Analyzing creditworhtiness from financial statements in the presence of operating leases.
The leasing market in the U.S. is very large. Some estimates show that more than half of all public and private investment in equipment and software in the U.S. is currently being acquired under leases (Equipment Finance and Leasing Foundation, 2007) with comparable results found in the acquisition and use of real estate assets, automobiles and airplanes, and many other tangible assets.
Leases are contractual obligations that allow assets owned by one party to be used by another party, for specified periods of time, in return for a payment or series of payments. Companies choose to lease assets for a variety of reasons, including economies of scale or scope, increased flexibility, tax advantages, improved access to capital, reduced costs of upgrading equipment, and improved risk sharing (SEC, 2005).
The accounting guidelines that pertain to leases are primarily dictated by Statement of Financial Accounting Standard 13 (SFAS 13) Accounting for Leases, which was issued in 1976. This statement provided a two-pronged approach to accounting for leases. Leases that transferred most of the benefits and responsibilities of ownership to the party using the asset would be treated as economically similar to sales with attached financing agreements, and generally referred to as "capital" leases. The user of the asset (the lessee) would record the asset and a related liability on its balance sheet in an amount estimated as the present value of the required lease payments with periodic write-offs incorporating the depreciation (amortization) of the asset, associated operating expenses such as property taxes, and the implicit financing charges.
Leases not considered capital leases were labeled "operating" leases and accounted for as rental contracts. The company using the asset would not record the asset or the related liability for future contractual rental payments on its balance sheet, but would instead record a periodic rental expense. SFAS 13 specified that a lease would be deemed a capital lease if 1) the lease transferred ownership to the lessee using the asset by the end of the lease term or through a bargain purchase option; 2) the term of the lease was at least 75 percent of the estimated economic life of the leased property; or 3) the present value of the minimum lease payments to be made by the lessee was at least 90 percent of the fair value of the leased asset. Leasing agreements that did not involve any of these requirements could be accounted for as operating leases.
Although the distinction made between capital leases and operating leases is usually straightforward, there are many issues such as contingent and variable payment requirements, optional term extensions, and other clauses that complicate the analysis. Nonetheless, such a distinction must be made to properly account for the transactions. Whether considered capital or operating leases, each still has extensive disclosure requirements. For example, companies must provide the following information: a description of the nature of leasing arrangements; the nature, timing and amount of cash flows associated with the leases; the amount of lease revenues and expenses reported in the income statement each period; and any additional information pertinent to the balance sheet classification of the various components of the leasing arrangements.
Unfortunately, the accounting guidance for leases has produced a situation in which similar transactions can receive different accounting treatment depending on very artificial distinctions. For example, a lease requiring payments equaling 89 percent of an asset's fair value would be treated as an operating lease while one with payments equaling 90 percent would be a capital lease, despite the two arrangements being very similar from an economic perspective. Likewise, there are significant economic differences between a one-month lease and a 10-year lease for the use of a building, yet they would likely each have similar accounting requirements as both would likely qualify as operating leases.
Accordingly, companies have been able to take advantage of these artificial distinctions and structure leases that achieve a specific accounting treatment, whether as a capital or an operating lease. These companies have been aided in these endeavors by a large number of attorneys, accountants, lenders, etc., to the point where lease structuring to meet various accounting or tax goals has become an industry unto itself (SEC, 2005).
In partial response to this, the SEC conducted a study of the issue and found that some 63 percent of the total population of issuers of financial statements reported operating leases, and 22 percent reported capital leases. Their sample showed that the undiscounted sum of the future committed cash flows related to non-cancelable operating leases was approximately $206 billion, which, if extrapolated to the entire population of U.S. issuers, suggested that the total amount of cash flows committed to operating leases approached $1.25 trillion. Assuming these leases were instead capitalized, discounting the cash flows would likely reduce this amount to some 60 to 80 percent of the total. Thus, perhaps some $1 trillion dollars of lease obligations is currently being unreported on the balance sheets of U.S. companies.
The "success" (some would say abuse) of operating leases has led the SEC to recommend that the Financial Accounting Standards Board (FASB), in conjunction with the International Accounting Standards Board (IASB), to reconsider its accounting guidance for leases, a process that the FASB and IASB began undertaking in earnest in July 2006. Given the complexity of the issues surrounding lease accounting, this process is expected to take a considerable amount of time (a check of the websites for either organization can be made to see the current status of the project). Nonetheless, it is expected that many of the so-called operating leases of today will need to be accounted for more like capital leases in the future.
