Shareholder value and the clash in performance measurement: are banks special ?
Franco Fiordelisi University of Wales Bangor, Centre for Banking and Finance, United Kingdom Università di Roma Tor Vergata, Dipartimento di Studi Economico-Finanziari e Metodi Quantitativi, Italy
e-mail:
[email protected]
Abstract This paper analyses the information content of traditional and innovative performance indicators in the light of creating SHV within the banking industry. There is a growing number of studies examining which performance measure is the most compatible with SHV maximisation, but the evidence surrounding this issue is mixed. In addition, few papers deal with this issue in the banking industry context. This study examines both relative- and incremental-information content focussing on the Italian banking industry. The investigation technique follows Biddle et al., (1997), with a few departures to better tailor the analysis to the peculiarities of a bank. Our results suggest that the superiority of EVA is not verified in term of relative information content, but there is confirming evidence when considering the incremental contribution provided by its components. One feature of our findings is that they are sensitive to the proper accounting of bank’s peculiar features: as these distinctive characteristics are ignored when calculating EVA, results change and there is little evidence to support the EVA’s superiority.
JEL classification: M141; G14 Keywords: Value-relevance, banking, information content, performance measures, Economic Value Added (EVA), valuation
The author wishes to acknowledge the helpful comments provided by Daniele Previati of the Università di Roma Tre, Alessandro Carretta, Umberto Filotto, and Giuseppe Galloppo of the Università di Roma Tor Vergata, Phil Molyneux of the University of Wales, Bangor and David Marqués-Ibañez of the European Central Bank. The author is also grateful to Gianluca Mattarocci for the assistance provided in collecting data.
1. Introduction Managing to create a sustained and sustainable Shareholder Value (SHV) is currently recognised by academics and practitioners as the most important objective for European banking. A survey1 carried out in 1999 among European banks, for example, reported that “half of the respondents say that they communicate an explicit objective for shareholder value creation, and more than 70% find that the investor community expects banks to set up shareholder value goals. The shareholder value concept has, according to many respondents, received the right degree of attention in the business press, but some find that this is not the case as regards the governing of companies”. Shareholder-value maximisation has also been recognised as a reasonable goal by regulators: Greenspan (1996) affirms “you may well wonder why a regulator is the first speaker at a conference in which a major theme is maximising shareholder value…regulators share with you the same objective of a strong and profitable bank system”2. One might ask why since the 1990s there is such a strong interest toward SHV among practitioners, academics and, even, regulators. The primary reason of this increasing interest of European banks toward the creation of SHV is that the banking market has evolved becoming more competitive: this new scenario requires a new approach to keep both stakeholders and shareholders satisfied. In detail, three macroeconomic factors3 contributed to make SHV creation a primary target in banking. These are: •
Deregulation and Re-Regulation. The banking industry has traditionally been one of the most heavily regulated industries, with considerable barriers to competition, to assure the stability of this fundamental economic sector4. However, the banking literature consistently emphasises: 1) the need for financial intermediaries to be allocationally and operationally efficient to achieve economic development and stability; 2) these objectives are not coherent with structural regulations, which assure stability but provide unnecessary protection for inefficient banks. Following these criticisms, during the 1990s, most countries have re-shaped the financial industry regulation: the “structural regulations” (and so competitive barriers) have been removed and new “prudential regulations” have been introduced in this newly competitive sector. This kind of regulation aims to guarantee stability to intermediaries in a competitive banking market, by containing the level of risk underwritten. This approach should allow several benefits to be achieved at the same time: competitiveness, financial industry stability, welfare gains related to the allocative efficiency, consumer (investors and savers) protection and the fairness of the bank’s behaviour. In this new framework raising market competition has become a concern for regulators (along with stability and efficiency), and as they succeed in their pursuit equity capital becomes available only by supplying a satisfactory remuneration to shareholders;
•
Privatisation. During the 1990s, many European Governments (such as the Italian) have reduced their equity participation in banks by selling to private investors their holdings. Governments often used their banking holding to pursue social goals (such as the creation of new jobs and a prudent management of families’ savings), in addition to the goals traditionally pursued by private investors. Lacking the pressure for profits, and a system to monitor manager
1
See Dalborg (1999), who reports the results of a questionnaire submitted to the group of “Institut International d’Etudes Bancaire” (IIEB) banks in February 1999 2 Remarks of Alan Greenspan at the annual convention of American Bankers Association, Honolulu, Hawaii, October 5th , 1996 3 Several macro-economic factors have been identified in literature (see, for example, Resti 1999 and Schuster 2000). The list proposed here does not aim to identify all factors, but contains the primary determinants for the increasing importance of shareholder value. 4 For further details on the reason of the banking regulation or with the different methods of banking regulations, see please Gardner (1997), Gualandri (1996 ), Vesala (1993).
1
performance, banks have been often inefficiently managed. After the privatisation, these two factors (i.e. social goals and inefficient management) disappeared making SHV a primary target for banks; •
Mergers and Acquisitions. The ability to create SHV is a necessary condition to raise company value, and to play an active role in the banking sector’s consolidation. In addition, when mergers or acquisitions take place executives and managers are judged according to their results (in terms of shareholder value).
While there is a huge number of contributions sustaining the SHV approach5, there is often a substantial confusion on how create value for shareholders6 and, especially, on how SHV should be measured. The identification of the best measure for defining SHV has become critical. Indeed, the concept of SHV is one of the oldest nostrum in business7: a company creates value when the return on invested capital is greater than its opportunity cost, or than the rate that investors could earn by investing in other securities with the same risk. While this is straightforward, it is debated what is the best method for assessing the value created by firms for their owners, as researchers and practitioners grapple with different performance metrics. As noted in the Economist (1997, p. 61), “inevitably the measures are also a big business for consultants. Stern Stewart, the New York firm that developed EVA, is the leader of the pack. But in recent years it has faced the competition from the Boston Consulting Group (BCG), Braxton Associates, McKinsey and others. Many consultancies produce league tables of value added and go to increasingly absurd lengths to protect their particular brand”8. At first sight, the problem may be considered trivial since the solution is implicit in the concept of shareholder: if a company creates value when the returns on its capital is greater than the opportunity cost, SHV can be simply assessed by comparing the overall investor return (i.e. capital gain and dividend) for the period with the rate of return expected in the same period. This indicator is usually defined as Market Adjusted Return (MARm)9 since the shareholder market return is expressed net of the expected return. Although this is straightforward, the problem is not completely solved since: • •
The Market Adjusted Return (as well as the overall shareholder return) is available only for publicly traded banks. Since the number of listed banks is still small in Continental European Countries, there is a strong need to find which performance indicators best capture SHV creation; The Market Adjusted Return provides an accurate ex-post assessment of the bank’s ability to create SHV, but it has a limited use as an ex-ante assessment, since future stock prices are unknown. Since it is easier to produce forecasts for traditional- (e.g. ROE, ROA, Net income) and innovative(e.g. EVA) performance measures than it is for stock prices, it is important to find which performance measure better captures the creation of SHV: its forecast can supply a reliable signal about the bank’s ability of create SHV.
5
See, for example, Rappaport (1896) and (1998), Black et al (1998), Uyemura et al. (1996), Stewart (1991), Resti (1999), Schuster (2000) 6 This puzzlement is well described by Resti (1999, p. 19), who notes “SHV in banking is becoming a sort of mot de passe, of a skeleton key for making all to agree, of a spice to give flavour even to non-tasting projects and find the favour of all tablecompanions. Yet, despite of unavoidable banalizations that accompany any formulas of success, the creation of SHV is both urgent and possible”. 7 See Hamilton (1777) and Marshall (1890) 8 Without aiming to be exhaustive, we report some of these measures and its proponents: EVA (Stern Stewart), CFROI (Holt), Total Business Return (Boston Consulting Group), Economic Profit (McKinsey), SVA (LEK/Alcar). 9 In other studies, such as Biddle et al., (1997), Market Adjusted Return is computed as a firm’s 12-month compounded stock return less the 12-month compounded value weighted market wide return.
2
•
The analysis of the information content of different performance measures supplies useful inputs to the normative policy debate of what performance measures should be considered in financial regulation: this point is particularly interesting, for example, with regards to the third pillar (Market discipline) of the New Basle Capital Accord
Most of the studies dealing with SHV have investigated the superiority of the innovative performance measures (especially, EVA which is the most popular) over the traditional measures (i.e. ROE, ROA, Net Income, etc). A growing number of studies investigate which performance measure is the most compatible with SHV maximisation. The evidence surrounding this issue is mixed, and these studies can be divided in two groups: those carried out by consultants and those carried out by academics. As stated in Lehen and Makhija (1997, p. 90), “EVA is seen by its proponents as providing the most reliable year-to-year indicator of a market-based performance measure known as Market Value Added … Despite the wide interest in EVA, little is known empirically about the efficacy of this measure versus other measures of performance… The evidence from these studies is mixed, however, and has not resolved the debate over performance measures”. In addition, as far as we are aware, few papers investigated this issue focussing on the banking industry. This paper aims to assess the information contents of innovative (namely, Residual Income and EVA) and traditional accounting figures (Net Income, ROE, ROA, Interest- and intermediation-margins) with the goal of creating shareholder value focussing on the banking industry. The paper is organised as follows. Section 2 proposes a literature review of the most relevant studies. Section 3 presents the methods employed to calculate EVA and to assess the information contents of the performance measures. Section 4 describes the data and section 5 summarises the empirical results. Section 6 outlines the peculiarities of a bank and investigates the sensitivity of our results. Section 7 presents the conclusions.
