ANALYZING FINANCIAL SERVICES INDUSTRY USING DATA ENVELOPMENT
                                ANALYSIS

              Rashm...
PREVIOUS STUDIES

Previous studies illustrate the use of data envelopment analysis to evaluate the performance of the
bank...
can be measured in different units. The DEA approach does not require specification of any functional
relationship between...
Figure 1 illustrates a decision support system using data envelopment analysis. The decision support
system uses the DEA m...
remaining six are below the average level. The seven 100% efficient companies turned out to be the
best practices companie...
Corporation, General Electric, Capital One, JP Morgan Chase, UBS, and Wells are Pareto-efficient as
the DEA model has been...
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2121 ANALYZING FINANCIAL SERVICES INDUSTRY USING DATA ...

  1. 1. ANALYZING FINANCIAL SERVICES INDUSTRY USING DATA ENVELOPMENT ANALYSIS Rashmi Malhotra, St. Joseph’s University, (610) 660-3497, rmalhotr@sju.edu D.K. Malhotra, Philadelphia University, (215) 951-2813, MalhotraD@philau.edu C. Andrew Lafond, Philadelphia University, (215) 951-2950, LafondA@philau.edu ABSTRACT The ongoing credit crisis in the mortgage market has led to tremendous turmoil in the financial services industry. As a result, during the last six months, we have seen a substantial decline in the profitability and liquidity of the financial services companies. In this paper, we analyze the financial statements of financial services firms to evaluate their relative performance in the industry. We illustrate the use of data envelopment analysis, an operations research technique, to analyze financial statements of financial services firms by benchmarking financial ratios of a firm against its peers. Data envelopment analysis clearly brings out the firms that are operating more efficiently in comparison to other firms in the industry, and points out the areas in which poorly performing firms need to improve. INTRODUCTION Financial services industry is undergoing turmoil in recent months due to the sub-prime mortgage loan crisis. In fact, several leading firms in the industry are reporting significant decline in their net incomes due to losses in the sub-prime mortgage loan markets, resulting in steep drops in their stock prices. In this paper, we analyze the financial statements of the leading firms in the financial services industry by benchmarking them against one another to find out which of the firms have been hit hard in comparison to other firms in the industry. Financial statement analysis usually begins with a set of financial ratios. Ratio analysis begins with the calculation of a set of financial ratios from the information posted in the financial statements (income statement, balance sheet, statement of cash flows). Ratio analysis is a commonly used analytical tool for verifying the performance of a firm. While ratios are easy to compute, which in part explains their wide appeal, their interpretation is problematic, especially when two or more ratios provide conflicting signals. Therefore, ratio analysis is often criticized on the grounds of subjectivity, because an analyst must pick and choose ratios in order to assess the overall performance of a firm. In this paper, we illustrate the use of data envelopment analysis, an operations research technique, to analyze financial statements of financial services firms by benchmarking financial ratios of a firm against its peers to understand the relative performance of a firm. Data envelopment analysis clearly brings out the firms that are operating more efficiently in comparison to other firms in the industry. Data envelopment analysis also points out the areas in which poorly performing firms need to improve. The rest of the paper is organized along the following lines. In section II, we provide a review of previous studies on financial statement analysis. Section III discusses the model that we use in this study. Section IV discusses the data and methodology used in this study. In section V, we provide an empirical analysis of our results. Section VI summarizes and concludes our study. 2121
  2. 2. PREVIOUS STUDIES Previous studies illustrate the use of data envelopment analysis to evaluate the performance of the banking industry. Halkos and Salamouis (2004) explore the efficiency of Greek banks with the use of a number of suggested financial efficiency ratios for the time period 1997-1999. They show that data envelopment analysis can be used as either an alternative or complement to ratio analysis for the evaluation of an organization's performance. The study finds that the higher the size of total assets the higher the efficiency. Neal (2004) investigates X-efficiency and productivity change in Australian banking between 1995 and 1999 using Data Envelopment Analysis (DEA) and Malmquist productivity indexes. It differs from earlier studies by examining efficiency by bank type, and finds that regional banks are less efficient than other bank types. The study concludes that diseconomies of scale set in very early, and hence are not a sufficient basis on which to allow mergers between large banks to proceed. Paradi and Schaffnit (2004) evaluate the performance of the commercial branches of a large Canadian bank using data envelopment analysis. Chen, Sun, and Peng (2005) study the efficiency and productivity growth of commercial banks in Taiwan before and after financial holding corporations' establishment. They employ a Data Envelopment Analysis (DEA) approach to generate efficiency indices as well as Malmquist productivity growth indices for each bank. Howland and Rowse (2006) assess the efficiency of branches of a major Canadian bank by benchmarking them against the DEA model of American bank branch efficiency. Sufian (2007) uses DEA approach to evaluate trends in the