Of course, the level of importance placed on this underreporting of lease obligations is a matter for each individual user of financial information to determine. One particular area in which the issue may be especially critical is in assessing the credit standing of individual companies.
There is a plethora of approaches to assessing credit standing. Many of these are based to varying degrees on using a cross-section of financial and accounting ratios. For example, there are ratios (e.g., interest coverage and fixed charge coverage ratios) that look at a company's ability to generate income and/or cash flows to meet debt obligations. There are other ratios (e.g., debt and debt-equity ratios) that focus on the relative amount of outside (creditor) funding of a company's operations. In addition, there have been more sophisticated metrics and models developed that attempt to incorporate a wide array of data to provide insights into a company's creditworthiness and likelihood for it to experience financial distress.
The best known of these more sophisticated models is the Altman Z-score (Altman, 1968). Using multiple discriminant analysis on a variety of financial ratios, the model breaks down to a simple weighted average of five specific accounting ratios (working capital, retained earnings, earnings before interest and taxes, and sales, each in relation to total assets, plus the ratio of market value of equity to book value of liabilities). The result is compared to arbitrary cutoff points indicating either a high or low probability of financial distress (i.e., bankruptcy).
Altman's model remains the standard against which most others are compared and tends to be the one most embraced by practitioners (IOMA, 2003), even though it is some 40 years old and has faced a constant barrage of criticism. Surprisingly, it continues to offer several advantages over more sophisticated models in both its simplicity and its effectiveness. Bellovary, Giacomino, & Akers (2006) discuss how broader and arguably more rigorous models generally do not improve upon simpler models like Altman's, which have stood the test of time. For the purposes of this study, we focus on the Altman model, and examine the impact that capitalizing operating leases (a likely outcome of the current revisions being discussed by the FASB and IASB) would have on that assessment.
Prior evaluations of the effects of capitalizing operating leases on a company's financial statements have generally been based on the seminal papers by Imhoff, Lipe, and Wright (1991, 1997). These typically involve reconstructing a company's financial statements in a manner in which the operating lease obligations reported as footnotes in the annual reports are capitalized following the methods employed for capital leases. This has potential implications for the reported values of both balance sheet and income statement items, and has been extensively examined in a variety of ways (Beattie, Edwards, & Goodacre, 1998; Hodge & Ahmen, 2003; Bennett & Bradbury, 2003; Fulbier, Silva & Pferdehirt, 2006; Noland, 2006). However, none of these papers focuses on the critical area of how credit analysis might be affected by changes brought about by capitalizing operating leases. Furthermore, none of these explicitly examines alternative methods that might be used to value the operating leases, a particularly crucial item in any assessment of the significance of said leases. This paper offers an examination of both topics.
DATA AND METHODOLOGY
Data for this study was gathered from Compustat (Research Insight). The primary sample, the one used to examine the impact of capitalizing operating leases on the Altman model and similar credit-focused metrics, includes all U.S. nonfinancial companies that reported in their most recent annual report some amount of operating lease obligations for each future lease period as required by SFAS 13. For purposes of comparability over time, such leasing data was also required for each of the four previous reporting periods. To eliminate some severely nonsensical ratio results, the companies included in the sample were also required to report positive amounts of both current liabilities and total equity. As a result, 595 companies were included in the primary sample.
In selecting and constructing variables for the study, several variables were found with extremely high or low values. For example, while the median value of the interest coverage ratio for the most recent year's results was 8.09, the maximum value was 24,247.5 and minimum value was -2,430.0. Eliminating the influence of these extreme values provides us with an increase in statistical significance and explanatory power, an issue that is especially critical when evaluating financial ratios (Frecka & Hopwood, 1983). Therefore, in order to reduce the effect of outliers on our results, all dependent and independent variables were winsorized at the 5th and 95th percentiles. This resulted in much less extreme maximum and minimum values. For example, the resulting range of interest coverage ratios was reduced to a maximum value of 301.0 and a minimum value of -2.94. Although winsorizing also reduced the mean of the ratio from 101.46 to 35.65 and the standard deviation from 1047.6 to 75.5, the overall results of our study do not appear to be especially sensitive to winsorizing as the results proved to be similar in both qualitative and quantitative ways.