2. A quick glance at results in empirical studies Most of the studies dealing with SHV have investigated the information contents of the innovative performance measures (especially EVA, the most popular) over the traditional measures (i.e. ROE, ROA, Net Income, etc). In other words, there is a growing number of studies investigating which performance measure is the most suitable to maximise SHV. The evidence surrounding this issue is mixed and these studies can be divided in two groups: those carried out by EVA promoters and those carried out by academics. As stated in Lehen and Makhija (1997, p. 90), “EVA is seen by its proponents as providing the most reliable year-to-year indicator of a market-based performance measure known as Market Value Added … Despite wide interest in EVA, little is known empirically about the efficacy of this measure versus other measures of performance… The evidence from these studies is mixed, however, and has not be resolved the debate over performance measures”. Regarding the practitioner literature, these studies usually observed the EVA superiority since EVA is found to better explain stock returns and firm values. As noted in Garvey and Milbourn (2000, p. 211), “Stern Stewart, Boston Consulting Group, and LEK/Alcar make the claim that their proprietary performance measure correlates more closely with stock returns than do either traditional
3
accounting measures or the measures of rival firms, allegedly making it a more desirable compensation tool”. In detail10: •
O’Byrne (1996)11 analyses industrial companies and found the EVA superiority in a two-step analysis. In the first, the firm market value was regressed on EVA and then on earnings (namely, NOPAT): O’Byrne (1996) found an adj R2 for EVA of 0.31 and of 0.33 for the NOPAT. In the second step of the analysis, a set of adjustments were proposed: firstly, EVA separate coefficients were allowed for positive and negative value of EVA; secondly, the natural log of capital was introduced as predictor in order to take into account differences in the way the market value firm of different sizes; thirdly, 57 dummies variables were introduced to consider potential industry effects. In this second stage, O’Byrne (1996) found an R2 for EVA of 0.56, which enable him to conclude that EVA is superior to earnings in explaining firm value.
•
Al Ehrbar (1998)12 reports that several empirical analyses have been carried out by Stern Stewart using the Performance 1000 database. According to the Stewart findings, EVA explains half of the volatility in companies’ MVA, the highest correlation found.
•
Uyemura et al., (1996)13, a particularly interesting study for our purposes since it focuses on banking, analysed the largest 100 U.S. bank holding companies over a period of ten years (198695). By regressing changes in standardised MVA against changes in standardised EVA (defined as EVA divided by capital) and traditional performance measures, EVA was found to have the highest correlation with MVA (table 1).
Table 1 Uyemura et al., (1996) results Variable
Alpha
Beta
R2
Standard Error
EVA
186 (26)
3.40 (0.14)
40%
757
ROA
-435 (59)
62,018 (5,429)
13%
912
ROE
-309 (56)
3,581 (367)
10%
928
Net Income
19 (35)
0.75 (0.09)
8%
938
EPS
-179 (45)
76 (11)
6%
950
Source: Uyemura et al., (1996, p.99)
10
The focus is on three study made by the EVA proponents Stephen F. O’Byrne is vice president of the Stern Stewart & Company 12 Al Ehrbar is senior vice president of the Stern Stewart & Company 13 Dennis Uyemura, Charles Kantor and Justin Pettit are senior vice presidents of the Stern Stewart & Company 11
4
Focussing on studies proposed by academics, the superiority of EVA is usually not verified. In detail: •
Peterson and Peterson (1996) analysed traditional and value-added measures of performance and compared them with stock returns. According to their findings, traditional measures are not empirically less related to stock returns than return on capital: as result, traditional measures should be not eliminated as a means of evaluating performance, though these have no theoretical appeal. From this point of view, Peterson and Peterson (1996) rule out the possibility of value added measures not being worthwhile: since value added measures focus on economic rather than accounting profit, these play an important role in evaluating performance because managers will aim towards value creation rather than mere manipulation of short-sighted accounting figures.
•
Biddle et al., (1997 and 1999) analysed a sample of 6174 firm-years over the period 1984-93 by comparing adjusted R2 obtained regressing stock market adjusted returns against EVA, Residual Income (RI), accounting earnings (namely, Earning Before Extarordinary Item - EBEI) and Operating Cash Flow (CFO). According to their results, EBEI has the highest adjusted R2 and EVA has a smaller adjusted R2: these results do not support the hypothesis that EVA dominates traditional performance measure in its association with stock market returns. In addition, Biddle et al., (1997 and 1999) also assessed the relationship between performance measures and firm value by replicating O’Byrne’s (1996) study with some adjustments. In order to level the playing field, Biddle et al., (1999) extended the adjustment proposed in the second stage of O’Byrne’s (1996) analysis to the regressions run against NOPAT: in this case, the EVA superiority disappears. In fact, according to their results, accounting earnings have the highest adjusted R2 (0.53), EVA has an adjusted R2 of 0.50 and NOPAT has an adjusted R2 of 0.49. These results suggest that EVA does not dominate accounting earnings in explaining firm values.
•
Lehen and Makhija (1997) assess which performance measure does the best job of predicting the turnover of Chief Executive Officer (CEO). Focussing on the degree of correlation between different performance measures and stock market returns, Lehen and Makhija (1997) found that correlation coefficients vary from 0.39 and 0.76. In detail, EVA and MVA are the most highly correlated measure with stock market returns: 0.59 and 0.58 (respectively). The other performance measures have smaller correlations: 0.455 for ROA, 0.455 ROE and ROS 0.388. It is interesting to note that, similarly from all other studies where MVA was used as response variable, the measure mostly correlated with MVA is EVA.
•
Garvey and Milbourn (2000) assessed the “declared” EVA superiority by focussing on the suitability of EVA and earning to the management compensation system. This paper adds to the academic literature a different strain, since Garvey and Milbourn (2000) initially criticise the investigation techniques used previously (i.e. the statistical correlation with stock returns and/or firm value). Garvey and Milbourn (2000) suggest that a strong statistical correlation with stock returns does not establish (a priori) that a performance measure adds value to a compensation system. In order to define the criteria for judging the value alternative performance measures, Garvey and Milbourn (2000) proposed: o A theoretical analysis developing a standard agency model with a principal and one agent14: Garvey and Milbourn (2000) concluded that it is irrelevant to investigate whether EVA beats
14
Within this model, the agent is encouraged to decide an unobservable action. The outcome from this action exhibit itself both directly through two accounting metrics (i.e. EVA and earnings)and indirectly via stock price. The model focuses on
5
earning per se, while it would be more accurate investigate under what circumstances EVA beat earnings (and for what reasons); o An empirical investigation by testing the model in Paul (1992) to verify the theoretical model. In detail, Garvey and Milbourn (2000) analysed the marginal value of EVA adoption for each company (using the estimated correlations among earning, EVA and prices) and associate this to the firm’s EVA adoption (using a multivariate regression approach). Garvey and Milbourn (2000, p. 241) found that the “accounting measures continue to explain changes in compensation even when stock returns are used as explanatory variable. This is consistent with the Paul (1992) model in that firms do not use exactly the same weights as the stock market in determining compensation … More surprisingly, we show that the apparently simplistic idea of comparing the relative ability of alternative measures to explain stock returns is both theoretically defensible and a reasonable representation of practice. Therefore, firms contemplating the adoption of EVA would be well advised to begin with an examination of EVA’s R2 with its stock returns”. •
Acheampong Y.J., Wetzstein M.E. (2001) propose an innovative type of analysis using parametric methods for estimating efficiency, focussing on the food industry. It is interesting to note that Acheampong. and Wetzstein (2001, p. 7) conclude that: “the analysis showed that there are no significant differences between traditional and value added measures of performance”.
On the basis of the studies above summarised, it appears that: 1. All studies carried out by practitioners found that EVA dominates traditional measures in explaining stock returns and firm values; 2. Studies carried out by academics found that traditional measures are not empirically less related to stock returns than EVA and other value added measures; 3. Garvey and Milbourn (2000) proposed a theoretical and empirical approach substantially different from all other studies. Although this contribution is very interesting, it appears to have a different focus because it compares EVA and earning as a basis for compensation systems rather than performance measures. In addition, it concludes that companies contemplating the adoption of EVA should (first of all) assess the EVA’s R2 with its stock returns; 4. Although these studies adopted quite similar investigation techniques, the variables adopted (as predictors, but especially as response variables) are heterogeneous. Some studies [such as O’Byrne (1996), Peterson and Peterson (1996) and Biddle et al., (1997 and 1999)] attempt to evaluate different performance measures, including accounting earnings and residual income measures such as EVA, by examining their degree of correlation with stock returns on the ground that the best measure is the most highly correlated with stock returns. Some other studies [Al Ehrbar (1998) and Uyemura et al, (1996)] and compared financial measures looking at the degree of correlation with the MVA, considered by EVA promoters the “ultimate measure of shareholder wealth creation”15. The selection of the predictor and response variable is important, particularly for the response variable, as it is outlined in section 3.2. the optimal weights to apply on these accounting and price metrics in order to efficiently motivate the agent. Garvey and Milbourn (2000) adopted the linear-exponential-normal (LEN) formulation. 15 E.g. see Uyemura et al, (1996, p. 96)
6
3. Methods This section deals with the methods used in the empirical investigation. It is organised in three parts outlining: 1) The procedure for calculating the Economic Value Added (section 3.1): among the innovative measures developed by consultants, EVA was preferred since it is the most well-known and it can be calculated by external analysts; 2) The (innovative and traditional) performance measures investigated (section 3.3); 3) The methodology for assessing the information content of SHV measures (section 3.3).
