model). The period for this evaluation is
7 years i.e. from the annual report of 2013 to 2019. The data of following banks were used
during this evaluation:
1. Deutsche Bank
2. Citigroup
3. Wells Fargo
4. Bank of America
6. JPMorgan Chase
7. Bank of China
8. Agriculture Bank of China
9. China Construction Bank Corporation
10. Industrial and Commercial Bank of China
1. Introduction
2. Literature Review
3. Methodology
4. Variables Used in Analysis
5. Results
6. Conclusion
7. References
1. Introduction
Financial crisis during 2007-2008:
The impact of 2007 financial crisis on the banking sector also
ings additional pressure on the profitability of major international banks. In 2007-2008, all the economy was te
ibly shook by the macroeconomic ensuing recession. The financial crisis of the year 2007-2008 was also meant by the Global Financial Crisis (GFC), which was considering as a severe worldwide financial crisis. In that phase, all the banks were kept going with huge risk taking factors that was combine with the subprime lending market in the united states in the downturn with the bankruptcy on an international banking (Beltran et al. 2019). In 2008, there were unprecedented bailout and different sort of stimulus in which the government were force to provide the necessary funds to the customers in order to make withdrawal their money.On the other side in order to avoid future collapsing and enhance lending, the faith into a commercial papers and treasury bills (Buyl et al. 2019).
Due to the financial crisis, the building up capital and liquidity buffers enhanced the resilience and banks globally substantially lead to future risks (D'Amato, 2019). The forward-looking basis meant for flowing the supply of credit in both the times whether it is good or bad that increased the stress levels of the particular supervisors and banks for providing the greater resilience(Heinemann, 2016).The evidences, which were based on the qualitative factor that indicates the banks have kept considerably, improved their risk management activities along with their internal practices. In context these are the changes or updates that considers as hard to resist and assess for improving the important scopes in order to the astonish uncertainties for the future evolution (Ho, 2018).
On the other side in the case of cooperative banks, they kept assessing the structural modification on the particular system for wide stability and theyare harder than the previous one for complex interactions (Huy, 2018).The public authorities and the reform process had a same objective was to fluctuate the number of changes pattern in the banks. In order to maintain the reform process the banks have to become more focused on the global level for their internal strategy that leads to intermediate their international locally claims (Kuppuswamy & Villalonga, 2016). There were a vast decline in the direct connections between the banks through the derivative as well as lending exposures and other than that, some European banks which considered as high capacity of assets indulge with the consolidation. A range of other types of reforms is that the effect of the business model diversity came from the reallocation ofseveral banks in context with commercial banks (Mei-ling& Zheng, 2019). By a more stable fund resources there were a shift that consider as a deposit and the rest further progress were depend on the recovery frameworks and resolution.
The certain sort of provision of bank that lending to the real economy is that the trends in the bank for intermediate credit purposes have uneven at any time across the countries over time that aims to reflecting the possibilities in their particular credit overhang and crisis experience (Nicol, 2018).The credit declining activity is important for the particular economic activity than could burnt or remove of various sort of crisis and despite of that most of the countries continues to focus on financial growth in order to provide the loans to their customer.
Apart from the regular banking practices, the international banking was one of the foremost areas that were affected very much by the crisis (Olson & Zoubi, 2017). The aggregate foreign banks that claim to show an important decline from the crisis that was driven by the banks particularly. On the other side the advanced economies also came into picture and show the report of those banks whoever are very much affected by the particular crisis and according to the list the from some part of the Europeancountries that was affected. In order to tackle the problem, there were a certain sort of highlights for the post crisis banking sector hat explain how the banks were able to came on the track after facing so manydifferent types of financial issues.
They also have resumed the supply of the services that was based on intermediation to the economy. In the supply of local credit the bank credit growth was always on top of the list that remains below as far as excessive crisis is concerned. In the advance economies there were a indications on a particular system that provide lending to the certain sort of bo
owers as well as sectors that had seen the variations of deductions and deductions. In the flow of credit,the banks have framed their risk profile for the policymakers.The benefits of the non-performing loans (NPL’S) are beneficial for addressing the problems that are associated with the crisis countries.
