Value relevance of the earnings impact of lease capitalization

VALUE RELEVANCE OF THE EARNINGS IMPACT OF LEASE CAPITALIZATION

C. S . Agnes Cheng and Su-Jane Hsieh

ABSTRACT We investigate value relevance of the earnings impact of the Statement of Financial Accounting Standards No . 13 (SFAS 13) . SFAS 13 requires committed long-term leases to be capitalized as assets and liabilities along with recognition of depreciation expense and interest charges. This requirement tends to have adverse effects on risk, debt ratios, and earnings numbers . Previous research finds significance of capital lease information through its impact on risk and debt ratios from a balance sheet statement perspective . Unlike previous studies, we evaluate the value relevance of SFAS 13 from an income statement prospective . SFAS 13 requires a restatement of prior year's income for the SFAS 13 effect when adopting the standard . Thus, we measure earnings impact of SFAS 13 for the year (t) prior to the adoption as the difference between the restated earnings minus the reported earnings of year t. The earnings impact of SFAS 13 can be reasonably inferred from the footnote disclosure in year t due to the disclosure requirement of Accounting Series Report No . 147. Based on the efficient market hypothesis, the disclosed earnings impact of SFAS 13

Advances in Accounting, Volume 17, pages 31-64 . Copyright ® 2000 by JAI Press Inc . All rights of reproduction in any form reserved . ISBN: 0-7623-0611-4 31



32

C . S . AGNES CHENG and SU-JANE HSIEH of year t should be reflected in the stock price of year t. We employ a return-earnings model to investigate whether the market participants incorporate earnings impact of SFAS 13 in the assessment of market value of firms . We do not find significant value relevance for the earnings impact of SFAS 13 using the traditional linear model . However, we find evidence supporting value relevance for the earnings impact of SFAS 13 using nonlinear models but only when the earnings impact is large. In addition, based on a rank-adjusted earnings model developed in this paper, we find that the earnings impact of SFAS 13 has larger value relevance than do the pre-SFAS No . 13 earnings . While our main objective is to evaluate the value-relevance of earnings caused by SFAS 13, our paper also contributes to an extensive investigation of the performance of different return-earnings models and to the development of a rank-adjusted earnings model .

INTRODUCTION We investigate value relevance of the earnings impact of the Statement of Financial Accounting Standards No . 13 (SFAS 13) . SFAS 13 requires committed long-term leases be capitalized as assets and liabilities along with recognition of depreciation expense and interest charges . This requirement tends to have adverse effects on risk, debt ratios, and earnings numbers . Previous research has found significance of capital lease information through its impact on risk and debt ratios from a balance sheet statement perspective .' However, none has explicitly evaluated the value relevance of the income statement effect caused by SFAS 13 . One might argue that this negative earnings impact may be insignificant to warrant a study . However, Imhoff, Lipe and Wright (ILW) (1993) report a significant decrease in net income for the airline industry (a median of 22 percent decrease) when the operating leases were capitalized . In addition, ILW (1997) illustrate that the earnings impact of constructive capitalization could be material with respect to valuing some key financial ratios (i .e ., return on assets and return on equity) . Although a direct measure of the earnings impact of SFAS 13 is very difficult, if not impossible, an indirect measure is feasible due to the restatement of prior year's earnings for SFAS 13 impact in the adoption year of SFAS 13 . Thus, we measure earnings impact of SFAS 13 for the year prior to the adoption (referred to as year t) as the difference between the restated earnings minus the reported earnings of year t . Moreover, the earnings impact of SFAS 13 can be reasonably inferred from the footnote disclosure in year t due to the disclosure requirement of Accounting Series Report No. 147 (ASR 147) . Based on the efficient market hypothesis, we expect the disclosed earnings impact of SFAS 13 be reflected in the stock price of year t . To investigate whether the market participants incorporate earnings impact of SFAS 13 in assessing the market value of firms in year t, we use a return-earnings model . Finding value relevance of the earnings impact of SFAS 13 may provide further supporting evidence for the statement . Failing to



Earnings Impact of Lease Capitalization

33

find value relevance may imply either that SFAS 13 earnings information is not important or that the market is not efficient with respect to footnote data . We do not find significant value relevance for the earnings impact of SFAS 13 using the traditional linear return-earnings model . However, we find evidence supporting value relevance for the earnings impact of SFAS 13 using nonlinear models when the earnings impact is large . In addition, based on a rank-adjusted earnings model developed in our paper, we find that the earnings impact of SFAS 13 has larger value relevance than do the pre-SFAS 13 earnings (i.e ., the reported earnings of year t) . While our main objective is to evaluate the value-relevance of earnings caused by SFAS 13, our paper also contributes to an extensive investigation of the performance of different return-earnings models and to the development of a rank-adjusted earnings model . Previous research reports that the return-earnings relationship is not linear and that a simple linear model may fail to detect the underlying theoretical relationship (Freeman and Tse 1992 ; Cheng, Hopwood and McKeown 1992) . We evaluate several forms of nonlinear models and find that the rank model performs best . Hence, we use rank as an instrumental variable . Due to the nature of one of our hypotheses, the coefficients of a rank model need to be comparable . The rank model as used in Cheng and colleages (1992) does not have this property . Thus, we develop a rank-adjusted earnings model which earnings coefficients (often referred as the earnings response coefficients, the ERC) have similar economic meanings and therefore comparable . The remainder of this paper is organized as follows . Section two provides background to our study . Section three discusses hypotheses and methodology . Section four describes the sample selection process, and the variables used in the empirical models and empirical analyses . Section five reports empirical results . A summary and conclusion are provided in the last section .

BACKGROUND The Development of Accounting Standards Related to Leases Long-term leasing has gained popularity over purchasing since the 1950s due to a number of financing advantages . Currently, leasing accounts for about one-third of the externally financed capital equipment acquired in the United States . In the case of an operating lease, a lessee reports the lease payment as rental expense and both the leased asset and the obligation remain off-balance-sheet . For a capital lease, a lessee not only reports depreciation and interest expenses, but the leased assets and obligations are reported on the balance sheet as fixed assets and liabilities, respectively . Managers, in general, prefer to report leases as operating leases rather than capital leases because of the adverse effects of lease capitalization on some financial



34

C . S . AGNES CHENG and SU-JANE HSIEH

numbers . First, the capital lease reporting method produces lower income numbers when rental payments are less than the sum of the depreciation expense and the implicit interest expense during the early stages of a lease . While this phenomenon may be reversed during later stages of a lease contract, it will persist if a company's capital lease activities continue to grow . Second, the increase in the reported liabilities (both long-term and current) will have adverse effects on the solvency (i .e., the debt/equity ratio) and the liquidity ratios (i .e ., the current ratio) . Moreover, the profitability ratios (i .e ., return on assets (ROA), return on equity (ROE)) and the activity measure (i .e ., the asset turnover rate) will both be adversely affected when using the capital lease accounting treatment as opposed to that of operating leases . 3 Recognizing the adverse effects of the capital lease reporting method and management preference to report leases as operating leases, accounting rule makers have made a series of efforts to regulate the reporting for leases . Accounting Principles Board Opinion No . 5 (APB 5) (effective 1964 and superseded by SFAS 13 in 1976) : Reporting of Leases in Financial Statement of Lessee provides criteria for lease capitalization . This standard requires a long-term noncancelable lease be capitalized if it is, in substance, an installment purchase of property (i.e ., leases with automatic ownership transfer). During the effective period of APB 5, most lease contracts were written in a way so as not to be qualified as capital leases even though they were in fact equivalent to purchases . To improve information disclosure concerning leases, the APB and the Securities and Exchange Commission (SEC) issued APB Opinion 31 : Disclosure of Lease Commitments by Lessees (APB 31) and ASR 147 : Notice of Adoption of Amendments to Regulation S-X Requiring Improved Disclosure of Leases, respectively in 1973 . APB 31 required the disclosure of future minimum rental commitments for all noncancelable leases . ASR 147 not only required the disclosure of future rental commitments but also required the disclosure of the present values of all noncancelable financing leases, the implicit interest rate used in deriving the present values and the impact on net income of capitalizing such leases . 4 Thus, the income effect of capitalization of leases became available when ASR 147 became effective in 1973 . Based on the efficient market hypothesis, this income effect should be reflected in the stock price . Although the income effect and financing information of all noncancelable lease arrangements became available via the disclosure requirement of ASR 147 and APB 31, the information remained off-income-statement and off-balance-sheet . Firms continue to have a great amount of flexibility in reporting leases either as operating leases or as capital leases when following the capitalization criterion of APB 5 . In order to reduce managerial flexibility in reporting leases, the FASB issued Statement of Financial Accounting Standard (SFAS) No . 13 : Accounting for Leases in November 1976 . This statement mandated that long-term noncancelable leases be reported in the financial statements as capital leases if one of four criteria is met .5 The four criteria established by the SFAS 13