3.1 Economic Value Added (EVA) for commercial banks SHV is measured in this empirical investigation using the Economic Value Added (EVA) method. EVA expresses the surplus value created by a company in a given period, i.e. the firm’s profit net of the cost of all capital. This measure is computed as the product of the difference between the return on investment and its composite financing cost (i.e. excess return) and the capital invested (model 1).
(1)
EVA = Capital Invested * (Return on Capital Invested– Cost of Capital) = = (Capital Invested * Return on Capital Invested) – (Capital Invested * Cost of Capital) = = NOPAT – (Capital Invested * Cost of Capital)
As noted in Velez-Pareja (2000), when EVA is used to assess company performance in a given period, capital invested and NOPAT should not be calculated in the same period. As investors expect to receive returns on the investment made at the beginning (and not on the cumulative amount at the end of the period), shareholders compare returns (i.e. NOPAT) earned over the period with the capital invested at the beginning (and not at the end) of the period. For this reason, capital invested is measured with a lag of one year and EVA is calculated as follows: EVAt = NOPATt – (Capital Investedt-1 * Cost of Capital) (2)
Where: EVAt = EVA of period t NOPATt = NOPAT of period t Capital Investedt-1 = Capital Invested measured at the end of period t-1
In order to calculate EVA, there are three basic inputs: a) Net Operating Profit After Tax (NOPAT); b) Capital invested; c) Cost of capital invested.
7
NOPAT and capital invested cannot be calculated on an accounting basis, but need to be calculated on an economic basis. Advocates of EVA have identified more than 160 accounting adjustments, but it is unrealistic even to think of making all these adjustments for any single company. In the empirical investigation, we calculate a “disclosed EVA”16, which is EVA obtained making some standard adjustments to publicly available accounting data. The calculation of EVA requires, in fact, to express NOPAT and capital invested on an economic basis: for this reason, advocates of EVA17 suggest some adjustments in order to: • Avoid mixing operating and financing decisions; • Provide a long term perspective; • Avoid mixing flow and stock; • Convert GAAP accrual items to a cash-flow basis or, in other cases, convert GAAP cash-flow items to additions to capital. In calculating EVA, seven adjustments have been carried out concerning the following items18: •
Loan loss provision and Loan loss reserve. Loan loss reserve is a reserve aiming to cover any future loan losses: for this reason, it should be equal to the net present value of all future loan losses. In any single period, this reserve is reduced by net charge-off (i.e. the current period losses due to credit risk) and replenished by loan loss provisions (i.e. the provision made in the current period to adjust the reserve both for pre-existing loans and for estimated future loan losses related to newly originated loans). This convention is certainly commendable from a management perspective since it implies that all loan losses are pre-funded out of current earnings. However, loan losses provisions are commonly used to manage earnings: if a bank achieves high operating returns, bank managers tend to overestimate this provision, while they are inclined to underestimate it if operating earnings are poor. This accounting practice introduces an important distortion in analysing bank performance since it smoothes earnings. Business is risky, and the volatility of profits is a manifestation of this risk: for purposes of economic performance evaluation, smoothing earnings is inappropriate.
•
Taxes. Most banks show significant and persistent differences between book tax provisions and cash tax payments. Since these differences are quasi-permanent, deferred taxes should be considered as capital and, similarly to loan loss provisions, taxes need to be considered as current period expenses for purposes of economic performance evaluation.
16
Al Ehrbar (1998) recognises that there may be several EVA numbers according to the number of accounting adjustments. As results, it is possible to identify a spectrum of EVA values: • the “basic EVA” is obtained using unadjusted GAAP operating profit and GAAP balance sheet capital.; • the “disclosed EVA” is obtained making some standard adjustments to publicly available accounting data. This measure, which improves the basic EVA by solving the main GAAP problems, is usually adopted by external analysts; • The “true EVA” can be calculated using “all” internal data that reflect the true economic condition of the company; • The “tailored EVA” is obtained using specific internal information (e.g. organisation structure, business mix, strategies, accounting mix) to adjust accounting figures. Internal analysts use a part of all internal data that balances the trade-off between simplicity and precision. 17 See for example, Stewart (1991), Uyemura et al. (1996), Rappaport (1998), Al Ehrbar (1998), 18 The fist five adjustments are specific for commercial banks (the first four have been originally suggested by Uyemura et al. 1996), while the remaining two are standard for any kind of company
8
•
Restructuring charges. Over the last decade, many banks have carried out restructuring plans in order to improve their operating efficiency. To the extent that such restructuring charges represents disinvestments, these costs should be treated as a capital reduction rather than costs (and therefore reduce NOPAT). Since data available do not allow us to evaluate the extent of real disinvestments due to restructuring charges, these costs are omitted when adjusting NOPAT and capital invested.
•
Security accounting. In many countries (such us U.S., Italy, France and U.K), “available for sale securities” (AFSS) are marked to market through the capital accounts. From an economic perspective, however, one might claim that selling a security, with a coupon below (or above) the current market yield, and using the proceeds to replace it with a current market yield security is a zero sum game. In evaluating the economic performance of a bank, it is therefore more accurate to remove from NOPAT the effect due to gains and losses on sale of AFSS: these gains and losses should be amortised against NOPAT over the remaining lives of the securities. However, since data on the remaining lives of the securities are not available and a reasonable assumptions cannot be made, these costs are omitted adjusting NOPAT: capital gains and losses generated marking to market AFSS (rather than past capital gains and losses amortised in the period t) are therefore considered as a part of NOPAT.
•
General risk reserve. This adjustment aims to correct the distortions deriving by the “general risk reserve”, a standard feature for Italian banks. This provision is a reserve aiming to cover a bank’s future generic loan-loss: in any single period, this reserve is reduced by net charge-off (i.e. the current period losses) and replenished by general risk provisions (i.e. the provision made in the current period to adjust the reserve according to the bank’s risks). Similarly to the loan loss reserve, this convention is certainly commendable from a management prospective, but it is used in an opportunistic manner. This accounting practice introduces an important distortion in analysing banks’ performance since it smoothes earnings.
•
Research and Development (R&D) costs and training costs, i.e. expenses designed to generate future growth. Current assets do not benefit from these expenses and it would be incorrect to reduce operating income by the amount of these expenses. However, GAAP requires companies to treat all outlays for R&D as operating expenses in the income statement. As a consequence, it is appropriate to correct this accounting distortion by considering operating income without these expenses.
•
Operating lease expenses are disguised financial expenses.
Before going on with the EVA calculation, it is necessary to precisely define how capital invested and cost of capital should be measured for commercial banks. Many studies (e.g. Velez-Pareja 2000) measure book value of capital using total assets and, therefore, measure the cost of invested capital as Weighted Average Cost of Capital (WACC). While this solution is certainly accurate for nonbanking companies, this procedure would be misleading for commercial banks. Since financial intermediation is the core business for banks, debts cannot be simply considered as a financing source (as for other companies), since they really are productive inputs (as the workforce, IT assets, etc). This view is also confirmed when analysing NOPAT’s significance. In non-banking companies, interest costs are not considered in NOPAT because these are not operating costs, but financial expenses. However, a bank’s operating costs mainly derive from interest expenses because financial
9
intermediation is the bank’s core business. As a consequence, if the capital charge is calculated applying WACC on total assets (as usually done for non-banking companies), EVA will be biased since it will be obtained counting twice the charge on debt: 1) Subtracting from NOPAT a capital charge on the overall capital (equity and debt) invested in the bank; 2) Calculating NOPAT, when interest expenses (i.e. the charge on debt capital) are subtracted from operating revenues. For these reasons, our advice is to focus on equity capital and measure the capital invested in the bank as the book value of shareholder equity. Regarding the cost of capital, the capital charge cannot be obtained applying the bank’s WACC on the capital invested because the latter is given by the equity capital and not by the overall capital (debt and equity). Consequently, a commercial bank’s cost of capital invested should be measured by the cost of equity19. To support this view, Sironi (1999) identifies four differences (labelled as “the separation principle”, “banks as providers of liquidity services”, “capital ratios”, “off-balance sheet pro”) between a bank’s cost of capital and that of a non financial company, and observes “with a capital structure exogenously determined by regulators, a marginal cost of debt close to that obtainable from the interbank market, and relatively similar to that of all other major banks, and an array of products that do not need any debt financing, banks should look at their cost of equity capital as a key variable”20. The cost of equity is estimated using the Capital Asset Pricing Model (CAPM) looking at investors’ expected return21.
3.2 Traditional and innovative performance measures This section deals with the performance measures considered in the empirical investigation. As seen in section 2, the variables adopted as predictor and response variables in previous studies are heterogeneous. Some studies [such as O’Byrne (1996), Peterson and Peterson (1996) and Biddle et al., (1997 and 1999)] attempt to evaluate different performance measures, including accounting earnings and residual income measures such as EVA, by examining their correlation with stock returns on the ground that the best measure is the most highly correlated with stock returns. Other studies [e.g. Al Ehrbar (1998) and Uyemura et al, (1996)] compared EVA with ROE; ROA and EPS looking at the degree of correlation with the MVA.