According to the IMF, which stated that the financial crisis of 2007 was, consider as a wildfire for the finance and banking sector mainly in America & Europe. It displays the pattern of taking risk excessively and with shortage of capital and the liquidity buffers also and the standards within the company (Ramcharan et al. 2016). The explicit resolution frameworks have responded to the liquidity standards and demand capital in order to increase the stronger supervision. It reflecting the prolonged period of private sector in that the operating scenario of the commercial banks has also transform markedly that delivers the weak growth of the economy, and kept provide low interest rates that was accompanied by the shifts of globalization trend in the real economy. The trends that was familiar on most of the banks and financial institution the all based on ongoing and kept varying. Besides that the scrutiny of stakeholders is consider as very intense because it emerged the new technology with non-new bank and facing various challengers to the business of banks in order to add the competitive pressure.
In order to give responses, there were several banks that a
ange and respond about the post crisis and pre crisis for the operating environment. Globally the banks have been planned to readjust or redesign their business strategies that comprises of several sort of balance sheet options, how to enhance their growth plans, various types of organizational structures that meant to be beneficial for the banks and keen a eye on the scope of different types of activities and their geographic presence (Ruman & Bajaj, 2019). The adjustments that applicable on the business and have affected very few aspects that includes governance practices and risk management practices.
The Global financial system (CGFS) is a committee that worked on the post crisis ofbanking sector over the past decade.It established as a working committee that can examine the upcoming trends in the business models for banks, inspect the implications of banking markets and efficient the stability in same and focused on the performance and market structure.In addition to the individual investors or institutions, on the post crisis, changed the overall efficiency and performance of the banking and financial field for focusing on the system wide developments for the individual institution. In support of growth and services in the real economy, the efficiency of the banking sector meant for lending the capital market services and intermediation services to the economy.
The regulation and supervision of the bans and other financial institution reforms to the regulatory framework for strengthen the corporate on the global level that also increased the adverse requirements for the proper liquidity and high quality framework. In order to enablethe implicit subsidies of public and effective resolution of banks, the financial stability of the individual banks that encompasses the system and their wide perspective of risks that enhancing the reforms to their next level.Besides that, there are certain sorts of elements that are beneficial for bank regulatory changes (Santu et al. 2017). The stricter risk weighted is helpful to meet the requirements of quantity and quality of capital. In order to maintain or measure the constrain leverage and reducing the model risk for a non-weighted ratio that the authorities could launch the counter cycler capital that buffers all the banks in a particular jurisdiction that helpful for to build over the credit cycle and also help to reduce the procyclicality.
The other measures for measuring the globally and systematically standards for the various banks that are cu
ently focus in a top thirty banks of the universe that representing more than one fourth of the total banking assets. It comprises of large number of assets and capital structure along with the higher capita and additional formality for the disclosure of accounts as well as for the large exposures. The domestic economy has introduced the rules and regulation that is very strict for the tougher capital and the other rules for deemed banks that was systematically significant for meeting the stress testing requirements.
Meanwhile, another factor seems to become as a powerful factor for reducing the aggregate demand across globally. Due to the rise in the share of the production in the given output, many activities have taken place. The activities are at the level of subsistence decides the amount of wages that the workers have get the efficiency of the banking sector meant for lending the capital market services and intermediation services to the economy. Since the traditional way that stated lowering the interest rates for the workers was not beneficial and to address the lack of total spending. The government cannot spend the rest amount from their end. In this condition, the cost of house were grew solidly and the main spot of various examiners, banks and other GFC in the European countries and other US countries for improved their purchasing power and
oaden their capital markets. In the past months, there were a reminiscent turmoil for changing the prices with pace and magnitude.
On the other side, the international prudential regulation of the liquidity risk introduced along with the centerpiece of two qualitative requirements. One is the liquidity coverage ratio (LCR) and the other one is Net stable Funding ratio (NFSR). The motive of these two activities was to promote the short-termresilience of the financial institution and banks that provide the liquidity, which focuses to ensure that banks could easily manage the finding as well as capital structure for their assets and liabilities and also off balance sheet activities. The recovery and resolution of banks is consider as a global framework that particular body is responsible for allocating the funds and reserves sufficient for abso
ing the total loss capacity. It includes the proportion of debt in the event of losses and many countries are restructured their national resolution schedule that allow the up gradation to the orderly resolution of the particular banks.
In many banking firms across Europe and America that the post crisis tenure has seen a important change in the dynamic orientation (Sheikh & Qureshi, 2017). As there were certain sort of banks who redesign their venture for the trading activities purposes as well as dependence on the wholesale funding in association with the liquidity regulation and stricter capital purposes. The global banking sectors were fully consistent for the bank business models that emphasizing on
oad noticeable changes that considered as fully consistent.