Earnings Impact of Lease Capitalization

35

are based on a concept that leases should be capitalized as long as all the benefits and risks of the leased property are substantially transferred, not limited to the ownership transfer as set by APB 5 . Thus, mangers have less flexibility in reporting leases under the criteria of SFAS 13 . Managers have recognized the potential adverse effects of SFAS 13 on key financial numbers and have taken actions to reduce this potential adverse effect . 6 Due to concern of the significant adverse effects on some financial numbers and the potential for technical default from the implementation of SFAS 13, the FASB gave a four-year grace period (1977-1980) for firms to retroactively apply SFAS 13 for leases entered prior to December 31, 1976 . During the grace period, firms continue to disclose lease-related financing and income impact information as required by ASR 147 . Thus, during the grace period of SFAS 13, the earnings impact of capitalization of leases continued to be available through footnote disclosure . Moreover, FASB 13 required firms to restate prior year's earnings for FASB 13 effect when adopting the standard during the grace period . Therefore, the earnings impact of SFAS 13 for year t (the year prior to the adoption year of SFAS 13) can be estimated by comparing the restated earnings of year t with the reported earnings of year t . Prior Studies Previous research provides evidence on the importance of capital lease information mainly from the balance sheet statement perspective . They have demonstrated the impact of capital lease on stock price, risk (market risk and equity risk) and profitability ratios (i .e ., ROA, ROE) . Ro (1978) conducts event studies evaluating the equity market's reaction to the information disclosure of lease capitalization under ASR 147 . He finds that capitalized lease disclosures have an adverse impact on securities . However, this impact is less significant for firms which disclosed only present value data without the income effect than for firms which disclosed both present value and the income effect . El-Gazzar and colleagues (1986) observed that highly leveraged firms are more likely to rely on leasing to finance their assets in order to avoid a technique default . Hence, the adverse effect of SFAS 13 should be more severe for highly leveraged firms . El-Gazzar (1993) studied the market reaction to multiple regulatory event dates related to the passage of SFAS 13 and identified two event dates 8 having significant negative abnormal returns . This negativity is correlated with the changes in the tightness of debt covenant restrictions resulting from the compliance with SFAS 13 . While Finnerty and colleagues (1980) and Murray (1982) failed to conclude significant correlation between capital lease information and market beta, Bowman (1980) and Kuo (1988) found that capital lease information had a significant contribution to the assessment of market risk . Moreover, ILW (1993) and Ely (1995) demonstrated that the constructed capitalization information of operating lease is used by market participants in assessing equity risk . As for the adverse effect of capitalization of leases on key financial ratios, ILW (1991) illus-



36

C . S . AGNES CHENG and SU-JANE HSIEH

trated a significant decline in the ROA (varies from 34 percent to 10 percent) for seven different pairs of firms in seven industries when they apply a constructive capitalization technique to operating leases . In a latter study, ILW (1997) incorporated the income effect as well as the balance sheet effect in studying the capitalization impact on ROA and ROE . They found both ROA and ROE are lower than the reported ROA and ROE although the impact can be quite different from only adjusting the balance sheet effect . Despite numerous studies on the impact of lease capitalization, none has investigated the significance of earnings impact of SFAS 13 (lease capitalization) on investors' perception of firm value . Although Ro's (1978) results imply that capital leases have an impact on firm value through their adverse impact on earnings, he did not conduct a direct test of the earnings effect of capital leases on firm value . Due to the intensive usage of the bottom-line earnings by market participants in evaluating firms value (i .e ., a material positive or negative unexpected earnings usually results in a significant reaction in the stock market) and the significant earnings impact from lease capitalization (ILW 1993, 1997), it is important to understand whether the market impounds the earnings impact of lease capitalization (i .e ., the earnings impact of SFAS 13) in market value .

RESEARCH METHODOLOGY AND HYPOTHESIS We adopt annual return-earnings association models to evaluate the value relevance of earnings impact of SFAS 13 . The coefficients of earnings in the model are used to measure value relevance . 9 The test year, year t, is the year prior to the SFAS 13 adoption year, year t+1 . Two earnings variables are included in our main models : the pre-SFAS 13 earnings (the reported earnings of year t, denoted as E't) and the earnings impact caused by SFAS 13 (denoted as E",) . E", is the sum of depreciation and interest expenses from capitalized leases net of their rental payments (if treated as an operating lease) of year t . FASB required firms to apply SFAS 13 for leases entered prior to December 31, 1976, retroactively and report the restated earnings for previous years in the adoption year . Thus, the earnings impact of SFAS 13 of year t (E"t) is estimated as the difference between the SFAS 13 restated earnings of year t (Et) minus the reported earnings of year t (E t), E " t = Er - E t. Since we employ a return-earnings model for the study, an priori condition underlying the study is that this earnings impact of SFAS 13 for year t (E"t) is available to the market participants in year t so that it could be reflected in the stock price of year t. In estimating E", we rely on the restated earnings of year t which is not available until year t+1 . However, the earnings impact of SFAS 13 of year t can be inferred from the footnote disclosure of year t during the grace period of SFAS 13 . 10 Moreover, prior studies (i .e ., Espahbodi, Stock and Tehranian, 1991 ; El-Gazzar, 1993 ; Espahbodi, Espahbodi and Tehranian, 1995) have



Earnings Impact of Lease Capitalization

37

shown that the market anticipates the adoption of new standards and often responds to that information prior to the formal adoption . Thus, it is reasonable to assume that market is aware of the earnings impact caused by SFAS 13 in year t . Two hypotheses (in their null form) are tested : Hi0 : The earnings impact (E") does not have a significant positive relation to security returns . H20 :

The earnings impact (E') does not have different value-relevance from pre-SFAS 13 earnings (E') .

Failing to reject H1 may imply either that SFAS 13 earnings information is not fundamentally important or market is not efficient with respect to footnote data . If it is for the former reason, adding the SFAS 13 earnings impact into the bottom-line earnings will add noise to the earnings . If it is for the latter reason, then applying SFAS 13 may improve earnings quality . On the other hand, rejecting H1 will indicate that the earnings impact of SFAS 13 is value relevant and the market participants utilize this information in assessing the market value of firms . If E" is value relevant (i .e ., rejecting H1), a second issue is whether its value-relevance differs from that of pre-SFAS 13 earnings (i .e ., E'i ) . SFAS 13 prescribes earnings impact to be combined with earnings from other sources for reporting purposes . If the value-relevance of E" differs from that of earnings from other sources (i .e ., E'), E" should be reported as a separate component of earnings (as in the case of extraordinary items) . The magnitude of an association between returns and earnings depends upon tt several factors such as earnings permanence and earnings quality . It may also depends on the presentation format : formal recognition versus footnote disclosure . Capital leases involve long-term investments . Hence, capital lease differential earnings should, on average, have higher earnings permanence and therefore have a higher association to market returns than pre-SFAS 13 earnings . However, because footnote information may have more measurement error and receive less attention by the market than information reported in the financial statements, capital lease differential earnings information available in footnote disclosure may have less return-earnings association than the audited numbers formally reported in financial statements . Therefore, the SFAS 13 earnings impact can have either more or less association with returns than pre-SFAS 13 earnings . Accordingly, H2 focus on inequality test of the earnings impact of SFAS 13 (i .e ., E") and the pre-SFAS 13 earnings (i .e ., ED instead of directional difference . Thus, H2 is tested using a two-tailed test . Since our paper relies heavily on the association between earnings and returns to assess the value-relevance of earnings information, we discuss the return-earnings specifications and various models used in this study in the followina sections .



38

C . S . AGNES CHENG and SU-JANE HSIEH The Return-Earnings Model Specification

By assuming that corporate earnings follow a random walk and that current earnings can sufficiently determine security prices (that is security prices do not lead earnings), Ohlson (1991) and Biddle and Seow (1991, 1995) demonstrate that earnings levels and earnings changes deflated by beginning-of-period prices are equally correlated with stock returns . However, when assuming that security prices contain more information than earnings of the same period 12 and assuming that security prices lead earnings for one period, Ohlson (1991) and Ohlson and Shroff (1991) demonstrate that earnings levels specification yield an unbiased estimate of the slope coefficient while the change specification results in a biased estimate of the earnings coefficient. Assuming that security prices lead earnings for more than one period (i .e ., two periods), Kothari (1992) analytically demonstrates that both earnings levels and change specifications result in a biased estimate of the earnings coefficient . However, earnings levels specification outperforms the change specification by producing a higher adjusted R2 and a less biased earnings response coefficient estimate . Kothari (1992) also demonstrates analytically that the accurate proxy of market unexpected earnings can yield an unbiased estimate of the return-earnings association (i .e ., earnings response coefficient, ERC) and a higher adjusted R 2 than the earnings levels . However, he also concludes that an accurate proxy for the market's expectation is difficult to obtain, and that a proxy such as earnings deflated by beginning-of-period price may be the best available proxy for use in return-earnings regression . The findings of Easton and Harris' (1991) are consistent with those of Kothari (1992) . They compare the performance of earnings levels and earnings change specification models using cross-sectional regressions for the periods of 1968-1986, and find that the earnings levels specification consistently outperforms the earnings change specification in terms of adjusted R 2 for every year during their study period when the dependent variable is defined as the annual raw returns . Our study uses earnings levels as the independent variable because (1) using earnings levels as the independent variable is supported by both theoretical and empirical work, 13 and (2) the majority of our sample (99 out of 118 firms) restated financial statements for only one year . Hence the change in capital-lease earnings is not available for most of our sample firms . The Basic Linear Models The first two empirical models used for hypothesis testing are specified as follows : Model 1 .1 :

Rpt

= a0 1 + a' E'Jt + e lit

(1)

Model 1 .2 :

R ;r

= a0 2 + a' E'ir + a" E"1t + e2 i,

(2)



Earnings Impact of Lease Capitalization

39

where : t = the year immediately prior to the adoption of SFAS 13, Rpt

= the accumulated daily returns for firm j in fiscal year t ; 14

E'~t

= the reported accounting earnings of year t available for common stockholders for firm j in year t, scaled by beginning market value,

E"J

15

= the SFAS 13 differential earnings for firm j in year t (subtracting E'~1 from the restated accounting earnings denoted as Est), scaled by beginning market value,

a0 1 a02 = intercept coefficients for Model 1 .1 and Model 1 .2, respectively ; a' = coefficient for

E'~t

in Model 1 .1 or Model 1 .2 ; 16

=coefficient for

E".

in Model 1 .2,

a"

e lit E2/t = error terms .