19
This point is also supported by Uyemura et al (1996, p. 102) and Di Antonio (2002, p. 103) Sironi (1999, p.6) 21 In this framework, there are three inputs for estimating the cost of equity: 1. Risk Free Rate. Following a standard procedure, this has estimated taking the rate of return of a short term Government Bond (i.e. Buono Ordinario del Tesoro 1 year death). 2. Market Portfolio Return; We followed the modified historical approach proposed by Damodaran (1998) 3. Beta has been estimated using daily data on annual basis by regressing the bank’s share returns against the market returns (measured looking at Mibtel) These regression Betas have been successively adjusted following the Bloomberg procedure. 20
10
Regarding the selection of the response variable, the adoption of MVA or market return is relevant to test the “information content” of traditional and innovative performance measures. In fact: •
MVA is defined as the current market value of all capital elements minus the historical amount of capital invested in the company, this can be also seen as the net present value of all future market returns22. In this case, the regression models assess the information content of a performance measure by looking at its capability to explain the variation of the firm’s value, value added during the period considered (i.e. the NPV of future market returns) rather than the variation of current market return.
•
The market adjusted return expresses overall return, and it is obtained by adding a period’s capital gains and dividends, net of the cost opportunity. When market adjusted return is used as response variable (as in Biddle et al. 1997), the information content of traditional and innovative performance measures is obtained by comparing their capability to explain the variation of shareholder value over the period analysed (rather than the firm value added at the end of the period considered).
Although both MVA and market adjusted returns provide interesting indications about the information content of a bank’s performance measure, we prefer to use the market adjusted return: in our opinion, this measure more appropriately captures the meaning of shareholder value. Regarding the selection of predictor variables, an extensive approach is followed, as different kinds of performance indicators are considered: rates of return (such as ROE, ROA), unitary earnings (such as EPS) and different accounting configurations of income (such as EVA, Residual income, Net Income, Interest Margin, Intermediation margin). From another point of view, we consider two innovative measure (i.e. EVA and Residual Income23), four traditional accounting performance measures (i.e. ROE, ROA, Net Income, EPS) and two traditional income indicators for commercial banks (i.e. interest margin and intermediation margin). Regarding these performance measures, their information content is assessed in terms of:
• Relative information content, which is useful to select a single measure (the performance indicators analysed are considered mutually exclusive). In this case, all bank performance indicators are considered;
• Incremental information content, which aims to assess if a performance indicator adds
information to the data provided by another measure. In this case, the performance indicators considered are those composing EVA, as outlined in figure 1.
22
For further detail on the MVA’s economic meaning, see section 3.3.6. Residual income should not be considered as an innovative measure since it was recommended as an internal measure of business unit performance by Solomon (1965) and as an external performance measure by Anthony (1973) 23
11
Figure 1 The incremental information content: EVA and its components EVA = Int.M. + Net CFI + CIAcNOPAT
– CapChg + AccAdj
Intermediation Margin Book value of NOPAT Residual Income EVA Where: Int.M Net CFI CINopat Capital Charge AccAdjNOPAT
= Interest Margin = Net Commission and Fee Income = Costs and incomes necessary to arrive to the accounting NOPAT = Cost of Equity * Book value of Capital = Accounting adjustment made to NOPAT and Capital invested to calculate EVA (i.e. EVA – Residual Income)
3.3 Traditional vs. innovative performance measures Most of the studies dealing with SHV sought to find the performance measure with the strongest correlation with stock market returns. The evidence surrounding this issue is mixed and one may cast a doubt about their reliability, according to the degree of independence of the researchers. In order to investigate this issue, our empirical investigation follows the procedure proposed in Biddle et al. (1997). By assuming that equity markets are efficient in a semi-strong way and forward looking, we aim to assess the information contents of the performance measures considered (i.e. ROE, ROA, net income, interest and intermediation margin, residual income and EVA) by distinguishing between: • •
Relative information content, which is useful when selecting a single measure since the performance indicators analysed are considered mutually exclusive. Incremental information content, which aim to assess if a performance indicator adds data to that provided by another measure.
12
Regarding the relative information content, this is assessed looking at the statistical significance in the following two Ordinary-Least-Squares (OLS) regression models: (3)
D t = b 0 + b1
FE X t Def t -1
+ et
where: Dt is the abnormal return for the time t; FEXt is the unexpected realisation (or forecast error) for a given accounting measure X Def t-1 is a variable used to scale FEXt et is a random disturbance term
Differently from Biddle et al (1997), two different kinds of performance indicators are considered: rates of return (such as ROE, ROA) and different configurations of profit (such as EVA, residual income, interest margin, intermediation margin). Since the performance indicators have a different nature, it is opportune to scale these variables using an appropriate deflator (DF) to make them homogeneous with the response variable. The first group of variables (i.e. rates of return) have the same nature of the market-adjusted return: in this case, the deflator is a constant equal to 1. The second group (i.e. different configurations of profit) are scaled by the market value of the firm’s equity five months after the beginning of the fiscal year24. In order to represent the forecast error, we follow Biddle and Seow (1991), Biddle et al (1995) and Biddle et al (1997): the forecast error is expressed as the difference between the realised value of a performance measure and the market’s expectation: (4)
FE X t = X t - E(X t )
Where: Xt is the realised value of a performance measure for the time t; E(Xt) is the market’s expectation of a performance measure for the time t; 24 It is worthwhile to note that the model run to assess the relative information content of NOPAT provides also useful information about the information content of another well known performance indicator: Earning per Share (EPS). Since EPS are unitary earnings, this variable should be scaled by the share market price at the time t-1: by assuming that the number of shares does not change between t and t-1, the models to test EPS and net income are equivalent. This can be shown by applying model 7 to both measures:
D t = b'0 +b'1
Eps t Eps t -1 + b'2 + et = Pt -1 Pt -1
Net income t Net income t -1 No. of shares t No. of shares t -1 b'0 +b'1 + b'2 + et = Pt -1 Pt -1 b'0 +b'1
D t = b'0 +b'1 b'0 +b'1
Net income t Net income t -1 + b'2 +e No. of shares t * Pt -1 No. of shares t -1 * Pt -1 Net incomet Net incomet + b'2 + et = MVE t -1 MVE t -1 Net incomet Net incomet + b'2 + et No. of sharest -1 * Pt -1 No. of sharest -1 * Pt -1
13
The market expectation of a performance measure is assumed to follow a discrete linear stochastic process (in autoregressive form): (5)
E(X t ) = φ + ψ1X t -1 + ψ 2 X t -2 + ψ 3 X t -3 + ...
where: ϕ is a constant ψ are the autoregressive coefficients
Rearranging models 2 and 3, we obtain:
X t - (φ + ψ 1 X t -1 + ψ 2 X t - 2 + ψ 3 X t -3 + ...) Def t -1
D t = b 0 + b1 (6)
= b' 0 +b'1 Where: E( b'0 ) = b0 - b1 ϕ ;
+ et =
Xt X t -1 X t -2 X t -3 + b' 2 + b' 3 + b' 4 + ... + e t Def t -1 Def t -1 Def t -1 Def t -1 E( b'1 ) = b1
E( b' i ) = bi ψi-1 for i>1
;
Model 6 links extra-ordinary returns with lagged performance measures, encompassing a range of alternative specifications for market expectations such as random walk, ARIMA, constant stock price multiple and combined “levels of changes” specification. Although these two models allow one to consider any order of lag for information, we limit both models to one lag (following Biddle et al 1997) since it is reasonable to assume that abnormal returns should not be influenced by the value of performance measures older than one year. With one lag25, model 6 can be stated as:
(7)
D t = b' 0 +b'1
Xt X t -1 + b' 2 + et Def t -1 Def t -1
Following Biddle et al., (1997), this model is also run by allowing the regression coefficient to vary in response to positive and negative performances26. As noted in O’Byrne (1996), the information content of performance measure can vary according to their signs: for this reason, all performance measures tested are partitioned in positive and negative values27. The new models can be stated as:
(8)
D t = b'0 +b'1
X t , pos Def t -1
+ b'2
X
t , neg
Def t -1
+ b' 3
X t -1 , pos Def t -1
+ b'4
X
t -1 , neg
Def t -1
+ et
25
These models with one-lag are equivalent to the levels of changes specification proposed by Easton and Harris (1991) Hayn (1995), Burgstahler and Dichev (1997) and Collins et al (1997) found that loss firm have smaller earning response coefficients than do profitable firms. 27 This model is inappropriate for those performance measures having only positive values. 26
14
The statistical test proposed to assess the relative information contents (in both model 7 and 8) is that applied in Biddle et al. (1995) 28: we run 28 pairwise comparisons of regressions among the eight performance measures considered. In addition, the information content of each performance measure has been tested in comparison to all the other competing measures29 using the F-statistic.
In order to investigate the incremental information content, the following one-lag specification of model 9 is proposed, generalised to the performance measures composing EVA (detailed in figure 1) scaled for market value of the bank’s equity calculated five months after the beginning of the fiscal year.