In conclusion, the more important changes in the
oad assets framework that have worked on the several portfolios and underlying shifts that was present within the bans asset portfolio. The most observed post crisis that increases the market share of liquid assets holdings that comprises of Australia, many European countries, some part of the United Kingdom and United States. In other cases, the government increased the debt holding pattern that has increased substantially. On the other side many bank cut the share of inte
ank assets for the purpose of the particular share that was available for issue to the particular banks that leads to liquidity purposes.
Data Envelopment Analysis (DEA): The banking sector plays a crucial socio-economic role at the regional, national and international levels. Banks are at the heart of financial systems in that they act as financial intermediaries; to be more specific, they bo
ow money by accepting deposits and issuing debt securities, and lend money both directly to their customers and indirectly through capital markets by investing in debt securities. Banks play an important role in money supply and the efficient allocation of financial resources in an economy. Banks make profits in exchange for their services including risk management. Nowadays, banks have a diversified portfolio of activities that range from personal, corporate and investment banking to trading of cu
ency, commodities, and financial securities on stock markets. Because of the crucial importance of banking systems to the economy and the financial risks they face, banks are required to comply with both national and international regulations, and their performance is constantly monitored by both regulatory bodies and investors. In fact, poor performance often leads to distress which might lead to bankruptcy under some circumstances along with substantial financial, economic and social undesirable consequences.
In this paper, we assess the efficiency profiles of Top 10 commercial banks of the world. Since we have taken Top 10 Commercial Banks the overall banking system is relatively big compared to the banking systems of rest of the world. Its size is the result of a combination of factors including its history, as these banks has been a financial centre since the eighteenth century. As a financial hub, these banks system offers the benefits of clustering such as higher productivity and wage. With the spread of Globalization and its openness to trade and capital flow seem to provide attractive incentives and flexibility for foreign banks to do business all over the world and for domestic banks to do business a
In this paper, we propose a revised methodological framework; namely, Data Envelopment Analysis (DEA) with a regression-based feedback mechanism along with new DEA models (i.e., DEA models without explicit inputs or outputs), and use it to assess the efficiency profiles of top 10 commercial banks of the world. The proposed methodology is useful for variable selection especially when the lack of discrimination is a concern. It is used to address three research questions: (1) how do DEA analyses with and without a linear regression-based feedback mechanism compare? (2) how effective is a linear regression-based feedback mechanism in improving discrimination in DEA? and (3) when a feedback mechanism is used to inform the researcher or analyst about the relevance of the choices of inputs and outputs in a DEA analysis, how do radial model (CCR) works? From a practical perspective, we are questioning whether the efficiency determinants identified in previous studies (i.e., inputs and outputs in DEA analysis under the intermediation approach) are actually (empirically) contributing to efficiency or not and whether methodological choices (e.g., choice of DEA model to use, choice of metrics or proxies of performance criteria) have something to do with it. For the sake of completeness and update of analyses, we also address two conventional research questions: (4) are Top 10 commercial banks of the world are managed efficiently? and (5) what are the drivers of efficiency for the Top 10 Commercial Banks of the world?
Regression Analysis: DEA, as a non-parametric approach, is often criticized for drawing the statistical inference and undergone several improvements (Seiford, 1996). A two-stage regression was used primarily to treat the obtained efficiency values on a set of explanatory variables with the help of linear regression to overcome this. An immediate improvement has been made, considering the efficiency scores obtained in the first stage are censored, which led to the use of limited dependent variable models over OLS (Casu & Molyneux, 2003). Another issue arises while using the two-step procedure, which takes no account of the underlying Data-Generating Process (DGP). In this process, the variables used to obtain the efficiency scores in the first stage will be co
elated with the explanatory variables used in the second stage, resulting in inconsistency and biases (Simar, Lovell, & Vanden Eeckaut, 1994). The basic DEA model explains the environmental variables i.e., Factors affecting the efficiency of a DMU. These factors are not traditional inputs and are not under the control of management. First, DEA is used to estimate the efficiencies. In the second stage, the efficiency estimates are to be regressed against a set of environmental variables using the Tobit model because it can account for truncated data (Idris, Siwar, & Talib, 2013). In this context, the sign of the resulted coefficients of the environmental factors show the direction of the influence, and standard statistical hypothesis testing is utilized to examine the strength of the relationship (Casu & Molyneux, 2003).