efficiency of the Singapore banking sector. The paper uses DEA approach to distinguish between technical, pure technical and scale efficiencies. Sanjeev (2007) evaluates the efficiency of the public sector banks operating in India for a period of five years (1997-2001) using the Data Envelopment Analysis (DEA). The study also investigates if there is any relationship between the efficiency and size of the banks. The results of the study suggest that no conclusive relationship can be established between the efficiency and size of the banks. Lin, Shu, and Hsiao (2007) study the relative efficiency of management in the Taiwenese banking system through Data Envelopment Analysis (DEA). The goal is to estimate the competitiveness of each bank and managerial efficiency is to show the efficiency variation of each bank through Malmquist index. Bergendahl and Lindblom (2008) develop principles for an evaluation of the efficiency of a savings bank using data envelopment analysis (DEA) as a method to consider the service orientation of savings banks. They determine the number of Swedish savings banks being "service efficient" as well as the average degree of service efficiency in this industry. As illustrated above, there is no study that specifically deals with the financial services industry and investment banks. Therefore, this study extends previous literature by providing financial statement analysis of the financial services industry at a point in time when the industry is going through much turmoil. METHODOLOGY WHAT IS DATA ENVELOPMENT ANALYSIS? Data envelopment analysis1 is a technique used to assess the productive efficiency of homogenous operating units such as schools, hospitals, banks, or utility companies. It is a powerful technique for measuring performance because of its objectivity and ability to handle multiple inputs and outputs that 2122
  3. 3. can be measured in different units. The DEA approach does not require specification of any functional relationship between inputs and outputs or a priori specification of weights of inputs and outputs. DEA provides gross efficiency scores based on the effect of controllable and uncontrollable factors. DEA uses a number of financial ratios to determine how good a company’s performance has been. A firm’s performance is analyzed on the basis of a set of financial ratios that include liquidity ratios (current ratio, quick/acid test ratio), asset management ratios (inventory turnover ratio, asset turnover ratio), debt management ratios (leverage ratio, total debt to total assets, and times interest covered ratio), and profitability ratios (Return on Equity, Return on Assets, and net profit margin). The ratios that need to be maximized serve as outputs and ratios that need to be minimized serve as inputs. Using this information, approach does not require specification of any functional relationship between inputs and outputs or a priori specification of weights of inputs and outputs. DEA provides gross efficiency scores based on the effect of controllable and uncontrollable factors. DEA uses a number of variables to determine how good a firm is. With these financial ratios as inputs, the DEA-based decision support system calculates an efficiency score for a firm. This score is a relative value computed by comparing the given firm to a pool of well-performing companies that serve as a benchmark for the company under evaluation. Each firm is evaluated against either an existing firm or a hypothetical firm with an identical set of inputs or outputs that is constructed as a combination of good performing companies. By using the existing good companies as a “role model,” DEA not only helps differentiate well performing (efficient companies from poorly performing (inefficient) firms, but also brings out the reasons why a company may be underperforming. This helps investors and creditors justify their decisions to invest or not to invest their funds in a particular company. This will also help management identify areas of weakness for a firm so that management plans can focus on plugging the weaknesses or taking steps to counter the weaknesses. DATA AND METHODOLOGY We used the financial statement data available for the last quarter of 2007 from Hoovers Online for this study. We used ten financial ratios to evaluate thirteen companies from the financial services industry. Thirteen financial services firms that we include in our study are: Citibank, American Express, Bank of New York Mellon Corporation, J.P. Morgan Chase, Morgan Stanley, Lehman Brothers, UBS, Goldman Sachs, Capital One, GE, Bear Sterns, Wells Fargo, and Merrill Lynch. Hoovers Online These financial services companies have been identified as competitors by Hoovers Online. The variables are the days of sales outstanding, leverage ratio, total debt/equity ratio, long-term debt per share, cash flow per share, net profit margin, return on equity, return on assets, asset turnover, and interest rate coverage. Table 2 illustrates the pooled data of the thirteen companies used for analysis. In our study, the comparative evaluation among the companies is an important consideration. Therefore, we select the envelopment models for our analysis. In addition, the outputs are an outcome of managerial goals. Therefore, input-based formulation is recommended for our study. The objective of the analysis is to suggest a benchmark for the financial services firms. Furthermore, to investigate the affect of scale of operations, if any, among the 13 companies, we consider both variable returns to scale and constant returns to scale DEA models. Also, the structure of the DEA model (in envelopment form) uses an equation and separate calculation for every input and output. Therefore, all the input and output variables can be used simultaneously and measured in their own units. In this study, we use the Input- Oriented Variables Return to Scale (VRS) to evaluate the efficiency of thirteen financial services company for the year 2007. 2123