D t = b' 0 +b'1 (9)
+ b' 6
CI Nopat t INTM t -1 NetCFI t NetCFI t -1 INTM + b' 2 + b' 3 + b' 4 + b' 5 + MVE t -1 MVE t -1 MVE t -1 MVE t -1 MVE t -1
CI Nopat MVE t -1
+ b' 7
CapChg t CapChg t -1 AccAdjt AccAdjt -1 + b' 8 + b' 9 + b' 8 + et MVE t -1 MVE t -1 MVE t -1 MVE t -1
The incremental information content is assessed using the t-tests on individual coefficients and Ftests of the joint null hypotheses: H0X: b1=b2 (or c1=c2 ; d1=d2 ; f1=f2) H0Y: b3=b4 (or c3=c4 ; d3=d4 ; f3=f4) …
The approach proposed slightly differs from those proposed in Biddle et al. (1997). The empirical analysis carried out considers: 1. Additional performance indicators specific for commercial banks (such interest margin and the intermediation margin);
28
The null hypothesis is that there is no difference in the ability if two competing sets of independent variables to explain variation in the dependent variable. 29 The null hypothesis of no difference in the ability of two competing sets of independent variable to explain variation in the dependent variable (i.e. no difference between pairwise comparison of adj R2, Biddle et al. 1995). The null hypothesis tested is that the information content of measure X1 is equal to that of X2Two-tailed p-values express the probability of rejecting a true null hypothesis, where the null hypothesis tested is that the information content of measure X1 is equal to that of X2 = X3 = X4 = …
15
2. A generalised deflator in the model used to test the relative information content of performance measures. Since all performance measures tested in Biddle et al (1997) have the same nature, these could be deflated using a single variable (namely, the market value of the firm’s equity). In our case, the analysis tests several performance indicators that differ in nature: as a result, different variables need to be considered to properly scale each performance measure. Namely, two deflators were applied: a. A constant equal to 1 for all predictor variables having the same nature as the response variables (i.e. ROE and ROA); b. The market value of the bank’s equity five months after the beginning of the fiscal year for all different configuration of profits (specifically, interest margin, intermediation margin, residual income and EVA); 3. Variables obtained using different procedures. In detail: • EVA data is not taken from the Stern Stewart 1000 database, but has been calculated following the procedure outlined in section 3.1; • Market adjusted return is calculated by dividing the actual overall investor market return (i.e. capital gain and dividend) by the rate of return expected in the same period. • The market value of the firm’s equity is calculated five months after the beginning of the fiscal year. Market Adjusted Returns are calculated considering a 12-month non-overlapping period ending five months after the firm’s fiscal year-end. In our opinion, a gap-period of five months is appropriate to assume that the information contained in the bank’s annual report is reflected in stock market prices.
4. Sample description Data are collected from different sources. In detail: • •
Financial statement information are collected by using Bilbank, a database managed by Associazione Bancaria Italiana (ABI). Data are taken from unconsolidated balance sheets; Market information are obtained from Datastream. In detail, equity market prices, risk free rates and equity indices (as it is indicated in section 4.3) are collected from this source, while Beta, Bond spread and equity spreads were calculated using Datastream’s data.
The sample selected includes almost all the banks listed in the Italian Stock Exchange over the period considered. In some cases, some information (such as Bank’s EPS and/or Market Value) were unavailable through the Datastream database and, consequently, these banks could not be included in the sample. Next, both the response and predictor variables were winsorised to ± 4 standard deviation from the median: in other words, data greater or smaller than 4 standard deviation from the median of the firm year observation are assigned a value equal to the median plus or minus (respectively) 4 standard deviations. As result, the sample is an unbalanced panel composed of 33 commercial banks and 109 firm-year observations over the period 1995-99. Table 2 contains some descriptive statistics for both dependent and independent variables adopted to test the relative information contents and the incremental information content.
16
Table 2 Descriptive statistics on dependent and independent variable+ A - The relative information content assessment Market Adjusted Return (MAR)
Mean
Median
Std Dev.
0.1590
0.0776
0.3305
Intermediation Margin (IM)*
1.3490
0.4910
2.9110
Interest Margin (IntM)*
1.0750
0.3690
2.4530
ROE
0.0593
0.0593
0.0831
ROA
0.0045
0.0046
0.0047
Net income*
0.0065
0.0052
0.9639
Residual income (RI)*
0.0740
0.0025
1.0363
EVA*
0.1340
0.0160
1.1250
Correlation
NI EVA IntM IM RI ROE ROA
MAR 0.225 0.283 0.104 0.094 0.306 0.173 0.189
NI
EVA
IntM
0.583 -0.162 -0.157 0.621 0.798 0.578
0.589 0.589 0.966 0.364 0.201
0.995 0.484 - 0.339 - 0.398
IM
RI
0.475 -0.335 - 0.399
0.407 0.249
ROE
0.894
B - The incremental information content assessment Mean
Median
Std Dev.
Market Adjusted Return (MAR)
0.1590
0.0776
0.3305
Interest Margin(IntM)*
1.0750
0.3690
2.4530
Net CFI*
0.2742
0.1250
0.5230
CI
NOPAT*
-0.998
-0.3600
2.1820
CapChg*
0.2767
0.1155
0.5665
AccAdj*
0.0597
0.0069
0.2962
Correlation INTM t NetCFI CINopat CapChg AccAdj
+ *
MAR 0.104 0.032 0.033 0.049 0.007
INTM 0.849 -0.878 0.849 0.545
NetCFI
-0.816 0.775 0.642
CINopat
CapChg
-0.648 -0.595
0.349
The sample has 109 firm year observations. All variables were winsorised to ± 4 standard deviation from the median. Deflated by the market value of equity calculated five months after the beginning of the fiscal year.
17
With regard to the relative information content assessment (panel A), all performance measures considered have a positive firm-year mean and median: interest- and intermediation- margins have the highest firm-year mean and median among all scaled performance measures. EVA and Residual income have mean and median close to zero, which is consistent with companies operating in a competitive market (where it is difficult to earn more than the cost of capital). Correlations among performance measures are also provided: all performance measures exhibit a slight positive correlation with market-adjusted variables showing that a variation of any performance measure has a positive association on SHV. With regard to the relative information content assessment (panel B), all variables considered have a positive firm-year median and mean, except CINOPAT. These data are not surprising since: 1) Interest margin and net commission-fees are typically positive in commercial banking; 2) CINOPAT. mainly includes bank’s operating costs and, consequently, it is expected to be negative; 3) capital charge is positive since it is computed as Cost of Equity multiplied by Book value of Capital (however, since it has a negative impact on SHV, a negative regression coefficient is expected); 4) accounting adjustments are positive: as a consequence, these accounting adjustments seem able to reveal the creation of SHV value that would be hidden when measuring SHV with other metrics. Concerning correlations, interest margin shows a weak positive correlation, while all the other independent variables appear to be uncorrelated with the independent variable.
5. Results Regarding the relative information content, Table 3 provides the results obtained by ordering the adjusted R2s from the highest (on the left) to the smallest (on the right). Information supplied includes: • the predicted sign of regression coefficient: all performance indicators are expected to have a positive regression coefficient30: • the coefficient estimates and their statistical significance (i.e. based on t-statistic); • the p-values from a two-tailed statistical test31 (p-value*) assessing the null hypothesis that all performance measures have the same information content; • the p-values from two-tailed statistical tests (p-value+) assessing the null hypothesis that there are no differences between pairwise comparisons on adjusted R2. This latter test, proposed in Biddle et al., (1995) and carried out by comparing the adjusted R2 obtained in the seven regressions (one for each performance indicators), enables us to test the information content of each couple of competing sets of independent variables to explain variation in the dependent variable (i.e. no difference between pairwise comparison of adj R2). According to the results obtained, net income has the higher adjusted R2 than all other performance measures and it is found to be a statistically significant predictor at 1%32. According to its p-values*, it is possible to reject (at the 1% significance level) the hypothesis that net income has the same 30
We focus on regression coefficient on the non-lagged terms. As stated in Biddle et (1997, p.319), regression coefficients of lagged terms are predicted to have the opposite sign. 31 Based on the F-stat 32 Based on the t-stat
18
relative information content of all other performance measures. By running a pairwise comparison (p-value+) with all other performance measures, the hypothesis that net income has the same relative information contents of any of the other performance measure can be rejected at a 2.5% significance level or better. Residual income has a R2 higher than EVA and both are higher than the R2s of ROE and ROA: all these independent variables exhibit a statistical significance at 1%. For each of these four performance indicator, differences among R2s and p-values* allow to reject (at 2% significance level or better) the null hypothesis of equal information content with all other performance measures. In addition, looking at the pairwise comparison of R2, p-values+ enables one to reject the possibility that residual income provides the same relative information contents supplied by any of the other performance at the 2.6% significance level or better and for EVA at a 3.4% significance level. Interest- and intermediation margins have the smallest R2s and it is not possible to reject the hypothesis that both financial ratios have equal information content as all other performance measures. These results seem to suggest that net income has a superior relative information content of any other indicator, such as Residual income and EVA. In detail, it appears that net income outperforms residual income, residual income outperforms EVA, EVA outperforms ROE, ROE outperforms ROA. The most traditional banking performance indicators (namely, interest and intermediation margins) show a very low information content in the light of creating SHV. Following Biddle et al., (1997), the regression model adopted to test the information content was run a second time by allowing the regression coefficient to vary in response to positive and negative values of each performance 33. Similarly to Biddle et al (1997) and prior research, estimated regression coefficients are usually larger and more statistically significant for positive performance measure values than for negative values. Results (table 4) obtained show that the ranking of the five performance measures changed, providing evidence that the information content of performance measures (especially financial ratios) varies according to their signs. In detail, ROE improved substantially its capability of explaining the variation in marked adjusted returns, while the R2s of residual income, net income and EVA are smaller than those obtained running the model with constrained coefficients. Differences among R2s become smaller, showing that when regression coefficients are allowed to change according to positive and negative performance values these performance measures have an almost equivalent relative information content. The analysis of p-values* still enable to reject the hypothesis that all performance measures have the same information content at 5% or better (only ROA cannot be rejected at low significance level). P-values+ provide a weaker evidence of the superiority of a performance indicator over another than those obtained when regression coefficients are constrained: for example, although residual income achieved the higher R2, the hypothesis that this indicator has the same information content as net income or ROE or EVA can be rejected only at a significance level higher than 6%. These results seem coherent with prior academic literature: EVA does not appear to dominate (in terms of relative information content) other performance measure. In particular, EVA seems to suffer a comparative information gap relative to net income and residual income.