The DEA generated efficiency values are dependent on each other statistically because the score generated is a relative index, not an absolute index (Xue & Harker, 1999). In response, Efron (1979) pioneered a powerful statistical tool- the bootstrap method, and subsequently, Simar and Wilson (2007) proposed an advanced version. The bootstrapping procedure improves statistical efficiency and draws valid inference by simulating the sample distribution and taking the data generating process (DGP) into account. In this approach, we presume that the DGP generates the original sample of data, and by simulating DGP, draw a new pseudo set of data. This repeating process gives rise to an empirical distribution of bootstrapped data following a Monte Carlo approximation to help draw new inferences. In the second stage, we use both a censored truncated normal Tobit regression with maximum likelihood method and bootstrapping truncated regression analysis. We regress the Average Profit Efficiency (APE) obtained from the first stage on a set of variables using the following regression model.
2. Literature Review
Data Envelopment Analysis (DEA): McFadden developed the theory of profit function and its relation to the production function of competitive firms in 1966 (Mullineaux, 1978). Further, this concept was extended to non-competitive firms (Lau, Fuss, and McFadden, 1978). Afterward, the functional form of profit function was studied (Diewert, 1973). Researchers also used profit function in the measurement of the economic efficiency of Indian agricultural firms (Lau & Yotopoulos, 1971).
Unlike cost function, profit function covers both output (revenue) as well as input (cost) efficiency (Berger 1992, Berger et al., 1993a). More issues are involved in the output than input efficiency assessment, as output inefficiencies are more extensive than that of input (Berger, Hunter, & Timme, 1993b; English, Grosskopf, Hayes, & Yaisawarng, 1993). Profit frontier can be described for a set of observations, assuming none is located above, while for cost efficiency, no observation should be below the frontier. The production function for any firm is related to the firm’s ability to attain the maximum level of outputs for a given set of inputs or utilizing a minimum level of inputs for a given set of outputs (Pastor, Perez, & Quesada, 1997). Profit frontier can be linked with the maximum level of profits, calculated by a given set of input and output prices.
Studies that applied profit function using Stochastic approach include Aigner, Lovell, and Schmidt (1977), Aigner and Chu (1968), Akhigbe and Mcnulty (2003, 2011), Akhigbe and Stevenson (2010), Ariff and Can (2008), Berger et al. (1993a), Deyoung and Hasan (1998), Isik (2008), Kumbhakar (1987), Olson and Zoubi (2011), Luo, Tanna, and De Vita (2016). The e
or term in efficiency measurement in the stochastic approach does not represent the exactness but an estimate of mean inefficiency over the sample (Førsund, Lovell, & Schmidt, 1980). Berger and Humphery (1997) recommend the deterministic frontier approach to measure the profit efficiency using DEA. Deviation from the efficient frontier is assumed as inefficiency and can be estimated from the model. Suppose there is a firm, producing Y units of output by consuming X1 and X2 units of inputs. The frontiers of this bank can be drawn as depicted in Figure 2. The dotted lines show the probabilistic position of the stochastic frontier (as their position cannot be precisely determined), and the solid curve shows the exact position of the deterministic frontier. If bank A is inefficient i.e., it is not on the frontier, then as per deterministic view, its deviation is measured as inefficiency (AB), while as per stochastic, it is a combination of inefficiency and e
or term. Moreover, one cannot predict the exact position of e
or and inefficiency term on the deviation line of bank A from the stochastic frontier. In Figure 1, differences between stochastic and DEA can be observed in terms of their inefficiency measurement.
Charnes, Cooper, and Rhodes (1979) pioneered the use of DEA, followed by Sherman and Gold (1985) in the USA, among others and Luther Committee (1977) in India for the Banking Sector. DEA is employed for efficiency estimate in developed countries (Aly, Grabowski, Pasurka, & Rangan, 1990; Grabowski, Rangan, & Rezvanian, 1994; Miller & Noulas, 1996; Rangan, Grabowski, Aly, & Pasurka, 1988, Chu and Lim, 1998), as well emerging economies (Bhattacharyya et al., 1997; Das et al., 2005; Leightner & Lovell, 1998; Saha & Ravisankar, 2000; Sathye, 2003).
The estimated efficiency in the first stage utilizing the DEA is regressed through the Tobit model in the second stage to find its factors (Begum, Alam, Buysse, Frija, & van Huylen
oeck, 2012; Galanopoulos, Aggelopoulos, Kamenidou, & Mattas, 2006; Khoshroo, Mulwa, Emrouznejad, & Arabi, 2013). Balcombe, Fraser, Latruffe, Rahman, and Smith (2008) and Alexander, Haug, and Jaforullah (2010) used a two-stage analysis by using DEA to measure efficiency and later adopted a bootstrap procedure to examine the effects of environmental and managerial factors on bank efficiency.