The regression coefficients in these models reflect the value-relevance of earnings information to market returns . When returns are measured over an annual window, much information is available for the market to assess firm value . Accordingly, the coefficients measure the potential relevance of earnings to firm value instead of how the market responds to earnings . If differential earnings are reflected in stock prices (or returns) in year t, we should observe a significant a" and a higher adjusted R2 for model 1 .2 than for model 1 .1 . Further, if the differential earnings and other earnings information have similar valuation implications, we should observe no difference between a' and a" Nonlinearity in Return-earnings Models Lev (1989) surveys a large number of studies on earnings/returns relationship and reports that the explanatory power of earnings variable in these studies was very low . He suggests that model specification problems may be one of the reasons causing low explanatory power of earnings . Freeman and Tse (1992) suggest that the relationship between earnings and returns is not linear, but instead is S-shaped because earnings permanence becomes relatively small when earnings are large in magnitude . Cheng and colleagues (1992) empirically investigate model specification problems (including heteroscedasticity, residual nonnormality, and omitting of variables) associated with "unexpected earnings response regression model" (UERRM) . UERRM is a linear regression model using the unexpected earnings variable to explain risk adjusted returns . They find that the specification error not only induces low R2 but is large enough to



40

C . S . AGNES CHENG and SU-JANE HSIEH

affect conclusions regarding the relationship of returns and earnings . 17 They show that when nonlinearity is uncontrolled, statistical results are weaker and often unstable . In an attempt to reduce the nonlinearity and specification problems, Cheng and colleagues (1992) transform the earnings forecast errors via either power or rank transformation and find that the adjusted R 2 improved substantially . In addition, stable statistical results on earnings/returns relationship are obtained with this transformation . Among various models they compare, the rank model performs best . 18 We apply some S-shaped models suggested in Freeman and Tse and the rank model suggested in Cheng and colleagues' and find that the rank model performs best. Accordingly, we focus on using the rank models to control for nonlinearity. The reason that the rank measure performs well in the return-earnings models may be due partly to the fact that the ordinal measure (i .e ., the rank) effectively takes into account the reduced return-earnings association for extreme values of earnings (or outliers) as suggested by Freedman and Tse . However, Cheng and colleagues do not find that deleting outliers result in an elimination of model specification problems . The strong evidence of superior performance of the rank model in Cheng and colleagues, even without a theoretical explanation, has promoted many studies to use the rank transformation . The rank transformation has been applied in various market studies with different valuation/returns models (e .g., Subramanyam 1996 ; Smith-Bamber and Cheon 1995 ; Kothari and Zimmerman 1995 ; Teets and Wasley 1996) . In Cheng and colleagues' rank model, an earnings change variable is transformed into an annual rank . The coefficient is adjusted by the number of ranks used in the regression model for comparison across regressions . Our linear models differ from the models in Cheng and colleagues in two respects . First, we use earnings levels instead of earnings changes . Second, our main model (Model 1 .2 in equation 2) contains two variables (the pre-SFAS 13 earnings (E') and SFAS 13 differential earnings (E')) . While there are no studies providing reasons for the superior performance of the rank model, we take the "rank transformation" as an empirical necessity and apply it strictly to the independent variables in our models . This necessity is viewed from the perspective that the rank model is an effective process to correct measurement errors in the original earnings variables . Therefore, it does not matter what earnings variables are transformed (i .e ., earnings change, earnings level or other earnings) and whether we transform these earnings variables based on yearly or cross-sectional rankings . However, to test the robustness of our results, we combine both the original and the rank-adjusted variables in the same model to test the superior performance of the rank-adjusted variables . In a typical return-earnings model, the coefficient of earnings (often referred to as the earnings response coefficient, or ERC) represents the effect of a one unit increase in the earnings variable relative to prior price on the change of market returns, 19 However, when using the rank model, the coefficients of the rank-transformed earnings do not have traditional economic meanings . The equality tests of



Earnings Impact of Lease Capitalization

41

the coefficients, needed for hypothesis H2, require that the coefficients of earnings have the traditional economic meanings . To overcome this problem, we introduce a rank-adjusted earnings model, which not only can have the power of a rank model, but also produce coefficients with traditional economic meanings . This model adjusts the original earnings measures based on a linear relation between the earnings measures and their respective ranks . The restated earnings measures, instead of ranks, are then used in the rank-adjusted earnings model . Accordingly, the equality tests for the coefficients of the two earnings variables (E' and E') can be conducted . This model is similar to the instrumental variable approach in Brown and colleagues (1987) . The following four steps describe the adjustment process : 1.

The deflated earnings levels (E~t and E'it) of all firms in the sample are ranked . 2 . The deflated earnings levels are regressed on the ranks from step 1 using the following equations : E lt =t'o+q' Rank ~t

+o'. t

E' Jt = 0" p + 0" Rank'~ t + 0 Jt

(3a) (3b)

where : E'~ t, E'~t

=

as defined in Models 1 .1 and 1 .2,

0'p, 4"p = intercept coefficients, 0', 0" = coefficient for Rank '.Jt and Rank' . , Rank~t = the rank assigned for the deflated original reported earnings for firm j at year t, Rank". = the rank assigned for the deflated differential earnings for firm j at year t, 0' .t, 0' ~ t = error terms .

3.

Based on equations (3a) and (3b), the earnings are adjusted as follows : REJt = 0'p + O' Rank~t

(4a)

RE'ft = 0"o+ O"Rank".

(4b)

where : RE~t = rank-adjusted deflated reported earnings, R E" ;,

=

rank-adjusted deflated differential earnings .



42

C. S . AGNES CHENG and SU-JANE HSIEH

The rank-adjusted earnings variables share the same means with the original cardinal measures ; however, their variances are smaller . Referring to equations (3a) and (3b), since the independent variables are orthogonal to the residual errors, the variances of the original cardinal measures (the dependent variable) are the sum of two components : one is affected by ranks (variance of O'Rank' /t or 0"Rank' jt) and another one equals the variances of the residual errors (0' . or 0". ) . If rank measures of earnings do capture the essential value-relevant element contained in earnings, an adjustment procedure adopted in equation (4a) or (4b) will filter the random noise contained in the original cardinal measures . Hence, the restated earnings will better delineate the true earnings/ returns relationship . 4.

Accordingly, the restated earnings REJ: and RE' ~t are used in return-earnings models to test our hypotheses . The rank-adjusted earnings models are specified as follows : Model 2 .1 : RJt = (301 + P'RE' .t + 01Jr

(5)

Model 2 .2 : RJt = (302 + G3' RE'!t + 0" RE". + 02Jt

(6)

The coefficients of the restated earnings variable (i .e ., (3' and (3 ") will have similar economic meanings as the coefficients of the unadjusted measures (i.e ., a' and (x") . The former group of the coefficients (i .e ., (3' and 13") indicates the effect of a change of one unit of the adjusted (scaled) earnings on returns while the latter group (i .e., Wand a") indicates the effect of a change of one unit in the original (scaled) earnings on returns . The equivalence of the restated earnings models (i .e ., equations (5) and (6)) to the rank model can be shown as follows : Substituting equation (4a) into equation (5), we obtain Rit = Po + P'Wo + 0' Rank Jt) + 01Jt = (PO + R'0'o) + P'O' Rank''j + 01J,

(7)

Substituting equation (4a) and (4b) into equation (6), we obtain Rjt = PO + 1'(O' o + 0' Rank J t) + (3"(O" o + 0" Rank".) + 02Jt _ (Po + (3'O'o + R"0"O) + (3'O'Rank' + P"0"Rank 'jt + 02Jt

(8)

Equations (7) and (8) are the rank models and can be restated : Model 3 .1 :

RJt =y01 + Y Rank' . + 01 Jr

Model 3 .2 :

R; t = 02 + Y Rank' ; t + Y' Rank";t + 02 ;t

(9) (10)



Earnings Impact of Lease Capitalization

43

Model 3 .1 and 3 .2 will generate the same R 2 and t-statistics as those from Model 2 .1 and 2 .2 . However, coefficients in Model 2 .1 . and 2 .2 have traditional economic meanings and can be compared with coefficients in Model 1 .1 and 1 .2 . Coefficients in Model 3 .1 and 3 .2 can only be compared to each other and the differences in coefficients only indicate the relative explanatory power of the earnings variables . Testable Hypotheses Linear model Model 1 .2 and rank-adjusted earnings model Model 2 .2 are the main models used to test our hypotheses . If SFAS 13 differential earnings are value-relevant to market values, we will observe a significant association between E" and the market returns . That is, the slope coefficients for differential earnings in Model 1 .2 or in Model 2 .2 should be significant. In addition, we expect to observe positive relationships between earnings and returns . 20 Therefore, the null and alternative for the first hypothesis test are : H1 0 : a" <_ 0 in Model 1 .2 or "<_ 0 in Model 2 .2 Hl a : a" > 0 in Model 1 .2 or

> 0 in Model 2 .2 .

One-tailed tests will be employed for testing HI . If the equity market is indifferent to E" and E' with respect to their permanence, quality, and reporting formats, the coefficients of reported earnings (E) and SFAS 13 differential earnings (E") in Model 1 .2 (or in Model 2 .2) should not be significantly different . Thus, the null and alternative for the second hypothesis test are : H20 : a' = a" in Model 1 .2 or P'= (3" in Model 2 .2 H2a : a' # a" in Model 1 .2 or P'* (3" in Model 2 .2 . Two-tailed tests will be employed for testing H2 .