33
Since intermediation- and interest margin have positive values in our firm-observations, the information content of performance measure cannot vary according to their signs and this regression model is worthless for these performance measures. This model is run to test the information content of the remaining five performance measures that assumes both positive and negative values in our sample.
19
Table 3 The relative information content: coefficient of positive and negative values of each performance measure constrained to be equal Net income
Predicted coef. sign Estimated Coeff
Residual income
EVA
ROE
Interest margin
ROA
Intermediation margin
T
t-1
t
t-1
t
t-1
t
t-1
T
t-1
t
t-1
t
t-1
+
-
+
-
+
-
+
-
+
-
+
-
+
-
0.1191
-0.0621
0.0987
-0.0148
0.0846
-0.0139
1.614
-0.850
28.62
-15.38
0.0047
0.0112
0.0024
0.0101
(1)
Adj R2
(-)
(1)
(-)
(1)
(-)
(1)
(-)
(1)
(-)
(-)
(-)
(-)
(-)
8.4
7.9
6.6
5.7
5.4
0.0
0.0
0.004
0.005
0.010
0.016
0.020
0.492
0.545
Residual income
EVA
ROE
ROA
Interest margin
Intermediation margin
0.020
0.015
0.022
0.011
0.019
0.019
Residual Income
-
0.026
0.021
0.010
0.006
0.007
EVA
-
-
0.034
0.016
0.010
0.010
ROE
-
-
-
0.069
0.061
0.045
ROA
-
-
-
-
0.042
0.050
Interest M.
-
-
-
-
-
0.484
Intermediation M
-
-
-
-
-
-
p-value* p-value+
Net Income
* The null hypothesis tested is that the information content of measure X1 is equal to that of X2 = X3 = … =X7 + The null hypothesis tested is that the information content of measure X1 is equal to that of X2 (1)
Statistically significant at 1% Statistically significant at 5% (10) Statistically significant at 10% (5)
20
Table 4 The relative information content: coefficient of positive and negative values of each performance measure# allowed to change
Predicted coeff. signs Estimated coefficients Adj R2 p-value*
Residual income t t-1 + + -
+
+
+
-
-
-
-
-
.09395
0.1252
-.0315
-.0056
.0922
0.1225
-.0464
(10)
(_)
(-)
(-)
T
(5)
Net income t-1 + -
(-)
(-)
ROE
EVA
t
t-1
ROA
t
t-1
t
t-1
+
-
+
-
+
-
+
-
+
-
+
-
-
+
+
-
-
+
+
-
-
+
+
-
-
-.0653
1.892
1.951
-2.305
-.7237
.0845
.1174
-.0308
.0011
31.01
40.31
-28.01
-14.14
(-)
(10)
(-)
(-)
(-)
(10)
(-)
(-)
(-)
(5)
(5)
(-)
6.8
6.6
6.5
5.6
5.2
0.023
0.025
0.026
0.040
0.049
Net income
ROE
EVA
ROA
0.06
0.062
0.078
0.030
Net income
-
0.004
0.075
0.003
ROE
-
-
0.079
0.015
EVA
-
-
-
0.027
ROA
-
-
-
-
p-value+ Residual income
#
(-)
Since all firm-year observations for intermediation margin and interest margin are positive can be only positive, it is meaningless to allow to differ to coefficient of positive and negative values for these three performance measures. * The null hypothesis tested is that the information content of measure X1 is equal to that of X2 = X3 = X4 = … + The null hypothesis tested is that the information content of measure X1 is equal to that of X2 (1) (5) (10)
Statistically significant at 1% Statistically significant at 5% Statistically significant at 10%
21
Regarding the analysis of the relative information content (table 5), we reported the expected signs of coefficients. We expect a positive association between the market adjusted return and the following items: interest margin, net commission and fee income (since one might expect that when these income increase, a bank creates SHV) and all costs and income necessary to calculate accounting NOPAT starting from the intermediation margin (since these are manly operating costs, the sum of these item produce a negative result and, as a consequence, we expect a positive coefficient). Regarding accounting adjustments, we do not have reasonable expectations about the predicted sign of the regression coefficients since these adjustments aim to eliminate GAAP distortions: as a consequence, these may have a positive or negative effect on market adjusted returns. The results obtained are all in the predicted direction. The analysis of the statistical significance of the estimated regression coefficients gives evidence that EVA components are statistically significant and their coefficients have a relatively large magnitude. In detail, CINopat (embodying mainly operating costs) is found to be a highly meaningful predictor and its two tail F-statistic (significant at 1%) suggests that CINopat provides the largest incremental contribution in explaining market-adjusted returns (MAR). Capital charges (CapChg) and accounting adjustments (AccAdj) are found to be of statistical significance as well, but their statistical significance is lower: their F-statistics (significant at 1,5%) suggest that these provides substantial incremental contributions in explaining variation of the independent variable. Interest margin (Int.M) and net commission and fee incomes (Net CFI) exhibit a poor statistical significance and a marginal contribution in explaining variation of MAR.
Table 5 The incremental information content: results Int.M
Int.M
NetCFI
NetCFI
CINOPAT
CINOPAT
CapChg
CapChg
aCCaDJ
aCCaDJ
t
t-1
t
t-1
t
t-1
t
t-1
t
t-1
+
-
+
-
+
-
-
+
?
?
Coeff.
0.0478
0.0197
0.3659
-0.2525
0.1221
-0.0094
-0.1490
0.4328
-0.0957
0.8818
t-stat
0.63
0.51
1.01
-0.71
2.93(1)
-0.12
-1.79(10)
0.61
-1.80(10)
-0.52
Predicted signs:
F-value*
0.71
0.87
3.12
2.55
2.43
p-value
0.492
0.484
0.008
0.014
0.013
(1) (5) (10)
Statistically significant at 1% Statistically significant at 5% Statistically significant at 10%
As a whole, EVA components offer a substantial contribution to the EVA information contents: as a result, the information contents of EVA become larger than those of all other performance measures. The adj. R2 is improved substantially (i.e. 11,7%) suggesting that the economic significance of incremental information content of EVA components is substantial. According to the above discussion, net income and residual income appear to have the highest relative contents, but both are outperformed by EVA when this measure is decomposed in its components.
22
6. Sensitivity analysis: are banks special? Banks have several peculiar characteristics that seem to make them different from any other type of firm. This paper has investigated the information content of performance measures (in the light of creating SHV) by accounting for these features and tailoring the investigation procedure to their special features. In detail, the empirical analysis carried out differs from the standard procedure adopted in prior research34 since: 1. the information contents of some performance indicators specific for banks are examined (such as interest margin and the intermediation margin); 2. the number of performance measures tested is extended by introducing some indicators, well known by practitioners, such as ROE and ROA; 3. EVA is calculated performing some adjustments tailored to banks’ features, rather than apply standard adjustments usually proposed for all companies. In detail, EVA calculation differs in the following aspects: a) seven adjustments have been carried out concerning the following items: 1) loan loss provision and loan loss reserve; 2) taxes; 3) restructuring charges; 4) security accounting; 5) general risk reserve; 6) research and development costs and training costs, 7) operating lease expenses. The fist five adjustments are specific for commercial banks35, while the remaining two are standard for any kind of company; b) The cost of invested capital is not measured by Weighted Average Cost of Capital (WACC), but by the cost of equity. Book value of capital (necessary to calculate the capital invested) is not measured in terms of total assets (as done in many studies), but by the book value of equity capital. The reason for these two differences can be explained by the banking industry’s core business: financial intermediation. In banking, debts should be considered a bank’s input (as the workforce, IT assets, etc), rather than a financing source (as in any other company). This difference influences the determination of NOPAT, of capital invested and of the cost opportunity of capital, as shown in section 3.2. This section analyses the sensitivity of the basic results reported in the previous section to an alternative specification that does not account for these banking peculiarities. The relative and incremental information tests are repeated by: •
Calculating EVA without the five bank’s specific adjustments (EVAstd). In other words, the adjustments made to calculate capital invested and NOPAT concerns the following items: 1) research and development costs and training costs, 2) operating lease expenses;
•
The cost of invested capital is measured by Weighted Average Cost of Capital (WACC) and book value of capital is measured in terms of total assets. As a consequence, capital charge is calculated by applying WACC on total assets.