We examined a broad array of variables that are frequently used to assess the credit standing of individual companies, with a specific focus on the Altman model. These included:
Altman's Z-score, which itself is made up of five distinct ratios, and calculated as follows: Z = 1.2[X.sub.1] + 1.4[X.sub.2] + 3.3[X.sub.3] + 0.6[X.sub.4] + 0.999[X.sub.5], where [X.sub.1] is the ratio of net working capital to total assets, [X.sub.2] is the ratio of retained earnings to total assets, [X.sub.3] is the ratio of earnings before interest and taxes to total assets, [X.sub.4] is the ratio of the market value of total equity to the book value of total liabilities, and [X.sub.5] is the ratio of total sales to total assets. Generally speaking, the higher the Z-score, the lower the probability that the company would be expected to experience financial difficulties (e.g., bankruptcy), with 3.0 essentially being the threshold for considering companies to be of low risk, 1.8 considered the cut-off for high risk candidates, and with results between 1.8 and 3.0 representing a range of uncertainty. The current ratio, calculated as total current assets divided by total current liabilities. The quick ratio, calculated as the total of cash, marketable securities, and receivables divided by total current liabilities. The debt ratio, calculated as total liabilities divided by total assets. The interest coverage ratio, calculated as total earnings before interest and tax expense (EBIT) divided by total interest expense. When expanding the definition to include other types of financing activities, the ratio is often adjusted and reformulated as a fixed charge coverage ratio. Unfortunately, this ratio is defined and measured in a wide variety of ways in practice. Here it is assumed to be a simple extension of the interest coverage ratio, incorporating (adding back) the assumed amount of financing incorporated in the current and future operating lease payments to both the numerator and denominator. The assumed amount of financing embedded in operating lease payments was determined based on an estimate of the present value of all current and future operating lease payment obligations of the company. The present value of these lease payments was calculated using a discount rate of six percent. (Other discount rates up to 10 percent were examined with little impact on the final results). The resulting "present value of operating leases" (PVOL) was assumed to represent the additional amount of lease assets and liabilities that would be reported on the balance sheet IF the operating leases were valued and reported similar to the methods used for capital leases. Note: Because operating lease commitments beyond five years into the future are presented in a lump-sum, a method of valuing these payments had to be developed. This was accomplished by converting the "after five year" amount to an annuity with a duration equal to the number of periods needed to equate that amount given payment amounts equal to the fifth year's obligation, or 10 years, whichever was shorter. The resulting PVOL figure was multiplied by six percent to arrive at the current amount of financing (interest expense) assumed to be incorporated in the operating lease payments. Other researchers include different items in their definitions of additional fixed charges to be included in the ratio. Two common techniques involve either assuming that all of the current lease payments (not only the financing component) were "fixed charges" or a "rule-of-thumb" approach of considering one-third of the current payment to be a proxy for the total financing inherent in all current and future operating lease obligations. These two alternative formulations are also examined.
EBITDA (earnings before interest, taxes, depreciation, and amortization) coverage ratio, calculated like the interest coverage ratio, but adding back the amount of depreciation and amortization expense to the numerator. EBITDA is often used as a "quick and dirty" cash flow proxy.
ROIC (return on invested capital), defined as earnings before interest and taxes (EBIT) valued on an after-tax basis divided by the sum of total debt and total equity, with debt referring to external financial commitments of the company rather than total liabilities. Return ratios such as ROIC are not necessarily evaluated as credit assessment variables on their own but ROIC is included here to try to capture the impact that capitalizing operating leases would have on both the income statement (EBIT) and the balance sheet (total debt).
Capitalizing operating leases as if they were capital leases can have very profound effects on the analysis of financial statements and their associated ratios. To begin with, it impacts both sides of the balance sheet. The present value of the operating lease obligations (PVOL) can be regarded as additional liabilities to be reported on the balance sheet. Given the accounting identity, the total increase in liabilities would then need to be offset by an equal amount of assets, if one assumes the PVOL equaled the economic value of using those assets over time. However, this association is rarely one-to-one given the exponential features of present value calculations vis-a-vis the linear features of straight-line depreciation often assumed for long-term assets. This results in an asset valuation that is on average less than the corresponding liability valuation. This difference, although varying across time and discount rate assumptions, is often assumed to average around 75 percent; that is, the average value of the capitalized assets is 75 percent of the value of the capitalized liabilities (Imhoff, Lipe, & Wright, 1991).