EMPIRICAL ANALYSES Sample Selection Firms included in Moody's Industrial Manual of 1980 were used as the population for the sample . 21 Footnote disclosures were reviewed to identify firms which retroactively adopted SFAS 13 in 1979 or the year prior . In addition, the following criteria were imposed for sample selection : (1) firms must restate net income for at least one year prior to the adoption year; (2) firms must not have any other accounting method change in the year in which SFAS 13 was adopted ; (3) firms



44

C . S . AGNES CHENG and SU-JANE HSIEH Table 1 .

Sample Selection and Their Adoption Years of SFAS 13 Number of Firms

Panel A. Sample Selection Firms adopting SFAS 13 prior to 1979 without another accounting

192 (12)

method change in the same year Firms without restated earnings data Firms without returns data or financial data available on the CRSP or COMPUSTAT tapes Finns with missing daily returns for 10 days or more over the one year test period

(52) (10)

Number of Firms in the Sample

Panel B. The Adoption Year of SFAS 13

Number of Firms in the Sample Notes:

118 Adoption Year"

Number of Firms

1979 1978 1977

45 62 11 118

*Using May as the cutoff month for the adoption year (i.e., if adoption is from 6/1/77 to 5/31/78, the adoption year is 1977 and if adoption is from 6/1/78 to 5/51/79, the adoption year is 1978) . This cutoff criterion is consistent with that of the COMPUSTAT tapes . The testing year will be one year prior to the adoption year .

must have daily returns data available on the CRSP tapes for the testing period (nine months prior to and three months after the fiscal year-end of the SFAS 13 adoption year) ; and (4) firms must have financial data available on the COMPUSTAT tapes . Criterion (1) is imposed so that the earnings impact of capitalized leases recognized in the income statement can be measured, whereas criterion (2) is imposed to avoid the confounding effect from other accounting method changes . Criteria (3) and (4) are imposed to insure data availability . Table 1 reports the selection procedures and distribution of the retroactive adoption years for our sample firms . One hundred and ninety-two firms were identified as SFAS 13 adopters in 1979 or prior years . Sixty-four firms were deleted because restated income was unavailable on the Moody's Industrial Manual, return data were unavailable on the CRSP tapes, or financial data were unavailable on the COMPUSTAT tapes . In addition, 10 more firms were deleted due to missing return data on the CRSP tape over the test year . 22 Thus, the final sample size is 118 firms . Of the 118 firms, 11 adopted the standard in 1977, 62 in 1978, and 45 adopted the standard in 1979 .



th

00 O

N

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46

C . S . AGNES CHENG and SU-JANE HSIEH Variables

Daily return data were collected from the CRSP tapes from nine months prior to and three months after the fiscal year-end of the year in which SFAS 13 was adopted.23 A logarithmic transformation was applied to convert the daily returns to annual returns . Restated income numbers were collected from Moody's Industrial Manual and the original income numbers were obtained from the COMPUSTAT tapes . 24 The earnings numbers were calculated by subtracting preferred dividends from the income number . All earnings variables were scaled by the market value at the beginning of the period . Among the 118 firms selected, 83 had earnings per share data adjusted downward, 30 had no change, and five had earnings data adjusted upward . Regression models 1 .1 and 1 .2, the rank-adjusted earnings models of 2 .1 and 2 .2, and the rank models 3 .1 and 3 .2 were all used to estimate the pooled cross-section sample of 118 firms . To focus on firms with material differential earnings, regression models were also estimated for a subsample with large (in magnitude) E'!t (differential earnings scaled by beginning price) . This subgroup was formed based on the ranking of IE' .t 1 . It consisted of firms with IE" jt l in the top 50 percent of the total sample . 25 Firm Characteristics Table 2 reports descriptive statistics for the variables used in the regression models and selected firm characteristics . The statistics are reported for both the total sample (n = 118) and the subsample (n = 55). The subsample includes firms with IE'~J greater than 0 .002 . The firm characteristics include three measures of size (market value, total assets and net sales), four measures of debt ratios and the accounting rate of return . IE'~t l is right-skewed because it has a maximum value of 0 .086 while the median is only 0 .002 and 0.006 for the total sample and the subsample, respectively . The mean and median of annual returns for the total sample (subsample) are 20 .5 percent (27 .6 percent) and 18.6 percent (24.1 percent), respectively . This indicates that the subsample has higher returns . Higher returns may be due to higher profitability or higher risk. By evaluating E' and E of the total sample and the subsample, it is not apparent that the subsample has higher earnings . Hence, the higher returns for the subsample should be due to higher risk associated with the subsample firms . To verify this assertion, we explore two additional firm characteristics : size and debt ratios, both having a known relationship to risk . Large firms are usually less risky and firms with high debt ratios are, in general, more risky . Table 2 shows that the subsample does consist of smaller firms with higher debt ratios . This finding confirms our assertion that the higher returns of the subsample as compared to those of the total sample are more associated with higher risk than with higher profitability .



Earnings Impact of Lease Capitalization

Table 3.

47

Basic Regression Analyses

Panel A . Total Sample, Number of Observations = 118

MODEL

Adj. R2

Intercept Coefficient

Coefficient of E'

Coefficient of E"

Test of H2

a"

Test of H2: a'= a"

1 . Linear Models

1 .1

10.04% t-stat. : p-value:

1 .2

9 .26% t-stat. : p-value:

a0 0.085

a' 0 .782

2.014 (0 .046) 0.083 1 .825 (0 .071)

3 .749 (0 .000) 0 .787 3 .678 (0.000)

-0 .236 -0 .101 (0 .460)

F value 0 .183 p Value (0 .670

2. Rank-adjusted earnings Models

2.1

17 .82% t-stat .: p-value:

2.2

17 .20% t-stat .: p-value:

30 0.027 0 .618 (0 .538) 0 .037 0 .711 (0 .479)

(3' 1 .161 5.136 (0.000) 1 .161 5.116 (0.000)

(3"

2.214 0 .357 (0.361)

Test of H2: (3'=13"

F Value 0 .029 p Value (0 .865)

3. Rank Models 'y0 3 .1

3 .2

Y

t-stat . : p-value:

-0 .031 -0 .587 (0 .558)

0.467 5.136 (0.000)

t-stat . : p-value:

-0 .042 -0 .685 (0 .495)

0.467 5.116 (0.000)

17 .82%

17 .20%

Y,

0.038 0.357 (0.361)

Test of H2 :1'=Y'

F value 9.338 p Value (0 .003)

Panel B . Large Capital-Lease Impacted Sample, Number of Observations = 55 1 . Linear Models

1 .1

6 .65% t-stat . : p-value:

1 .2

6 .10% t-stat . : p-value:

aO 0 .182 2 .831 (0 .007) 0 .213 2 .858 (0 .006)

a' 0.603 2.202 (0.016) 0.545 1 .926 (0.030)

a"

Test of H2 : a'= a"

2.497 0.831 (0.205)

F value 0.400 p value (0 .530)

2 . Rank-adjusted earnings Models

2.1

12 .14% t-stat .: p-value:

2 .2

15 .44% t-stat .: p-value:

30 0 .099 1 .291 (0 .202) 0 .214 2 .143 (0 .037)

(3' 1 .029 2.908 (0.003) 1 .045 3.011 (0.002)

a"

24.790 1 .752 (0.043)

Test of H2 : P'= P"

F value 2.817 p value (0 .099) (continued)



48

C . S . AGNES CHENG and SU-JANE HSIEH Table 3.

(Continued)

Panel B . Large Capital-Lease Impacted Sample, Number of Observations = 55 MODEL

Intercept Coefficient

Adj. R2

Coefficient of E'

Coefficient of E"

Test of H2

Y

Y'

Test of H2 : y' =Y"

0 .414 2.908 (0 .003) 0 .421 3 .011 (0 .002)

0 .427 1 .752 (0 .043)

3 . Rank Models 3.1

12 .14% t-stat . : p-value :

3 .2

15.44% t-stat .: p-value:

Notes :

TO 0 .048 0 .523 (0 .603) -0 .068 -0 .611 (0 .544)

F value 0 .001 p Value (0 .983)

Refer to Appendix B for model description and variable definitions. The dependent variable is defined as accumulated returns over a 12-month window starting from the fourth month in the fiscal year till the third month after the end of the fiscal year. E : Reported earnings available for common stockholders prior to the adoption of SFAS 13 scaled by the beginning market price . E" : SFAS 13 differential earnings (restated earnings of year t reported in the adoption year of SFAS 13 subtract reported earnings of year t ; t is the year prior to the adoption .) One-tailed tests are used for the coefficients of E' and F, and a two-tailed test is used for hypothesis H2.