  4. 4. Figure 1 illustrates a decision support system using data envelopment analysis. The decision support system uses the DEA methodology to assess the performance of each company. The DEA-based decision support system uses the company attributes – days of sales outstanding, leverage ratio, total debt/equity ratio, long-term debt per share as input variables. The system uses the cash flow per share, net profit margin, return on equity, return on assets, asset turnover, and interest rate coverage as output variables to calculate an efficiency score for a firm. This score is a relative value computed by comparing the given firm to a pool of well-performing companies that serve as a benchmark for the company under evaluation. Each firm is evaluated against the existing firms with an identical set of inputs or outputs that is constructed as a combination of performing and non performing companies. EMPIRICAL ANALYSIS Each of the financial services companies is a homogenous unit, because they have been identified as the major competitors by Hoovers Online. Therefore, we can apply the DEA methodology to assess a comparative performance of these companies. Using the DEA methodology, we can calculate an efficiency score for the thirteen companies on a scale of 1 to 100. We analyze and compute the efficiency of these companies using the financial statements for the year end 2007. As the DEA methodology does not work with negative values, we first transform the variables by adding the highest negative value to each observation. For example, cash flow per share is negative for many companies, and highest for Goldman Sachs. Thus, we add this value for all the companies to get positive values. Table 3 illustrates the efficiency scores for thirteen companies. Further, we also study the peers (model companies) for inefficient companies. Table 3 shows the relative performance of the financial services companies benchmarked against each other. Table 3 also shows that seven out of thirteen companies were ranked as efficient in 2007, and six companies were inefficient companies. American Express, Bank of New York Mellon Corporation, General Electric, Capital One, JP Morgan Chase, UBS, and Wells Fargo are 100% efficient. On the other hand, Citibank, Bear Sterns, Goldman Sachs, Lehman Brothers, Merrill Lynch, and Morgan Stanley are inefficient. Figure 2 shows the efficiency frontier graph of the pooled company data. The 100% efficient companies (blue dots) are on the efficiency frontier, where as the inefficient companies (red dots) are inside the efficiency frontier. The DEA Analyzer calculates the level of inefficiency by measuring the distance between the efficiency frontier and the inefficient companies. Therefore, a financial analyst can use this efficiency frontier to assess the relative efficiency of the firm in the industry. The DEA model compares the days of sales outstanding, leverage ratio, total debt/equity ratio, long-term debt per share, cash flow per share, net profit margin, return on equity, return on assets, asset turnover, and interest rate coverage. We present the score in percentage value varying between 0% and 100%. We find that the input efficiency of American Express, Bank of New York Mellon Corporation, General Electric, Capital One, JP Morgan Chase, UBS, and Wells Fargo is 100%. On the other hand, the input efficiency of the remaining companies is: Bear Sterns (29%), Morgan Stanley (32%), Lehman Brothers (44%), Goldman Sachs (52%), Merrill Lynch (53%), and Citibank (75%). This means that the observed levels of cash flow per share, gross profit margin, net profit margin, return on equity, return on assets, asset turnover, and interest rate for Bear Sterns can be achieved with 29% of the current levels of days of sales outstanding, leverage ratio, total debt/equity ratio, long-term debt per share. The same rationale applies to Citibank, Goldman Sachs, Lehman Brothers, Merrill Lynch, and Morgan Stanley. Table 4 illustrates the efficiency scores and the corresponding ranking of the pooled companies in the year 2007. The average score is 76%, with seven companies having efficiency levels above average, while the 2124
  5. 5. remaining six are below the average level. The seven 100% efficient companies turned out to be the best practices companies within the pooled database of the Decision Support System. The best practices companies: American Express, Bank of New York Mellon Corporation, General Electric, Capital One, JP Morgan Chase, UBS, and Wells Fargo are 100% efficient. As Citibank, Bear Sterns, Goldman Sachs, Lehman Brothers, Merrill Lynch, and Morgan Stanley are inefficient; the next step is to identify the efficient peer group or companies whose operating practices can serve as a benchmark to improve the performance of these companies. Table 5 illustrates the peer group for the inefficient companies. As shown in the Table 5, JP Morgan Chase, Wells Fargo, GE, and Bank of New York Mellon Corporation serve as peer for Citibank. In addition, Citibank is more comparable to JP Morgan Chase (weight 64%) and less comparable to it’s more distant peer Wells Fargo (29%), GE (6%), and Bank of New York Mellon Corporation (1%). Thus, Citibank should scale up its cash flow per share, net profit margin, return on equity, return on assets, asset turnover, and interest rate coverage relative to JP Morgan Chase. Similarly, Bear Sterns has Capital One (82%) as the closest peer that it should emulate and Bank of New York Mellon Corporation (18%) as the distant peer company that can also be investigated. Similarly, Goldman Sachs has American Express, Bank of New York Mellon Corporation, and Wells Fargo as its peers. Lehman Brothers has Wells Fargo, GE and Bank of New York Mellon