Reducing the value of the left-hand side of the balance sheet by 25 percent, we need corresponding adjustments to the right-hand side. Following Imhoff, Lipe & Wright (1997) the 25 percent valuation reduction can be allocated into two components. The remaining value not considered as a liability could be accounted for as a reduction in equity. However, given the tax consequences of deductible lease expenses, it would be logical to assign the difference between the tax effect (a separate liability from the lease obligations themselves) and the residual impact on equity (retained earnings). To demonstrate, assume the present value of lease obligations was $100 million for a firm with a tax rate of 40 percent. If $75 million is the assumed value of the assets, the $25 million reduction on the right-hand of the balance sheet could be assigned as a reduction in equity of $15 million ($25 x (1 - 0.40) and a reduction in tax liabilities of $10 million ($25 x 0.40). Refinements might also be necessary to separate the current and noncurrent nature of the operating lease obligations and concomitant tax implications. This adjustment was made as follows: the additional current liabilities (operating lease payment and taxes) would be equal to the total lease obligation due in the subsequent year, less the amount of taxes deferred beyond the first year based on the proportion of lease obligations in the first year to the overall PVOL. Any remaining amounts could then be considered as noncurrent lease and tax obligations.
These adjustments result in numerous changes to variables used in calculating various ratios used in the analysis of financial statements. For example, any ratio involving current liabilities would need to be adjusted as the amount of current liabilities would now also include assumptions about the short-term lease payments and associated tax liabilities. Similarly, ratios incorporating total liabilities or total equity would face similar adjustments. And on the asset side, ratios involving total assets would need to be adjusted to take into account the value of the leased assets. Current assets would typically not be adjusted since leased assets would likely be classified as capital assets and fall under a noncurrent time horizon. A case might be made about the prepaid rental value of lease agreements being considered as current assets but we have made no such assumption here.
Similar adjustments from capitalizing operating leases would be necessary for various income statement items. This would primarily result in reclassifying lease rental expenses into depreciation and interest expense components. Although net profit numbers and their respective ratios would be relatively stable (some minor shifting may occur from period to period), financial ratios involving other profit figures like EBIT or EBITDA used in various financial coverage ratios, may face significant adjustments.
For relatively complex variable such as Altman's Z-score, we see that capitalizing operating leases could have a dramatic and complex impact. For example, four of the variables include total assets in their denominators, which would result in lower ratio figures, given the assumed increased amount of assets. In addition, the first variable, net working capital to total assets, would also be affected as the numerator (and hence the overall value) would be further reduced by the assumed increase in current liabilities. The second variable, retained earnings to total assets, would face a similar fate due to the assumed reduction of retained earnings. The third variable, EBIT to total assets, would be affected as operating expenses are shifted to financing expenses, likely increasing the value of the numerator. The fourth variable, market value of equity to book value of liabilities, would likely decrease given the increased amount of liabilities and the assumption that a company's stock price would be unaffected by the capitalization of operating leases. However, this may not be the case if the company's stock value would be affected by the increased amount of leverage apparent from capitalizing the operating leases.
We tested several aspects of the potential impact that capitalizing operating leases would have on various financial ratios often used to evaluate a company's credit standing. We began by examining the individual ratios, both as calculated from the original financial statement data and then after making the necessary adjustments associated with capitalizing operating leases. Given its prominence in credit analysis, we specifically focused on Altman's Z-score to determine how it might be affected by capitalizing operating leases, particularly among companies with significant amounts of those types of leases reported in its financial statements.
Our sample consisted of 595 companies that: 1) reported operating lease obligations for each future period as required by SFAS 13, and reported such items for each of the five most recent reporting periods; and 2) reported positive amounts of current liabilities and total equity for each of the five period. The sample included companies from a wide variety of industries and covered a wide spectrum of sizes from large multinationals such as ExxonMobil ($219 billion in assets) to small local companies such as Good Time Restaurants ($11 million).
An initial look at the sample from the most recent reporting period provides glimpses of the magnitude of the impact that capitalizing operating leases could have on individual companies. The average size (total assets) of each company was $5.3 billion, with a median of $1.1 billion. The average amount each company was undervalued (as measured by the present value of leases) was $471 million with a median of $117 million. Total assets were understated by an average of some 10 percent, with an even higher proportion of underreported liabilities.