34 35
We followed the investigation procedure of Biddle et al. (1997) The first four are suggested by Uyemura et al. 1996)
23
The differences affect the calculation of EVA, which is computed in a standard form (EVAstd); all other performance measures are not sensitive to these adjustments. As a consequence, the analysis focuses on EVA’s information content (controversial in prior research) by assessing to what extent the failure to account for banks’ peculiarities can affect the investigation of the relative and incremental information investigation. A first indication of the high impact of these peculiarities can be obtained by some descriptive statistics about EVA and its components, in both forms (table 6): the standard- (EVAstd) and the banking-tailored (EVA). First of all, all banks considered in the sample obtained a negative EVAstd: capital charges are higher than NOPAT since capital invested is very high. EVAstd are found to be negatively correlated its components, with banking-tailored EVA and all its components. Regarding the relative information content, results are provided in table 7. According to the results obtained, EVAstd seems to have a inferior information content than other performance measures (including banking-tailored EVA): the adjusted R2 obtained is lower, the p-value* is higher and the statistical significance of regression coefficients is lower. In addition, the pairwise comparison between R2 ‘s suggests that EVA outperforms EVAstd. In order to investigate if the information content of EVAstd varies according to its signs, the regression model was not run a second time by allowing the regression coefficient to vary for positive and negative values since all firm-observation are negative36. We simply calculate the pvalues+ by making pairwise comparisons of EVAstd and the other performance measures (with coefficient of positive and negative values free to change): our results suggest that all performance measures (especially, Net income and EVA) outperform EVAstd. Regarding the incremental information content of EVAstd, our results (table 8) are consistent with those obtained for banking-tailored EVA. Operating costs (embodied in CINopat) is the only independent variable statistically significant and provides the largest contribution in explaining variation of market-adjusted returns, while interest margin, net commission, and fees income supply minor contributions. Our results seem to suggest that the economic significance of the incremental information content of its components is substantially higher (i.e. R2 is 8.7%). However, when compared with the banking-tailored EVA, this value is considerably lower, giving evidence of a lower information content.
36 Differently from results obtained investigating banking-tailored EVA and from prior research, estimated regression coefficients are often found smaller and/or less statistically significant for positive value of a performance measure than for the negative values.
24
Table 6 Descriptive statistics of EVA and its components in both the standard and the banking-tailored forms+ A - The relative information content assessment EVA* •
NOPAT
•
Capital invested#
•
Capital charge#
#
• •
1.1250
126237
773038
1056517
3490275
324986
117844
469745
-1.975
-0.503
5.034
381827
114490
745547
44761859
15359270
61585241
2114375
655532
3094643
NOPAT
Capital invested
Std Dev.
408185
# #
Median 0.0160
2587209
EVAstd* •
Mean 0.1340
#
Capital charge
Correlation
NOPAT Cap.Inv Cap.Chg EVAstd NOPAT Cap.Inv. Cap.Chg
EVA
NOPAT
Cap.Inv.
0.261 0.042 0.044
0.750 0.708
0.982
-0.352 0.263 0.081 0.091
-0.097 0.993 0.773 0.735
-0.061 0.750 0.957 0.888
Cap.Chg.
EVAstd
NOPAT
Cap.Inv.
-0.090 0.713 0.937 0.915
-0.090 -0.179 -0.271
0.761 0.726
0.950
B - The incremental information content assessment Mean
Median
Std Dev.
Market Adjusted Return (MAR)
0.1590
0.0776
0.3305
Interest Margin(IntM)*
1.0750
0.3690
2.4530
Net CFI* CI
NOPAT*
0.2742
0.1250
0.5230
-0.9980
-0.3600
2.1820
CapChg*
2.387
0.7500
4.9870
AccAdj*
0.2844
0.0874
0.6102
Correlation INTM NetCFI CINopat CapChg AccAdj
MAR 0.104 0.032 0.033 -0.054 0.007
INTM
NetCFI
0.849 -0.878 0.189 0.545
-0.816 0.2465 0.642
CINopat
CapChg
-0.148 -0.529
0.017
The sample has 109 firm year observations. All variables were winsorised to ± 4 standard deviation from the median. Deflated by the market value of equity calculated five months after the beginning of the fiscal year. # Data in ITL millions + *
25
Table 6 The relative information content: coefficient of positive and negative values of each performance measure constrained to be equal Net income
EVA
ROE
ROA
Interest Margin
EVAstd
Intermediation Margin
T
t-1
t
t-1
T
t-1
t
t-1
T
t-1
t
t-1
t
t-1
t
t-1
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
0.1191
-0.0621
0.0987
-0.0148
1.614
-0.850
28.62
-15.38
0.0272
-0.279
0.0047
0.0112
0.0024
0.0101
Predicted coef. signs Estimated Coeff
Residual income
(1)
Adj R2
(-)
(1)
(-)
0.0846 -0.0139 (1)
(-)
(1)
(-)
(1)
(-)
(5)
(-)
(-)
(-)
(-)
(-)
8.4
7.9
6.6
5.7
5.4
4.0
0.0
0.0
0.004
0.005
0.010
0.016
0.020
0.044
0.492
0.545
Residual income
EVA
ROE
ROA
EVAstd
Interest margin
Intermediation margin
0.020
0.015
0.022
0.011
0.012
0.019
0.019
Residual income
-
0.026
0.021
0.010
0.005
0.006
0.007
EVA
-
-
0.034
0.016
0.006
0.010
0.010
ROE
-
-
-
0.069
0.025
0.061
0.045
ROA
-
-
-
-
0.018
0.042
0.050
EVAstd
-
-
-
-
-
0.003
0.004
Interest M.
-
-
-
-
-
-
0.484
Intermediation M.
-
-
-
-
-
-
-
p-value*
p-value+
Net income
* The null hypothesis tested is that the information content of measure X1 is equal to that of X2 = X3 = … =X7 + The null hypothesis tested is that the information content of measure X1 is equal to that of X2 (1)
statistically significant at 1% statistically significant at 5% (10) statistically significant at 10% (5)
26
Table 7 The relative information content: coefficient of positive and negative values of each performance measure# allowed to change Residual income t Predicted signs Estimated coeff.
Net income
t-1
T
t-1
EVAstd
t
t-1
t
t-1
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
+
-
-
-
-
-
-
+
+
-
-
+
+
-
-
+
+
-
-
+
+
-
-
.094
.122
-.031
-.006
.092
.122
-.046
-.065
1.892
1.951
-2.305
-.723
.0845
.117
-.031
.001
31.01
40.31
-
0.027
(10)
(_)
(-)
(-)
(5)
(-)
(-)
(-)
(10)
(-)
(-)
(-)
(10)
(-)
(-)
(-)
(5)
-28.01 -14.14
(5)
(-)
(-)
-
(5)
6.8
6.6
6.5
5.6
5.2
4.0
0.023
0.025
0.026
0.040
0.049
0.044
Net income
ROE
EVA
ROA
EVAstd
0.06
0.062
0.078
0.030
0.000
Net income
-
0.004
0.075
0.003
0.034
ROE
-
-
0.079
0.015
0.045
EVA
-
-
-
0.027
0.000
ROA
-
-
-
-
0.047
EVAstd
-
-
-
-
-
Residual income
Since all firm-year negative values for * The null hypothesis + The null hypothesis
(10)
ROA
t
+
p-value+
(5)
t-1
-
p-value*
(1)
EVA
t
+
Adj R2
#
ROE
t-1
0.279 (5)
observations for intermediation margin and interest margin can only be positive, it is meaningless to allow free coefficients for positive and these three performance measures. tested is that the information content of measure X1 is equal to that of X2 = X3 = X4 = … tested is that the information content of measure X1 is equal to that of X2
Statistically significant at 1% Statistically significant at 5% Statistically significant at 10%
27
Table 8 The incremental information content: results Int.M
Int.M
NetCFI
NetCFI
CINOPAT
CINOPAT
CapChg
CapChg
aCCaDJ
aCCaDJ
t
t-1
t
t-1
t
t-1
t
t-1
t
t-1
+
-
+
-
+
-
-
+
?
?
Coeff.
.0652
.0344
-.0460
-.125
.1325
-.0961
-.0299
1.268
.0564
.1565
t-stat
0.58
0.61
-0.11
-0.23
3.36(1)
-0.65
1.04
0.24
0.55
Predicted signs:
-1.85
(10)
F-value*
0.71
0.87
3.12
2.53
2.03
p-value
0.492
0.484
0.008
0.015
0.038
(1) (5) (10)
Statistically significant at 1% Statistically significant at 5% Statistically significant at 10%
7.Conclusions This paper investigated the information content of traditional (such as interest and intermediation margins, ROE; ROA and net income) and non-traditional (such as residual income and MVA) performance indicators in the light of creating SHV within the banking industry. While there is a unanimous agreement on the concept of SHV, it is debated what is the best method for assessing the value created by firms for their owners, as researchers and practitioners grapple with different performance metrics. There is a growing number of studies investigating which performance measure is the most compatible with SHV maximisation, but the evidence surrounding this issue is mixed. In addition, few papers investigated this issue focussing on the banking industries. In order to assess which performance metric is the most compatible with SHV maximisation, our analysis examines both relative information content (which is useful to select a single measure since performance indicators are considered mutually exclusive) and incremental information content (which aims to assess if a performance indicator adds information to the data provided by another measure). The empirical investigation focuses on the Italian banking industry: the sample selected includes almost all the banks listed in the Italian Stock Exchange over the period 199599. The investigation technique follows Biddle et al., (1997), with a few departures to better tailor the analysis to the peculiarities of a bank. For example, two additional performance indicators specific for commercial banks (i.e. interest margin and the intermediation margin) were analysed. In addition, EVA data is not taken from the Stern Stwar1000 database, but has been calculated tailoring this indicator on banks’ characteristics.