EMPIRICAL RESULTS Basic Regression Analyses Panel A of Table 3 reports regression results of the total sample using linear models (Model 1 .1 and 1 .2), rank-adjusted earnings models (Model 2 .1 and 2 .2), and rank models (Model 3 .1 and 3 .2) . The coefficients of E' have a value of 0 .782 and 0 .787 for Model 1 .1 and 1 .2, respectively, and are significant at the 0 .01 level . The coefficient of E" in Model 1 .2 is not significant . Moreover, the adjusted R2 of Model 1 .2 decreases from 10 .04 percent to 9 .26 percent. These results imply that SFAS 13 differential earnings do not have value implications . Thus, hypothesis 1 (H1) is not rejected . When the rank-adjusted earnings models (Model 2 .1 and 2 .2) are used, the adjusted R2 increased significantly (almost doubled) and the t-statistics of the coefficients are all strengthened . The coefficients of E' in Model 2 .1 and 2 .2 are also positively significant and are larger than those of Model 1 .1 and 1 .2 (that 0'= 1 .161 for both models) . The coefficient of E" in Model 2 .2 (i .e ., P') has a positive value of 2 .214 . However, it is still not significant (with a p-value of 0 .361) . In sum, although the rank-adjusted earnings models perform better than the linear models, H1 is not rejected . The equality tests of the coefficients for E' and E" have a p-value of 0 .670 for Model 1 .2 and 0 .865 for Model 2 .2. Thus, we also fail to reject hypothesis 2 (H2) . Our findings that the two coefficients of the earnings components (i .e ., E' and E")



Earnings Impact of Lease Capitalization

49

do not differ significantly and the insignificance of the coefficient of E" imply that the aggregated earnings number E contains similar information as its major component E'. The implication of these results for accounting policy making is that reporting E has a similar effect as reporting E' . The rank models Model 3 .1 and 3 .2 generate the same adjusted R 2 and t-statistics as those of Model 2 .1 and 2.2, respectively ; however, the equality test (of y' and y") yields different results . The coefficient of y'(0 .467) is significantly larger than that of y" (0 .038) . In the rank models, the rank measures are standardized by dividing the original rank measure by the number of observations so that the rank measure has a value that is between 0 and 1 . Hence, the meanings of coefficients y' and y" are : if earnings levels of E' and E" increase from the smallest to the largest in the sample, market returns will increase by 0.467 and 0 .038, respectively . The larger y' implies that the major earnings component E' has more impact on market returns than the differential earnings E" since the formal is the major portion for firm evaluation . In sum, when using the total sample, coefficients of E" were not significant regardless of which model was used . This implies that the SFAS 13 may not have value relevance to returns from the earnings perspective . In addition, the coefficients of E' and E" were not significantly different except those in the rank model . This implies that earnings adjustment as required by SFAS 13, on average, does not improve value relevance and may even add noise . Although our results are not significant, the coefficient of E" is in the correct direction and is larger than that of E' when using the rank-adjusted earnings model . We believe that the value-relevance of SFAS 13 differential earnings should be higher for firms being affected more by SFAS 13 . Hence, we conducted similar analyses for a subsample with observations that have the differential earnings in the top 50 percent of the total sample . Observations are first ranked based on the magnitude of deflated differential earnings (i .e ., (E'~t I) and the 55 firms with IE',.t l greater than 0 .002 are selected . Panel B of Table 3 reports the regression results for the subsample. The coefficients of E' are 0 .603 and 0 .545 in Model 1 .1 and 1 .2, respectively, smaller than their counterparts in Panel A for the total sample . In addition, the adjusted R e s are approximately 3 percent lower than those reported for the total sample in Panel A . 26 The coefficient of E" in Model 1 .2 has a positive value of 2 .479 ; however, it is still not significant . The equality test of coefficients a' and a" is also not significant (with a p-value of 0 .530) . Thus, we fail to reject H1 and H2 when applying linear models for the subsample . Different results were observed when applying the rank-adjusted models (Model 2 .1 and 2 .2) for the subsample . Although the coefficients of E' in Model 2 .1 and Model 2 .2 for the subsample (1 .029 and 1 .045, respectively) are still smaller than those of the total sample (1 .161 for both Model 2 .1 and 2 .2), the coefficient (3" is much larger (24 .790) and is significantly positive at the 0 .05 level. Thus, H1 is rejected . The equality test of (3' (1 .045) and (3" (24 .790) has an F-value of 2 .817 with ap-value of 0 .099 . Hence, H2 is rejected at the 0 .10 level . 27 For models Model 3 .1 and 3 .2 . significant coefficients were observed for w' and



50

C . S . AGNES CHENG and SU-JANE HSIEH

y". However, the equality test indicates no difference in the coefficients for E' and E"

In sum, when rank-adjusted earnings models are applied to a subgroup of firms with material differential earnings, H1 is rejected at the 0 .05 level and H2 is rejected at the 0 .10 level . These results imply that E" is important for firms with a large SFAS 13 impact . For them, not only are SFAS 13 differential earnings value-relevant to returns but their relevance tends to be higher than that of pre-SFAS 13 earnings . This may imply that E" has higher earnings permanence than earnings from other sources, consistent with the view that E" is affected by long-term capital investments . The implication of our finding to accounting policymakers is that SFAS 13 has significance to firm value through its effect on earnings. Linear versus Rank-Adjusted Earnings Models The value-relevance of differential earnings can be detected by using the rank-adjusted earnings model as reported in the previous section . While the rank adjustment may be taken as an instrumental variable to correct the measurement errors of the original variables, it is not apparent whether the rank adjustment is appropriate (i .e ., reduces the measurement error of earnings or improves the quality of earnings) for both E' and E" variables . Bottom-line earnings (i .e ., E) tend to have measurement errors due to large accrual adjustment and management manipulations . Therefore, a rank adjustment may be necessary to correct the measurement errors . This may not be true for SFAS 13 differential earnings . To study the necessity of transforming the differential earnings, we use various models with both the original earnings and the rank-adjusted earnings measures . The coefficients of the original earnings and the rank-adjusted earnings are summed to estimate the true coefficients of earnings as suggested by Brown and colleagues (1987) and Cheng and colleagues (1996) . The rank transformation of E' is included in all models listed below while the treatment of E" varies among the models . 28 Model 4 .1 : Rp t = CO, + a' E'~ t + (3' RE jt + µlit

(11)

Model 4 .2 : Rp t = C02 + a' E'jt +a" E" + P'RE' .t + µ2 t

(12)

. '.t +(3 " RE'~t + µ3 t Model 4.3 : Rpt = C0 3 + a' E'J.t + 1i' RE'

(13)

Model 4 .4 : Rpt = C04 + WE st + a" E'~ t + (3' RE jt + R" RE'~t +µ4 t

(14)

Model 4 .1 is the basic model with both the original and rank-adjusted measures of E', Model 4 .2 adds the original measure of E'~1 , Model 4 .3 adds the rank-adjusted measure of E" and Model 4.4 includes both the original measure and rank-adjusted measures of all earnings .



51

Earnings Impact of Lease Capitalization

Table 4.

Earnings and Rank-adjusted Earnings Combined Models Panel A. Total Sample, Number of Observations = 118

MODEL

Adj. R2

Intercept Coefficient

4 .1

18 .29% t-stat . : p-value : 18 .60% t-stat. : p-value: 17 .75%

0 .027 0 .619 (0 .537) 0 .040 0 .899 (0 .370) 0 .040

t-stat. : p-value :

0 .782 (0 .436) 0 .042 0 .820 (0 .414)

4 .2

4 .3

4 .4

Model 4 .1

4 .2

4 .3

4.4

17 .89% t-stat. : p-value:

a'/a" -0 .545

-0 .760

-0 .565

-0 .759

2.766

(i'/(3" 1 .706 F-value p-value 1 .917 F-value p-value 1 .726 F-value p-value 1 .915 F-value p-value 0 .534 F-value p-value

Coefficient of E'

Coefficient of E"

a' -0.545 -1 .290 (0 .100) -0.760 -1 .660 (0 .050) -0 .565 -1 .330 (0 .094) -0 .759

a"

2.836 1 .201 (0.116)

-1 .650 (0 .051)

Test I a'=(3'?l a"= (3"

Coefficient of RE'

Coefficient of R E"

R'

R"

2.766

1 .706 3 .566 (0 .001) 1 .917 3 .768 (0 .000) 1 .726 3 .583 (0 .001) 1 .915

3 .023 0 .487 (0 .314) 0 .534

1 .095 (0 .138)

3 .745 (0 .000)

0.081 (0 .468)

a'+p'

a"+p"

Test 3 Test 2 (test of H2) (test of 111) (x'+ P' a"+(3"=0? =a"+a"?

1 .161 6.639 (0 .011) 1 .157

2 .836

8 .088 (0 .005)

1 .442 (0.116) 1 .161

3 .023

6 .774 (0 .011) 1 .156

0 .237

0 .090

(0.314)

(0 .765)

0 .283

0 .119 (0 .731)

3 .300

7 .981 (0 .006)

(0.298)

0 .081 (0 .776)

Panel B. Large Capital-Lease Impacted Sample, Number of Observations = 55

a' 4.1

4 .2

4 .3

11 .53% t-stat . : p-value : 13 .65% t-stat . : p-value : 16 .64% t-stat . : p-value :

0 .074 0 .897 (0 .374) 0 .107 1 .260 (0 .214) 0 .196 1 .958 (0 .056)

-0 .490 -0 .800 (0 .214) -0 .841 -1 .300 (0 .100) -0 .814 -1 .320 (0 .096)

a"

4 .539 1 .508 (0 .069)

0 .500 (0 .481)

R'

R„

1 .616 1 .981 (0 .027) 1 .982 2.354 (0 .011) 2.025 2.479 (0 .009)

29.740 2 .046 (0 .023)



52

C . S . AGNES CHENG and SU-JANE HSIEH Table 4.

(Continued)

Panel B. Large Capital-Lease Impacted Sample, Number of Observations = 55 MODEL

Adj . R2

Intercept Coefficient

4 .4

15 .00% t-stat . : p-value :

0 .192 1 .824 (0.074)

-0 .836 -1 .300 (0 .100)

p'/P"

Test 1 a'=(3'?/ a"=(3"

Model 4 .1

4 .2

4 .3

4.4

a%a" -0.490

-0.841

-0.814

-0.836

0.548

Notes:

1 .616 F-value p-value 1 .982 F-value p-value 2 .025 F-value p-value 2 .043 F-value p-value 27.850 F-value p-value

Coefficient Coefficient Coefficient Coefficient of E' of E" of RE' of RE" 0.548 0.130 (0.449)

a'+R'

2 .043 2 .443 (0 .009)

all +p"

27.850 1 .346 (0 .092) Test 3 Test 2 (test of H2) (test of 111) &+ 0' a"+p"=0? =a"+(3"?