Likewise, the average interest expense reported by these companies was $48 million (median $7 million). Capitalizing the operating leases and reclassifying a portion of the annual rental expense from an operating to an interest expense would increase reported interest expenses by an average of $35 million, with a median of $9 million). Thus, reported interest expenses could on average more than double.
Such changes would have a dramatic impact on the calculation of a multitude of financial ratios. For example, based on the financial statement data as reported, the mean current ratio was 2.26 (median 2.03). Based on figures adjusted to take into account the presence of operating leases, this amount falls to 2.04 (1.77), a reduction of greater than 10 percent. Even more striking is the 20 percent drop in value for Altman's Z-score, from a mean of 5.05 (median 4.40) using as-reported data to 3.68 (3.37) with the adjusted figures. A summary of results can be found below in Table 1. Note that in all cases the changes in mean and median are significant beyond the 99th percentile. Although not reported here, such significant results have remained fairly consistent over the past five years of financial data.
Another key result in examining the impact that capitalizing operating leases might have on credit analysis comes from examining the changes in the Z-scores for individual companies. A basic interpretation of the Z-score is that a company with a score above 3.00 is unlikely to suffer from financial distress, while one with a score below 1.80 is very likely to experience such difficulties. From our sample of 595 companies, 463 companies initially had Z-scores above 3.00, yet only 359 continued to have such scores after making the adjustments for the operating leases. Thus, nearly one-quarter (22.5%) of the companies considered relatively free of credit risk would not be so considered if their operating leases were taken into account. Similarly, 549 of the 595 companies initially had scores above 1.8, yet 44 of those companies would fall below the all-important 1.80 threshold when considering the impact of their operating leases.
Because of its significance as a tool used in credit analysis, we expanded the analysis to examine how operating leases might affect Altman Z-scores. We conducted a series of regressions in which we examined the relationship between changes in the Z-score with changes in the individual components of the model. We also looked at how both size, defined as the natural logarithm of total assets, and the relative amount of operating leases, measured as a ratio of current operating lease expense and present value of future obligations to total assets, might impact the Z-score. The results of the various regression models are shown in Table 2 below.
As seen in Table 2, changes in any of the five variables comprising the Altman Z-score had a significant relationship with changes in the Z-score itself. This relationship is especially strong with the Altman [X.sub.2], [X.sub.3], and [X.sub.4] variables, with evidence that the [X.sub.1] and [X.sub.5] variables (level of sales and working capital) offset each other with several models producing significant [X.sub.1] estimates and insignificant [X.sub.5] estimates and vice versa. The effect of company size is also consistent across different models, alone or in conjunction with various combinations of the Altman variables, but in a negative sense. That is, Z-scores for larger companies are less affected by capitalizing operating leases, even if the relative amount of leasing was high.
This is especially evident in the simplest model (Model 2) that examines only the relationship of size and extent of leasing on changes in individual Z-scores. The expected positive impact of relative amount of operating leases is offset by the negative influence of company size. Further research will be needed to determine reasons for this. For example, are the larger firms in the sample less prone to be heavy users of operating leases, is it an industry-level phenomenon that has not been captured, or is it something else? More sophisticated tests will be needed to cull out more specific conclusions given the relatively high amounts of correlation among all of the variables.
ALTERNATIVE VALUATION MODELS
Having explored the impact that capitalizing operating leases has on the calculation and interpretation of Altman Z-scores, we next briefly examine alternative methods for valuing the operating leases. Most research in this area is based on the Imhoff, Lipe & Wright methodology, using present value calculations to determine pro forma debt and interest payment amounts associated with capitalizing operating leases. However, at least three other heuristic approaches are also found in various academic and practitioner publications.
Alternatives to the present value methodology include multiplying the current year's operating lease expense by a factor of 8 (Imhoff, Lipe & Wright, 1993), multiplying the next year's lease obligations by a factor of 6 (Ely, 1995), or multiplying all current and future lease obligations by two-thirds, with one-third of each year's payment representing the financing cost of the leases (Gibson, 2007) for that year. The one-third, two-third approach is noteworthy given its simplicity and its legitimacy. Securities filings typically include the one-third figure as a representation of the interest factor of the company's leasing expenses when calculating its "earnings to fixed charges" ratio as required by SEC Regulation S-K, Paragraph 503d.