Corporation as its peers. Merrill Lynch is 53% efficient and has Capital One as its immediate peer, and Bank of New York Mellon Corporation as its distant peer. Similarly, Morgan Stanley has Capital One, Wells Fargo, JP Morgan Chase, and Bank of New York Mellon Corporation as its immediate peers in decreasing order. Finally, Capital One serves as the closest peer for three of the inefficient companies. Similarly, Bank of New York Mellon Corporation, Wells Fargo, and JP Morgan Chase serve as the most immediate or immediate peer for most of the inefficient companies. On the other hand, American Express and UBS are also immediate and distant peers for one of the inefficient companies respectively. Therefore, Capital One and Bank of New York Mellon Corporation are the most efficient company among the given pool of the companies in the DSS, as not only are Capital One and Bank of New York Mellon Corporation 100% efficient, but they also serve as the role model for all other companies. Similarly, Wells Fargo is the next most efficient company among the group of companies. Wells Fargo serves as the immediate peer for Lehman Brothers and next immediate or farther immediate peer for Citibank, Goldman Sachs, and Morgan Stanley. Finally, American Express and JP Morgan Chase serve as the immediate peer for Goldman Sachs and Citibank respectively, and far-distant peer for Morgan Stanley. The results are quite expected as the pooled companies have similar characteristics. The efficient peer companies have a similar mix of input-output levels compared to that of the corresponding inefficient company, but at more absolute levels. The efficient companies generally have higher output levels relative to the company in question. The features of efficient peer companies make them very useful as role models that inefficient companies can emulate to improve their performance. Furthermore, Capital One is the immediate efficient peer for three companies, so its frequency of use as an efficient-peer, expressed as a percentage of the number of pareto-inefficient companies, is 50%. All other reference companies serve as an immediate peer for one company. Thus, we have enhanced confidence that Capital One is genuinely well-performing company as it outperforms all the other companies. Furthermore, these companies are more likely to be a better role model for less efficient companies to emulate as their operating practices and environment match the majority of the other companies quite closely. Table 6 displays the benchmarking factor and the hit percentage of efficient company. Table 7 illustrates the slack values identified in the next stage of the DEA analysis. The slack variables for 100% efficient companies are zero. Therefore, American Express, Bank of New York Mellon 2125
  6. 6. Corporation, General Electric, Capital One, JP Morgan Chase, UBS, and Wells are Pareto-efficient as the DEA model has been unable to identify some feasible production point which can improve on some other input or output level. On the other hand, for Citibank, besides decreasing the level of total debt / equity by 2.35, there is further scope for increasing interest rate coverage by 1.40 units. Citibank can follow JP Morgan Chase and Wells Fargo as its role model and emulate their policies. Similarly, Goldman Sachs can reduce its days of sales outstanding by 30 units and total debt/equity by 4.54 units, and increase cash flow per share by 308.6 units, while maintaining efficient levels equivalent to that of its peers—American Express and Wells Fargo. Similarly, we can find the slack factors for Bear Sterns, Lehman Brothers, Merrill Lynch, and Morgan Stanley. Table 7 illustrates the slack values of the relevant factors for inefficient companies. SUMMARY AND CONCLUSIONS Traditional financial statement analysis techniques use ratio analysis to compare a firm’s performance against its peers in the industry as well as against the company’s historical performance. On the basis of this comparison, analyst will recommend whether the company is doing well or underperforming relative to its peers or relative to its own past performance. DEA employs relative efficiency, a concept enabling comparison of companies with a pool of known efficient companies. The DEA model compares a firm with the pool of efficient companies by creating an efficiency frontier of good firms—a tolerance boundary created by establishing the efficiency of firms in terms of several sets of financial ratios. Companies lying beyond this boundary can improve one of the input values without worsening the others. We found that American Express, Bank of New York Mellon Corporation, General Electric, Capital One, JP Morgan Chase, UBS, and Wells Fargo are 100% efficient. On the other hand Citibank, Bear Sterns, Goldman Sachs, Lehman Brothers, Merrill Lynch, and Morgan Stanley are inefficient. We also illustrate the areas in which inefficient companies are lagging behind efficient firms. We also provide an insight into the benefits of DEA methodology in analyzing financial statements of firms. The decision support system stores the company’s historical data, competitive firm’s data, and other industry specific data, and uses the DEA methodology to analyze a firm’s performance. Moreover, DEA modeling does not require prescription of the functional forms between inputs and outputs. DEA uses techniques such as mathematical programming that can handle a large number of variables and constraints. As DEA does not impose a limit on the number of input and output variables to be used in calculating the desired evaluation measures, it’s easier for loan officers to deal with complex problems and other considerations they are likely to confront. TABLES & REFERENCES Tables, references, and full paper available upon request from the authors. 2126

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