1 .126 2 .285 (0 .137) 1 .141

4 .539

3 .758 (0.058) 1 .211

1 .207

1 .263 (0 .266)

4 .185 (0.023)

3 .870 (0 .055)

2.496 (0.060)

2 .300 (0 .136)

29 .740

4 .118 (0.048) 3.967 (0 .052)

2 .273 (0.069)

28 .398

1 .311 (0.258)

Refer to Table 3 for references and to Appendix B for model descriptions and variable definitions. One-tailed tests are used for the coefficients of E', E" and hypothesis H1 . Two-tailed tests are used for hypothesis H2 . The sum of coefficients of the original earnings measure (a' and a") and the rank-transformed earnings measure ((3' or 13") are used to estimate the return-earnings association . For Models 4 .2 and 4 .3, only one coefficient for E" is included (a" or p" respectively) .

Table 4 shows the regression results for these models and provides results of three sets of tests . The first set of tests investigates whether coefficients of the rank-adjusted measures equal those of the original measures (i .e., whether a' = (i' for Model 4.1 to 4 .4 and whether (x " = (3" for Model 4 .4) . The second set of tests is to test H1 : if the coefficient of E" (i .e., a" in Model 4 .2, R" in Model 4 .3 and a "+p " in Model 4 .4) is greater than or equal to zero . The third set of tests is to test H2 : if the coefficient of E' (i .e ., a' in Model 4 .2, R' in Model 4 .3 and (X'+(3' in Model 4 .4) equals that of E" (i .e ., a", in Model 4 .2, R" in Model 4 .3 and (X "+(3" in Model 4.4) . Panel A of Table 4 reports results for the total sample . Comparing the results to those in Panel A of Table 3, we find that the combined models (with both the original and rank-adjusted measures of earnings) often perform better than models



Earnings Impact of Lease Capitalization

53

with only one type of variable (with either the original or the rank-adjusted measure, not both) in terms of explanatory power. Among these models, Model 4 .2 with E" treated as the original measure has the highest explanatory power . This implies that rank transformation does not improve the measurement errors contained in E" . The equality tests for the coefficients of original versus adjusted E' (i .e ., Test I of the equality between Wand R) are rejected for all the models . However, the equality test of a" versus R" for E" (i .e., Test 1 of the equality between a" and R' ; available only in Model 4 .4) is not rejected . Although all the combined models in Panel A of Table 4 perform better than the models reported previously, H 1 and H2 are still not rejected . (see test results of Test 2 and Test 3 in Panel A) . Panel B of Table 4 reports the regression results of the subsample applying Model 4 .1, 4 .2, 4.3, and 4 .4 and the three test results . Similar to the results of the total sample, the equality tests for the coefficient of the original measure and the rank-adjusted measure are rejected for all models (Model 4 .1 to 4 .4) regarding the equality test of a' and R'. For the equality test of a" and (3 " (available only for Model 4 .4), the p-value is not significant (p = 0 .258) . This implies that p" (the coefficient for the rank-adjusted E") is not statistically greater than its counterpart a" (the coefficient for the original measure of E"). Nevertheless, this panel indicates that the best performing model is Model 4 .3 with E" transformed by ranks (with an adjusted R2 of 16 .64 percent compared with an adjusted R 2 of 13 .65 percent for Model 4 .2 in which the E" is not transformed) . Thus, although the equality test of a" and (3" (in Model 4.4) indicates that the coefficient of rank-adjusted E" is not statistically greater than that of the original E", the significant improvement on the adjusted R2 when the rank-adjusted E" is used (i .e ., Model 4 .3 versus Model 4 .2) leads us to conclude that a rank-adjustment for E" is necessary when the magnitude of E" is large . Moreover, Hl (i .e ., test of (X"+(3"=0 as in Test 2) is rejected for all the models at either the 0 .10 or the 0.05 significance level . However, H2 (test of (x'+P'=a"+R" as in Test 3) is only rejected for Model 4 .3, which contains rank-adjusted differential earnings, with a p-value of 0 .055 . In sum, we find that the rank transformation is necessary for E' but it may not be necessary for E" except when the differential earnings (E") is large in magnitude . While the performance of the combined models (both the original earnings and the rank-adjusted earnings) is better than the models using only one measure of earnings (i .e ., the original earnings or the rank-adjusted earnings), the results reported in the previous section are not altered . Control for Size and Debt Ratio From previous analyses, it is apparent that the return-earnings relationship of large-SFAS 13 impact firms differs from that of small-SFAS No . 13 impact firms. For example, Table 3 reports that the adjusted R2 and the coefficient of earnings in Model 1 .1 for the subsample (6 .65 percent and 0 .603, respectively) are less than those of the total sample (10 .04 percent and 0 .782 . respectively) . This phe-



54

C . S . AGNES CHENG and SU-JANE HSIEH

nomenon persists for various models . The descriptive statistics of firms indicate that firms in the subsample are smaller firms with larger debt ratios . Previous studies have shown that firm size and debt ratio of firms affect the return-earnings relationship . To test the robustness of our results, we add slope dummies to control for these two firm-specific characteristics : size and debt ratio . Size is measured by end-of-year market value adjusted for inflation and debt ratio equals long-term debt scaled by the end-of-year market value of common equity . Model 5 .1 : R/t

=

7101 + a'E Est + a ;yf DM E'/t + aD DD E st +a"EE'~t+a"M DM E"J +a'DDD E"jt +vljt

Model 5 .2 : Rlt = x102 + P E RE' .t +

• Model 5 .3 : Rlt

=

(15)

M DM RE jt + R D DD RE jt

+ v2.jt

(16)

+a"EE jt+a "MDME'jt+a"D DD E" • WE REjt + P 'm DM RE.jt + WD DD RE'. • R „E RE' jt + (3 "M DM RE'~t + R'D DD RE' jt + v3jt

(17)

P "E RE". + P "M D M RE" . t + P "D DD RE j t

7103 + a'E Elt + a'M DM E'1t + o CD DD E' .

Where : E''jt E" .

= reported earnings and differential earnings as defined before,

RE jt RE"~t

= rank-adjusted earnings and differential earnings as defined

before, DM

= equals 1 when the inflation-adjusted market value is above the median, 0 otherwise,

DD

= equals 1 when the debt ratio (long-term debt/end-of-year market value) is above the median, 0 otherwise,

a'E (XM (XD

= coefficients of original measures of reported earnings : E', DME' and DD E',

(x "E (x "M cc "D

= coefficients of original measures of differential earnings : E", DME" and DD E",

WE R M WD

P "E P "M P "D

= coefficients of rank-adjusted measures of reported earnings : RE', DM RE' and D D RE',

coefficients of rank-adjusted measures of differential earnings : RE", DM RE" and DD RE".

Model 5 .1 uses the original measures only, Model 5 .2 uses the rank-adjusted earnings only, and Model 5 .3 uses both original and rank-adjusted measures .



55

Earnings Impact of Lease Capitalization

Table 5.

Models with Slope Dummies for Size and Debt Ratio

For the Total Sample, Number of Observation = 118

MODEL

Model5 .1

Model 5 .2

Model 5 .3

Intercept Adj. R2 Coefficient

16 .17% 16 .17% 16 .17% 15 .49% 15 .49% 15.49% 24.69% 24.69% 24.69%

0 .051 1 .069 0 .288 0 .025 0 .457 0 .649 0 .010 0 .177 0 .860

Model 5 .2

Coeff. of E'

Coeff. of DME'

Coeff. of D D E'

Coeff. of E"

aE '

am,

aD'

aE

11

11

am

Coeff. of DDE" aD11

0 .976 3 .402 (0 .001)

-0 .497 -1 .570 (0 .119)

-0 .037 -0 .131 (0 .896)

-1 .730 -0 .543 (0 .588)

-54 .800 -3 .480 (0 .001)

3 .827 0 .885 (0 .378)

1 .205 1 .263 (0 .209)

-1 .540 -1 .460 (0 .147)

-1 .910 -1 .820 -(0.072)

-3 .900 -0.836 (0 .405)

-42 .100 -2 .610 (0.011)

8 .122

Coeff. of RE"

Coeff. of DM RE "

Coeff. of RE '

Coeff. of DM RE '

Coeff. of DD RE '

$E'

PM,

PD '

PE "

-0.268

8.515

-0.766 (0.445) 1 .796 1 .639 (0 .104)

0.865 (0 .389) 4.794 0 .443 (0 .659)

1 .282 3 .971 (0 .000) 0 .032 0 .032 (0 .975)

Model 5 .3

Coeff. of DME"

0 .140 0 .386 (0 .700) 1 .312 1 .183 (0 .239)

PM -5 .170 -0 .480 (0 .632) 0 .002 0 .000 (1 .000)

1 .420 (0.158) Coeff. of DD R E"

PD" -9.700 -0.896 (0 .372) -11 .500 -0.988 (0 .325)

Test of HI : d'+/3„=0, Test of H2 : d+/3,=a"+/3" Dm=1, De=O Large/Low Debt

Dm=O, De=1

Dm=1, De=1

Small/High Debt

Large/High Debt

al. +R„

a, +(3 ,

a"+(3"

a, + (3'

a'+G3'

-1 .73 (0.588) (0.411) 8 .515 (0.389) (0.461) 0.894 (0.931) (0 .973)

0 .479

-56 .53 (0 .001) (0 .001) 3 .345 (0 .714) (0 .832) -41 .204 (0.021) (0.018)

Dm=O, De=O Small/Low Debt a, +(3'

a"+(3"

a"+ (3"

Model 0 .976 Model 5.1 Test of H1 Test of H2 Model 5.2 1 .282 Test of H1 Test of H2 Model 5 .3 1 .237 Test of H1 Test of H2

1 .422

1 .009

0 .939

1 .014

1 .123

2.097 (0 .513) (0 .715) -1 .185 (0 .907) (0.827) -2.484 (0.813) (0.727)

0 .442

1 .154

0.895

-52.703 (0.001) (0.001) -6.355 (0.553) (0 .478) -44.582 (0 .020) (0 .017)

Table 5 reports the regression results and the results of two sets of tests : one for Hi and the other is for H2 using the total sample .29 Regression results of coefficient values, t-statistics and p-values are reported in the first part of the table ; the second part of the table reports the test results for hypotheses HI and H2 . When



56

C . S . AGNES CHENG and SU-JANE HSIEH

size and debt are added to control for the differences in earnings response coefficients among firms, adjusted R2 increased to 24 .69 percent for the combined model with both measures of earnings (i .e ., Model 5 .3) . For both the linear model (Model 5 .1) and the combined model (Model 5 .3), H1 and H2 are rejected for large firms regardless of the level of debt . 30 The results in Table 5 suggest that a heterogeneous sample can weaken the results, and an absence of controls for firm characteristics may be one reason why we did not detect value-relevance for the total sample . Previous studies also show that return-earnings relations differ among industries ; hence, we could add control variables for industries to strengthen our results . However, since our sample is small and the degrees of freedom will be low if we add industry dummies (we already have 12 variables in Model 5 .3), we choose not to add such controls .