28
Our results suggest that net income and residual income have the highest relative contents among the performance indicators considered. However, EVA components offer a substantial contribution to the EVA information contents: capital charge (CapChg) and accounting adjustments (AccAdj) are found to be statistically significant and provide substantial incremental contributions in explaining variations of the independent variable, although operating costs (embodied in CINopat) provide the largest incremental contribution. As a result, considering the incremental contribution of EVA components, the information content of EVA becomes larger than those of all other performance measures, including net income and residual income. This finding is consistent with prior academic research since there is little evidence to support the claim that EVA has a relative information content superior to other performance metrics. However, our findings suggest that EVA outperforms other performance measures analysed when the incremental contribution supplied by its components is considered. Next, the sensitivity of these results was analysed by running an alternative specification that does not account for banking peculiarities. Differences concern the calculation of EVA, which is computed in a standard form (EVAstd). The first evidence about the high impact of banks’ distinctiveness is provided by the fact that all banks considered in the sample obtained a negative EVAstd. The analysis concerning the relative information content suggests that EVAstd has a substantially lower information content than the other performance measures (including bankingtailored EVA). Although the EVAstd’s information content increases when its component are considered, its R2 is considerably lower than those obtained by the banking tailored EVA, and is slightly superior to that of net income. As a result, the superiority of EVA is not directly verified in terms of relative information content, but evidence points to a certain advantage when the incremental contribution provided by its components is considered. In addition, this result is sensitive a proper accounting of bank’s peculiar features: as these distinctive characteristics are omitted in calculating EVA, results change and there is little evidence to support EVA’s superiority.
29
References
Acheampong Y.J., Wetzstein M.E (2001), A comparative analysis of value added and traditional measures of performance: an efficiency score approach, Social Science Research Network Electronic Paper Collection, FS 01-04 Al Ehrbar (1998), EVA – The real key to creating wealth, John Wiley & Sons, Inc Al Ehrbar (1999), Using EVA to measure performance and assess strategy, Strategy and Leadership, May-June, 20-24 Anthony R. (1973), Accounting for the cost of equity, Harvard Business Review, 51, 88-102 Bacidore, J.M., Boquist J. A., Milbourn T.T., Thakor A.V. (1997), The Search for the Best Financial Performance Measure, Financial Analysts Journal, May/June, 11-20 Basel Committee on Banking Supervision (2001), The new Basel capital accord, Bank of International Settlements, www.bis.org Biddle G., Seow G. (1991), The estimation and determinants of association between returns and earnings: evidence from cross-industry comparisons, Journal of Accounting, Auditing and Finance, 6, 183-232 Biddle G., Seow G., Siegel A. (1995), Relative versus incremental information content, Contemporary Accounting Research, 12, 1-23 Biddle G.C., Bowen R.M., Wallace J. S. (1999), Evidence on EVA, Journal of Applied Corporate Finance, 12, 2, 67-79 Biddle G.C., Bowen R.M., Wallace J.S. (1997), Does EVA beat earnings ? Evidence on Association with stock returns and firm values, Journal of Accounting and Economics, 24, 3, 301-36 Black A., Wright P., Bachman J.E., (1998), In search of shareholder value, FT Pitman Publishing, London Burgstahler D., Dichev I. (1997), Earnings, adaptation and equity value. The Accounting Review, 72, 187-215 Collins D.W., Pincus M., Xie H., (1997), Equity valuation and negative earnings: the role of book value of equity, unpublished papers, university of Iowa, Iowa city, IA Copeland T., Koller T., Murrin J.(1994), Valuation: Measuring and Managing the Value of Companies, : John Wiley & Sons, New York. Dalborg H. (1999), Shareholder value in banking, paper prepared for the May 1999 session of Institrut International d’Etudes Bancarie. Damodaran A. (1994), Damodaran on valuation, John Wiley & Sons, inc, New York Damodaran A. (1998), Value Creation and Enhancement: Back to the Future, Contemporary Finance Digest 2, 5-51. Damodaran A. (1999), Applied corporate finance, John Wiley & Sons, inc, New York Damodaran A. (2002), Estimating risk free rates, Working paper, Stern Business school, New York University, http://pages.stern.nyu.edu/~adamodar/ Damodaran A. (2002), Estimating risk parameters, Working paper, Stern Business school, New York University, http://pages.stern.nyu.edu/~adamodar/
30
Di Antonio M. (1999), Vecchi e nuovi indicatori di performance economico-finanziaria in banca: un inquadramento concettuale, APB news, n. 3, 63-92 Di Antonio M. (2002), Creazione di valore e controllo strategico nella banca, Bancaria Editrice, Roma Dillon R.D., Owers J.E. (1997), EVA as a Financial Metric: Attributes, Utilization, and Relationship to NPV, Financial Practice and Education, Spring/Summer, 32-40. Easton P., Harris T., (1991), Earnings as an explanatory variable of returns. Journal of Accounting Research, 29, 19-36 Forestieri G. (1993), Economies of scale and scope in the financial services industry: a review of recent literature, in OECD, Financial Conglomerates, 1993 Forestieri G. (2000), Corporate & Investment Banking, EGEA, Milano Forestieri G., Mottura P. (2000), Il Sistema finanziario. Istituzioni, Mercati e Modelli, EGEA, Milano Gardener E. P.M. (1997), The challenge of deregulation for the European Banks, Institute of European Finance, University of Wales Bangor, working paper no.3 Garvey G.T., Milbourn T.T., (2000), Eva versus Earnings: does it matter which is more highly correlated with stock returns?, Journal of Accounting Research, 38, 209-45 Greenspan A. (1996) Remarks at the annual convention of American Bankers Association, Honolulu, Hawaii, October 5, Gualandri E. (1996), Il quadro normativo e di vigilanza sulle istituzioni creditizie, in Onado (1996) Hamilton R. (1777), An introduction to merchandize. Edinburgh Hayn C. (1995), The information content of losses, Journal of Accounting and Economics, 20, 125-53 Kramer J.K., Pushner G. (1997), An Empirical Analysis of Economic Value-Added as a Proxy for Market Value Added, Financial Practice and Education, Spring/Summer, pp. 41-49. Lehen K., Makhija A.K.M. (1997), EVA, accounting profits and CEO turnover: an empirical examination, 1985-1994, Journal of applied corporate finance, 10, 2, 90-97 Marshall A. (1890), Principle of economics, The McMillan Press Ltd, London, New York Masini M. (1986), Alcuni profili di analisi del risultato della gestione finanziaria della banca, Il Risparmio, n.5 Masini M. (1987), Nuove strategie nella ricerca di produttività e redditività in banca, L'impresa banca, giugno Masini M. (1993), La misura e l'interpretazione del ROE in imprese complesse, in AA.VV., 'Finanza Credito e Assicurazione - Scritti in onore di Carlo Masini', EGEA, Milano, Myers, R. (1996), Metric Wars, CFO: The Magazine for Senior Financial Executives, 12, 10, 41-50 O’Byrne S.F. (1996), Eva and Market Value, Journal of Applied Corporate Finance, 9, 1, 11625 Onado M. (1996), La banca come impresa, Il Mulino, Bologna Paul J. (1992), On efficiency of stock-based compensation, Review of Financial Studies, p 471502 Peterson, P. P., Peterson D. R. (1996), Company performance and measures of value added, The Research Foundation of the Institute of Chartered Financial Analysts
31
Rajan M.V. (2000), Discussion of Eva versus Earnings: does it matter which is more highly correlated with stock returns?, Journal of Accounting Research, 38, 247-54 Rappaport A. (1986), Creating shareholder value, The free press, New York Rappaport A. (1998), Creating shareholder value, The free press, New York Resti A. (1999), La creazione di valore in banca: canali endogeni e canali esogeni, APB news, n.3, 19-37 Schuster L. (2000), Shareholder value management in bank, Macmillan press ltd Sironi A. (1999), Estimating banks’ cost of equity capital: evidence from an international comparison, Quaderno NEWFIN, Università L. Bocconi, Milan Solomon D. (1965), Division performance measurement and control. Financial Executives research foundation, New York Stewart B.G. (1991), The quest for value, Harper Business The Economist (1997), A star to sail by ?, Economist, August 2nd, 61-63 The Economist (2001), Marked by the market, Economist, November 29th Uyemura D.G., Kantor G.C., Pettit J.M., (1996), EVA for banks: value creation, risk management and profitability measurement, Journal of Applied Corporate Finance, 9, 2, 94-105 Velez-Pereja I. (2000), Value creation and its measurement: a critical look at EVA, unpublished paper, Universitad Javeriana, Bogotà, Colombia Vesala J. (1993), Retail Banking in European Financial Integration, Bank of Finland Research department, Finland
32