Given the broader focus of examining alternative methods of valuing operating leases, the four methods (present value and three heuristic models) were evaluated using a broader sample. In this case all firms found in the Compustat database (excluding financial, non-US, and zero or negative equity firms) were included in the sample.
The 4,390 companies in the sample were then classified based on each company's use of operating leases, identified as either "non-leasers" (524), "minimal" leasers (2,632), "moderate" leasers (1,021) and "heavy" leasers (213). The designation was based on the total value of lease obligations (in present value terms) as a percentage of total assets with 0.01% to 5% deemed to be "minimal" leasers, 5.01% to 50% "moderate" leasers, and above 50%, "heavy" leasers.
Table 3 shows the differences in means and medians of leases as a percentage of total assets using each of the four methods. It is clear that no matter what level of leasing activity a company employs, the one-third, two-thirds approach consistently understates the value of leases relative to the present value methodology, while the two multiplier approaches consistently overstate the value of leases, and by a considerable margin
Each valuation method was then evaluated in terms of their correlations. As seen in Tables 4 and 5, whether evaluated using a parametric (Pearson) or nonparametric (Kendall Tau-b) approach, the correlation of the simple one-third, two-thirds method clearly dominates the other two heuristic methods in terms of how well it tracks the results of the more sophisticated present value approach. Note that using a higher (e.g., 10% discount) rate does not significant affect the results, although the levels of correlation are marginally lower across the board.
One conclusion that may be reached in evaluating these results is that, assuming absolute precision in the valuation of operating leases is not paramount, the one-third, two-thirds approach gives a very good approximation of the more complex present value method. And this is true over a wide range of discount rates that may be appropriate for the present value calculation. Thus, given its relative ease of calculation and seemingly high level of accuracy, the one-third, two-thirds approach may be an appropriate tool to use when incorporating operating lease obligations into one's analysis of a company's overall financial, and especially credit, situation.
In light of the current activities of the FASB and the IASB regarding the proper accounting for "operating" leases, we have initiated a review of some of the potential impacts that capitalizing those leases would have on various financial ratios, particularly those used to assess the credit standing of companies. Given the crucial nature that credit analysis plays in the credit-providing functions of the economy, changes caused by the retooling of this accounting standard could have a dramatic impact on the credit process.
The issue of trying to assess a company's financial situation when it engages in a significant amount of operating leases is not a new one. It has been examined in academia and the professional literature since FASB 13 was first issued over thirty years ago, and even earlier. However, many of these approaches have been inconsistent or insufficient at best. We believe we have provided a new beginning to assessments of the effects operating leases have on various company's operations, and the financial reporting of those operations. This is likely a fruitful area of research, given the practical nature of the results, as well as the current economic situation in which the credit-providing industry has come under such increased scrutiny.