SUMMARY AND CONCLUSIONS Previous studies have examined the effects of lease capitalization either by studying the capital market's reaction around the event dates associated with the announcement of ASR No. 147 or SFAS 13 (Ro 1978 ; El-Gazzar 1993), by studying whether capital lease information is used by investors in assessing market risk (Bowman 1980 ; Kuo 1988 ; Murray 1982 ; Finnerty, Fitzsimmons, and Oliver 1980), or by studying the actions taken by managers to offset the adverse impact of SFAS 13 on some financial ratios (Abdel-Khalik et al . 1981) . Our study applied return-earnings models to investigate whether the impact of SFAS 13 is significant for firm valuation from the earnings prospective . Two hypotheses are tested : (1) the differential earnings of SFAS 13 (E") do not have value relevance (hypothesis 141) and (2) the SFAS 13 differential earnings (E") do not have different value-relevance from pre-SFAS 13 earnings (E) (hypothesis H2) . Based on linear return-earnings models, we fail to reject both H1 and H2 . Previous studies have reported that return-earnings relation is nonlinear and without controlling for nonlinearity, statistical results can be biased and unstable . We evaluate rank models as suggested by Cheng and colleagues (1992) and some S-shaped models as suggested by Freeman and Tse (1992) and find that rank models perform the best . Accordingly, we use rank as an instrumental variable to correct the measurement errors of the original earnings measures . One criticism of the rank models is that the coefficients do not have traditional economic meanings as the conventional earnings response coefficients do . Thus, an equality test of the coefficients of the' earnings components (H2) cannot be conducted. To overcome this problem, we develop rank-adjusted earnings models to transform earnings based on their ranks. This procedure not only maintains the statistical power of the rank models but also provides meaningful coefficients . Based on the rank-adjusted earnings models, we reject H1 at the 0 .05 significance level and H2



Earnings Impact of Lease Capitalization

57

at the 0 .10 significance level but only for firms that are largely affected by SFAS 13 . In addition to the basic linear and rank-adjusted earnings models, we also explore the effectiveness of rank-transformation for both E' (the pre-SFAS 13 earnings) and E" (the differential earnings) variables . We find that the rank transformation is necessary for E' but may not be for E" except when the magnitude of E" is large (refer to Table 4) . Another interesting finding is that when we control for firm size and debt ratio, the rank transformation does not perform better than original measures (by comparing the adjusted R2 of Model 5 .1 and Model 5 .2 in Table 5) . However, we find that models with both the original and the rank-transformed measures (i .e ., Model 5 .3) outperform models with only one type of variables (i .e ., just the original variable as in Model 5 .1 or just the transformed variables as in Model 5 .2) . Our results support Lev's (1989) suggestion that model misspecification may be one of the main reasons inducing low adjusted R2 in return-earnings models . Although our main purpose is to test the value-relevance of SFAS 13 affected earnings, the development and application of our non-linear models contribute to the clarification of some model specification problems discussed in previous literature and should enhance future research on return-earnings relationship .

APPENDIX A Summary of Accounting Standards Related to Reporting and Disclosure of Information Concerning Leases Accounting Principles Board (APB) Opinion No . 5: Reporting of Leases in Financial Statement of Lessee This standard was effective in 1964 and was superseded by Statement of Financial Accounting Standards No . 13 in January 1977 . This standard provides criteria for lease capitalization . It also requires that a long-term noncancelable lease be capitalized only if it is, in substance, an installment purchase of a property . When this criteria is applied literally, it only required lease contracts containing an automatic transfer of ownership be reported as capital leases . During the effective period of APB No . 5, most lease contracts were written in a way so as not to be qualified as capital leases even though they are in fact equivalent to purchases .



58

C . S . AGNES CHENG and SU-JANE HSIEH

APB Opinion 31 : Disclosure of Lease Commitments by Lessees.

This standard was effective in 1973 and was superseded by SFAS 13 . The purpose of this standard is to improve information disclosure concerning leases . It required the lessee to disclose the aggregate minimum rental commitments for all noncancelable leases for each of the five succeeding fiscal years, each of the next three five-year periods, and as a single amount for the remaining period. This standard also required that the amounts be classified by major categories of properties . Accounting Series Report (ASR) No . 147: Notice of Adoption of Amendments to Regulation S-X Requiring Improved Disclosure of Leases .

This standard was issued by the Securities and Exchange Commission (SEC) in 1973 . In addition to requiring similar rental commitment disclosures as those of APB Opinion 31, ASR No. 147 also required the disclosure of the present values of all noncancelable financing leases, the implicit interest rate used in deriving the present value, and the impact on net income of the capitalization of such leases . Thus, the income effect of all noncancelable leases and their financing arrangements (i .e ., the present values of the leased assets and the obligations) became available via the disclosure requirement of ASR No . 147 . A materiality constraint was imposed in this standard . That is, the disclosure of present value (or income effect) numbers is required only if the magnitude of present value (or income effect) exceeds 5 percent of long-term capitalization (or 3 percent of average net income for the most recent three years) . During the grace period of SFAS 13 (1977-1980), lessees were required to continue disclosing lease related information based on the disclosure requirement of ASR No . 147 . Statement of Financial Accounting Standard (SFAS) No. 13 : Accounting for Leases .

Prior to the effective date of the SFAS 13, the reporting of leases was based on the criteria set by APB Opinion No . 5 which only required a lease be reported as a capital lease when an automatic ownership transfer was in the contract . SFAS 13 (effective in January 1977) mandates that leases are to be reported as capital leases if one of the following four criteria is met : (1) a lease contract provides for a transfer of ownership ; (2) the contract contains a bargain purchase option ; (3) the lease term is 75 percent or more of the remaining useful life of the leased asset ; or (4) the present value of the future minimum lease payments is at least 90 percent of the fair market value of the leased asset . The underlying concept of SFAS 13 for lease capitalization is the transfer of substantial risks and benefits . Although SFAS 13 provided clearer guidance for reporting purposes, it reduced the disclosure information concerning leases . It only required the lessee to dis-



Earnings Impact of Lease Capitalization

59

close the future minimum lease payments for all noncancelable leases in the aggregate for each of the five succeeding years and one single amount for the remaining period . Many leases which were reported as operating leases under APB Opinion No . 5 may have to be reported as capital leases when applying SFAS 13 retroactively . Due to the concern of the adverse impact on certain financial numbers, the FASB gave a 4-year grace period (1977-1980) to apply SFAS 13 retroactively for leases entered prior to December 31, 1976. This transaction period allowed companies to renegotiate terms of some leases so that they would not be capitalized under SFAS 13, and to restructure debt and capital to avoid possible technique default . SFAS 13 required similar disclosures as those of ASR No . 147 for noncapitalized capital leases during the grace period prior to the retroactive application of the standard . Thus, the income effect of SFAS 13 (i .e ., the differential earnings) can be inferred by market participants from the footnote disclosure during the grace period .

APPENDIX B Summary of Models Linear Models : Model 1 .1 : Rjt = a0 1 + a' E'j + E1 jt Model 1 .2 : Rjt = a02 + a' E''3 + a" E' Jt + e2jt Rank-adjusted earnings Models: Model 2 .1 : Rjt = (30 1 + R' REjt + 91jt Model 2 .2 : Rjt = (302 + (3' RE jt + (3" RE" . r+ 92jt Rank Models : Model 3 .1 : Rjt = y0 1 + ' Rank'1 + 91jt Model 3 .2 : Rjt = y02 +'y Rank'. + y" Rank"Jt + 02jt Combined Earnings and Rank-Adjusted Earnings Models : Model Model Model Model

4 .1 : 4 .2 : 4 .3 : 4 .4 :

Rjt = C0 1 + a' E'j t + 3' RE jt + µljt Rjt = CO2 + a' E ' + a" E' Jt + (3' RE jt + µ2jt Rjt = C03 + a' E'jt + (3' RE jt +(3 " RE' Jt + µ3jt Rjt =C04 + a' E jt + a" E' Jt + (3' REjt + (3" RE'Jt + µ4jt



60

C. S . AGNES CHENG and SU-JANE HSIEH

Combined Models with Size and Debt Control: Model 5 .1 : Rjt=710 1 +WEE'. +aMDiM E jt +aDDD E jt +a"EE"t+a'M DM E"+a'DD D E ;t +vljt

Model 5 .2 : Rjt= 11 02 + F''E RE' . t + P 'M DM RE jt + O 'D DD RE jt + R "E RE' Jr + R "M DM REJ t + R "D DD RE'~t + v2jt

Model 5 .3 : Rjt= 7103 + WE Ejt + a'iY1 DM E jt + a'D D D E jt + a "E E'~t + a'M DM E' Jt + a' DD E' Jt + WE RE'jt + P 'M DM RE'jt + ~3 D DD RE' . 1 +R"ERE jt+P "f DM RE. +a"DDDREJt+v3jt

Notes : E' . t E'Jt

RE' RE" DM

DD

= the reported accounting earnings of year t available for common stockholders for firm j scaled by beginning market value, 31 = SFAS 13 differential earnings for firm j in year t (subtracting E' from the adjusted accounting earnings E as required by SFAS 13), scaled by beginning market value, = Rank-adjusted earnings and differential earnings defined previously . = Equals I when the inflation-adjusted market value is above the median, 0 otherwise . = Equals 1 when the debt ratio (long-term debt/end-of-year market value) is above the median, 0 otherwise .