Regression Analysis: Sharifi and Akhter (2016) considered the credit deposit ratio as a barometer of progress of a financial institution like commercial banks. According to them, it indicates the level credit deployment of banks in relation to deposits mobilized by them. A high credit deposit ratio indicates that banks are generating more credit from its deposits and vice-versa. Further, they say that the outcome of this ratio reflects the ability of the bank to make optimal use of the available resources. They ca
ied out a study with a purpose to present the performance of public sector banks through the credit-deposit ratio based on the secondary data collected from 26 public sector banks for a 7-year period (2008-2015). The data were analyzed using a descriptive statistics and panel data regression model. Their findings and analysis reveal that the CDR impact positively on public sector bank's financial performance.
Jilkova and Stranska (2017) analyzed the effect of the economic situation of the Czech Republic on the performance and profitability of the banking market through selected determinants in their study. They have focused on measuring the performance and profitability of the banking sector using the method of “Multiple linear regression model”. They not only studied the overall fitness of model but also determined which independent variables have the greatest and the smallest effect on the dispersion of the dependent variables. Their paper clarifies the structure of the Czech banking sector and it is focused on the performance and profitability in the defined time period and also compares with the selected banking sector and indicators in other countries.
Pandya (2015) analyzed the impact of priority sector advances of scheduled commercial banks operating in India on their profitability. Author, considered all the scheduled commercial banks operating in India for this purpose. Ratios of Priority sector advances to total advances (PSATA) of all commercial banks during the study period taken as an independent variable whereas, Return on Assets (ROA), Return on Investment (ROI), Return on Equity (ROE), Ratio of Operating Profit to Total Assets, (OPTA) and Ratio of Interest Income to Total Assets (INTTA) were taken as dependent variables. Linear regression models were used to examine the relationship between independent and dependent variables. The study reveals that there exists a statistically significant relationship between PSATA and ROI, ROA, OPTA, INTTA. The results thus imply that priority sector advances have bearing on bank profitability. Further, the study reveals that priority sector advances affect ROA and ROI of the banks. Therefore, author suggests the banks should exercise caution while advancing loans to priority sector else it would be adversely affecting the profitability of the banks.
Narwal and Pathneja (2015) discussed the different determinants of productivity and profitability of banks functioning in India. They have studied the performance of public and private sector banks in terms of productivity and profitability in two different time periods (2003-2004 to 2008-2009 and 2009-2010 to 2013-2014). Regression analysis was applied to discover the
determinants of different bank groups. The results of the study disclose that private sector banks are more productive than public sector banks over the whole study period and also observed no significant difference in the profitability of two bank groups. The author’s reason of more productivity of private sector banks is the better utilization of technology than the public sector banks.
Adam (2014) conducted the study to investigate financial performance of E
il Bank for
Investment and Finance, Kurdistan Region of Iraq during the period of has
used statistical tool for analysis purpose of several variables which would affect the banking
system in general in order to know whether these variables are significantly co
elated with the
financial performance for the bank. The findings of the study show the positive behavior of the
financial position for E
il Bank and some of their financial factors variables influence the
financial performance for the bank. Author also noticed that the overall financial performance
of E
il Bank is improving in terms of liquidity ratios, assets quality ratios or credit performance,
profitability ratios (NPM, ROA and ROE). Further, the study suggests a set of recommendations
egarding the development and enhancing of some banking operations which will boost the
ank's profitability and improve the financial performance for the bank.
Sarokolaei (2012) conducted the research to forecast the performance of 10 Iranian banks using
multi-linear regression method and artificial neural network and compared these two methods.
They have collected the financial data related to 10 Iranian banks for the four years from the
most reliable sources. The regression method has been used to find the relationship between
the calculated efficiency of ROA (Return on Average Assets) and the independent variables. The
findings of multi-linear regression method showed a positive relationship between efficiency
and the 3 independent variables of size, cost to income ratio and inflation rate. They also used
ROA as an output for the network in the artificial neural network and 7 different inputs were
used to identify the pattern according to the predictive variables finally, the performances of
these two methods were measured by using MSPR (Means Square Predicted E
or). The results
of the research showed that the amount of MSPR in multi-linear regression method is much
lower than the MSPR amount in artificial...

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