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Securities and Exchange Commission (2005). Report and recommendations pursuant to section 401(c) of the Sarbanes-Oxley act of 2002 on arrangements with off-balance sheet implications, special purpose entities, and transparency of filings by issuers.
Kurt R. Jesswein, Sam Houston State University
Table 1: Key Financial Ratios Using As-Reported and Adjusted Financial Statement Figures Ratio As Reported Adjusted Mean Mean Current Ratio 2.25 1.94 Quick Ratio 1.48 1.30 Altman [X.sub.1] (WC / TA) 0.2580 0.1973 Altman [X.sub.2] (RetEarn / TA) 0.1787 0.1129 Altman [X.sub.3] (EBIT / TA) 0.0991 0.0710 Altman [X.sub.4] (MVEq / TL) 4.6111 3.1446 Altman [X.sub.1] Sales / TA) 1.3796 1.1620 (Altman Z-score 5.02 3.70 Interest coverage (EBIT / Int) 35.58 7.66 Using Total Lease Payments 3.73 Using 1/3 of Lease Payments 6.39 EBITDA Coverage 49.36 11.38 Debt Ratio 89.56 154.70 Return on Invested Capital 8.81 6.29 Ratio As Reported Adjusted Median Median Current Ratio 2.03 1.77 Quick Ratio 1.22 1.07 Altman [X.sub.1] (WC / TA) 0.2644 0.1830 Altman [X.sub.2] (RetEarn / TA) 0.2800 0.2035 Altman [X.sub.3] (EBIT / TA) 0.0982 0.0719 Altman [X.sub.4] (MVEq / TL) 3.2230 2.2784 Altman [X.sub.1] Sales / TA) 1.2612 1.0790 (Altman Z-score 4.40 3.37 Interest coverage (EBIT / Int) 8.09 4.04 Using Total Lease Payments 2.61 Using 1/3 of Lease Payments 4.12 EBITDA Coverage 12.41 6.67 Debt Ratio 80.32 123.20 Return on Invested Capital 8.74 6.23 Table 2: Relative Impact of Variables on Changes in Z-Scores Adj [R.sup.2] Intercept PVOL pct 1 0.9220 Estimate 0.22 -0.16 t-value ** 2.22 * -1.74 2 0.3403 Estimate 1.63 1.80 t-value *** 6.48 *** 14.47 3 0.9162 Estimate 0.26 0.18 t-value *** 2.60 ** 2.36 4 0.5594 Estimate 1.52 -0.40 t-value *** 6.78 * -1.85 5 0.9183 Estimate 0.20 0.00 t-value ** 2.01 0.03 6 0.9129 Estimate 0.04 -0.26 t-value 0.45 *** -2.73 7 0.9220 Estimate 0.24 -0.14 t-value *** 2.63 -1.57 8 0.9215 Estimate 0.05 t-value ** 2.01 9 0.9148 Estimate 0.05 t-value ** 2.18 10 0.5377 Estimate 0.35 t-value *** 6.37 11 0.9183 Estimate 0.06 t-value ** 2.50 12 0.9121 Estimate 0.05 t-value * 1.90 13 0.9215 Estimate 0.05 t-value ** 2.25 Adj [R.sup.2] Size [X.sub.1] 1 0.9220 Estimate -0.02 0.45 t-value * -1.84 0.99 2 0.3403 Estimate -0.12 t-value *** -3.65 3 0.9162 Estimate -0.03 0.87 t-value ** -2.08 * 1.84 4 0.5594 Estimate -0.15 3.10 t-value *** -5.45 *** 2.85 5 0.9183 Estimate -0.02 0.86 t-value -1.46 * 1.86 6 0.9129 Estimate 0.00 0.56 t-value -0.07 1.15 7 0.9220 Estimate -0.03 t-value ** -2.16 8 0.9215 Estimate 0.51 t-value 1.17 9 0.9148 Estimate 1.63 t-value *** 3.89 10 0.5377 Estimate 4.46 t-value *** 4.31 11 0.9183 Estimate 1.06 t-value ** 2.47 12 0.9121 Estimate 0.24 t-value 0.54 13 0.9215 Estimate t-value Adj [R.sup.2] [X.sub.2] [X.sub.3] 1 0.9220 Estimate 2.11 6.60 t-value *** 8.34 *** 5.37 2 0.3403 Estimate t-value 3 0.9162 Estimate 2.26 8.42 t-value *** 8.66 *** 6.77 4 0.5594 Estimate 2.78 24.62 t-value *** 4.63 *** 8.77 5 0.9183 Estimate 2.82 t-value *** 12.84 6 0.9129 Estimate 12.01 t-value *** 10.88 7 0.9220 Estimate 2.12 6.80 t-value *** 8.37 *** 5.61 8 0.9215 Estimate 2.07 5.84 t-value *** 8.47 *** 5.02 9 0.9148 Estimate 2.05 10.19 t-value *** 8.04 *** 9.81 10 0.5377 Estimate 2.23 23.31 t-value *** 3.76 *** 8.59 11 0.9183 Estimate 2.73 t-value *** 12.99 12 0.9121 Estimate 11.15 t-value *** 10.73 13 0.9215 Estimate 2.05 6.19 t-value *** 8.41 *** 5.49 Adj [R.sup.2] [X.sub.4] [X.sub.5] 1 0.9220 