ACKNOWLEDGMENTS We wish to thank Kenneth R . Ferris, Scott Jerris and an anonymous reviewer for their helpful comments . Any errors remain the responsibility of the authors .

NOTES 1 . Imhoff, Lipe, and Wright (1991,1993,1997), Ely (1995), Bowman (1980), Kuo (1988), Finnerty, Fitzsimmons, and Oliver (1990), and so forth. 2 . Imhoff, Lipe, and Wright (1991) illustrate that the equilibrium of the rental expense and the sum of the depreciation and interest expenses does not occur until past the halfway point in a lease life . 3 . Imhoff, Lipe, and Wright (1991) apply constructive capitalization technique to operating leases of seven pairs of firms in seven industries . When omitting the income effect of capitalization, they find an average decrease in return on assets ratio (ROA) to be 34 percent for firms with high leases and 10 percent for firms with low leases . The impact on the debt equity (D/E) ratio is even more significant . In a later study (ILW, 1997), they incorporate the income effect in studying the capitalization impact on financial ratios . They find that both ROA and return on Equity (ROE) are lower than reported ROA and ROE when adjusting both income effect and balance sheet effect . Nevertheless, these effects could be quite different from only adjusting the balance sheet effect as in ILW (1991) .



Earnings Impact of Lease Capitalization

61

4. Refer to Appendix A for details . 5 . Refer to Appendix A for the requirements . 6. A study conducted by Abdel-Khalik and colleagues (1981) indicates that the lessee samples are four times likely to take some action to reduce the adverse impact of SFAS 13 on financial ratios than the nonlessee (control) samples. These actions include sale of common and preferred stock, retirement of long-term debt and conversion of bonds to stock . These actions would all lead to the reduction of debt/equity ratio and would offset the negative impact of SFAS 13 on the financial ratios . In studying the economic consequences of SFAS 13, Imhoff and Thomas (1988) conclude that most lessees engage in capital structure changes (i .e ., increasing equity or reduce debt) rather than renegotiate contracts affected by lease capitalization to avoid the adverse impact of lease capitalization on financial ratios . The findings of Imhoff and Thomas (1988) corroborate with those of Abdel-Khalik and colleagues (1981) . 7 . Ro (1978) used a control group (no noncancellable lease firms) and a treatment group (firms with noncancellable leases) with similar beta risk to study securities market reaction to these two-group of sample firms during the event period of ASR No . 147 (from June 1973 to March 1974) . 8 . One is the public hearings date of the FASB (November 25, 1974) on which opponents of lease accounting changes failed to convince the FASB of the dangers of tightening lease accounting . The other is June 3, 1976, on which a Wall Street Journal article reported that FASB was back to the drawing board . 9. Even though many studies refer to the coefficient of earnings in the return-earnings association model as the earnings response coefficient (ERC), we choose to use the term "value relevance" instead because the returns are measured over a year and the earnings mainly reflect the underlying information set used by the market (Kothari 1989) . While value relevance is a popular term for testing balance sheet data (e .g ., Barth and Beaver 1996 ; Amir 1996), earnings play an important role in explaining firm value (Ohlson 1995) . 10. SFAS 13 required firms continue to disclose the income effect and present value of all noncapitalized leases as required by ASR No . 147 during the grace period (1977-1980) of SFAS 13 . Also, a few of our sample firms explicitly indicate the potential net income impact of SFAS 13 in year t . 11 . Previous studies have shown that earnings permanence can influence earnings information content or value-relevance (e .g., Ali and Zarowin 1992 ; Cheng, Liu, and Schaefer 1996) . 12 . Earnings measurement is constrained by GAAP, but security prices may reflect information other than current earnings, including expectations of future earnings changes and cash flows . 13 . Both earnings level deflated by beginning-of-period price (e .g ., Biddle and Seow 1991a ; Easton and Harris 1991 ; Kothari and Sloan 1992) and earnings change deflated by beginning-of-period price (e .g ., Beaver, Lambert, and Morse 1980 ; Easton and Harris, 1991) have been widely used as the explanatory variable in return-earnings models ; similar qualitative results are derived regardless as to which model is used . Accordingly, we focus on earnings level specification . 14 . A three-month time lag is given for the returns to reflect the annual earnings . Thus, the return period is from nine months before the fiscal year end to three months after the fiscal year end. This practice is used by Easton and Harris (1991) . 15 . For simplification, instead of expressing the deflated earnings expressively such as E'J. rPit_1 when PJt_1 represents beginning market value/price, we choose to use E'~t (or E'~t,Ejt) to represent the deflated measures used in the models . We use E', E", or E to represent the general term for recognized earnings prior to SFAS 13, inferred capital-lease earnings and the restated earnings required by SFAS 13, respectively . Note that E = E'+E" 16 . To simplify the use of symbols, we do not use different symbols for the coefficients of E' in Model 1 .1 and Model 1 .2 . We follow this pattern of presentation for each set of models that are discussed in this paper . 17 . In Cheng and colleagues (1992), the linearity test was rejected in nine out of I1 test years (from 1975 to 1985), the test for normality in the residuals was rejected in eight out of I I years, and the Rnensch-Paoan-C.ndfrev test of heternscedacticity failed in four of the 11 vears Evidence of



62

C. S. AGNES CHENG and SU-JANE HSIEH

non-linearity and model specification problems of UEERM is pervasive in the Cheng and colleagues (1992) study . 18 . On average, the adjusted R2 improved from 0 .088 (with original earnings forecast error, untransformed) to 0 .141 when the power transformation of the forecast error is used in the UERRM. The adjusted R2 improved more to an average of 0 .172 when rank transformation is applied to the earnings forecast errors . 19 . The functional form of the coefficient can be developed from theoretical models (Christie 1987 ; Kothari 1989 ; Ohlson 1995) . 20. A higher adjusted R2 should also be observed for Model 1 .2 than for Model 1 .1, and for Model 2.2 than for Model 2 .1 . 21 . Accounting Trends and Techniques (ATT) was initially used to identify a sample of firms. The 1977-1981 issues of ATT indicate that there are 97, 98, 33 and four companies with an accounting method change involving leases in response to SFAS 13 for the years 1977, 1978, 1979 and 1980, respectively . However, when Moody's Industrial Manual was used to collect restated earnings data for the prior year, only 54 of those firms were identified as having restated financial statements . In an effort to enlarge the sample size, funis reported in the Moody's Industrial Manual of 1980 (approximately 2700 firms), which contains financial statements of firms for seven, five or two years (thus, a range of 1975 to 1979 was covered), were used as the data base . 22. Although the criterion to delete firms with missing daily returns was set to 10 daily returns, most of these 10 firms had missing returns for more than 200 days . 23 . The test year is the year prior to the adoption year when the income number was restated . The returns period is extended to nine months prior to and three months after the fiscal year-end to correspond with the earnings announcement period. 24. Restated earnings per share was not clearly stated in the Moody's Industrial Manual for a number of sample firms. Consequently, the restated income numbers were collected instead and the restated earnings per share were derived using the restated net income . 25 . Seven firms with E" equaling 0.002 were all dropped from the subgroup . Thus, instead of 59 firms (50 percent of 118 firms), this subgroup has only 55 firms . 26. These results indicate that earnings reported by firms with larger differential earnings have less explanatory power than earnings reported by the rest of the firms in the sample . Table 2 has reported that firm characteristics differ between the total sample and the large capital-lease impact sample . Firm characteristics can affect the return-earnings association . This difference persists for the rank-adjusted earnings models although to a lesser extent . The rank model is not sufficient to take into account the inherent differences of return-earnings relationship for firm with distinct firm characteristics . We will look into this issue by adding debt and size slope dummies as control variables for firm characteristics. 27 . If we hypothesize that the coefficient of E" should be larger than that of E', H2 will be rejected at the 0 .05 level using a one-tailed test . 28 . Since many forms of returns/earnings model are used, Appendix B is provided for reference . 29 . Model 5 .1, 5 .2, and 5 .3 are applied to the total sample only . There are two reasons supporting this decision . First, since significant value relevance for differential earnings has already been found for the subsample without controlling for firm characteristics, controlling for firm characteristics for this subsample will only strengthen the results . Second, the firm characteristics of the subsample firms (with respect to size and debt ratios) are more homogeneous . Thus, there is less need to apply these models to the subsample . 30. We use a one-tailed test for this table . Because we add slope dummies to the model by assuming that the error terms are the same for all observations, our coefficients will be largely affected by this related effect . We find that large firms respond to the differential earnings much less than small firms do . 31 . See note 15 .



Earnings Impact of Lease Capitalization

63

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