Estimate 0.53 0.84 t-value *** 52.30 *** 6.72 2 0.3403 Estimate t-value 3 0.9162 Estimate 0.53 t-value *** 50.02 4 0.5594 Estimate -0.06 t-value -0.20 5 0.9183 Estimate 0.55 0.98 t-value *** 54.76 *** 7.91 6 0.9129 Estimate 0.54 0.93 t-value *** 49.96 *** 7.10 7 0.9220 Estimate 0.54 0.74 t-value *** 52.73 *** 6.92 8 0.9215 Estimate 0.54 0.74 t-value *** 53.73 *** 7.21 9 0.9148 Estimate 0.53 t-value *** 51.14 10 0.5377 Estimate -0.24 t-value -0.99 11 0.9183 Estimate 0.55 1.01 t-value *** 56.27 *** 11.23 12 0.9121 Estimate 0.54 0.73 t-value *** 50.87 *** 6.70 13 0.9215 Estimate 0.54 0.78 t-value *** 54.71 *** 8.18 *** denotes significance at 99%, ** significance at 95%, and * significance at 90% Note: PVOLpct = ratio of total operating lease expense and present value of future lease obligations as a percentage of total assets; Size = natural logarithm of total assets; [X.sub.1] = net working capital as a percentage of total assets; [X.sub.2] = retained earnings as a percentage of total assets; [X.sub.3] = earnings before interest and taxes as a percentage of total assets; Altman [X.sub.4] = market value of equity as a percentage of total liabilities; and [X.sub.5] = sales as a percentage of total assets Table 3: Value of Operating Leases by Different Valuation Methods Mean Std Dev Median Minimal Leasers (n = 2632) PVOLpct 0.0258 0.0213 0.0213 PVOL13pct 0.0214 0.0194 0.0170 PVOLx8pct 0.0865 0.0765 0.0687 PVOLx6pct 0.0535 0.0473 0.0435 Moderate Leasers (n = 1021) PVOLpct 0.1581 0.0866 0.1314 PVOL13pct 0.1373 0.0846 0.1098 PVOLx8pct 0.3518 0.2740 0.2879 PVOLx6pct 0.2480 0.1597 0.2051 Heavy Leasers (n = 213) PVOLpct 0.8052 0.5343 0.6615 PVOL13pct 0.7342 0.5371 0.5998 PVOLx8pct 1.2258 0.8947 1.0089 PVOLx6pct 0.8319 0.4679 0.7313 Note: PVOL pct is the present value of leases as a percentage of total assets using the present value approach (and a 6% discount rate) PVOL13pct is the value based on the one-third, two-third approach PVOLx8pct is the value using the 8 times current lease expense approach, and PVOLx6pct is the value based on the 6 times next year lease expense approach. Table 4: Pearson Correlations of Lease Valuation Methods By Level of Leasing Minimal Leasers PVOLpct PVOL13pct PVOLx8pct PVOLx6pct PVOLpct 1.0000 PVOL13pct 0.9314 1.0000 PVOLx8pct 0.4452 0.3674 1.0000 PVOLx6pct 0.7656 0.6460 0.6133 1.0000 Moderate Leasers PVOLpct PVOL13pct PVOLx8pct PVOLx6pct PVOLpct 1.0000 PVOL13pct 0.9514 1.0000 PVOLx8pct 0.1380 *0.0691 1.0000 PVOLx6pct 0.4972 0.3683 0.4199 1.0000 Heavy Leasers PVOLpct PVOL13pct PVOLx8pct PVOLx6pct PVOLpct 1.0000 PVOL13pct 0.9775 1.0000 PVOLx8pct 0.4952 0.4134 1.0000 PVOLx6pct 0.7739 0.6672 0.6927 1.0000 All correlations significant at the 99% level, except for *, significant at the 95% level. Table 5: Kendall Tau-b Correlations of Lease Valuation Methods By Level of Leasing Minimal Leasers PVOLpct PVOL13pct PVOLx8pct PVOLx6pct PVOLpct 1.0000 PVOL13pct 0.9420 1.0000 PVOLx8pct 0.4608 0.4339 1.0000 PVOLx6pct 0.7164 0.6759 0.5979 1.0000 Moderate Leasers PVOLpct PVOL13pct PVOLx8pct PVOLx6pct PVOLpct 1.0000 PVOL13pct 0.8781 1.0000 PVOLx8pct 0.1763 0.1182 1.0000 PVOLx6pct 0.3739 0.2953 0.5328 1.0000 Heavy Leasers PVOLpct PVOL13pct PVOLx8pct PVOLx6pct PVOLpct 1.0000 PVOL13pct 0.8730 1.0000 PVOLx8pct 0.2505 0.1531 1.0000 PVOLx6pct 0.3674 0.2587 0.7091 1.0000 All correlations significant at the 99% level.
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|Author:||Jesswein, Kurt R.|
|Publication:||Academy of Accounting and Financial Studies Journal|
|Date:||Jan 1, 2009|
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