The impact of SFAS no. 8 on equity prices of early and late adopting firms

Journal of Accounting and Econotics

li (1959) 35-69. North-Hdinnd

THE IIMPACT OF WAS NO. 8 ON EQUITY PRXCES OF EARLY AND LATE ADOPTING FIRMS AR Events Study and Cms-fiieetimal Anatysis*

Received May 19%. final version received August lQ8S This study examines the economic consequences of S:atement of Financial Accounting Standards No. 8 (SFAS No. 8). Compared to a matched control group. both early adopters and late adopters of SFAS No. 8 exhib!ted significautl~ negative excess returns in the Exposure Draft rcknsc period. even after adjusting for a size effect m January. Weighted Icast-squares and ordinary least-squares cross-sectional regressions were compared. The Iate n.dopter regressions wc,akly support contracting and political cost theories.

1. hetmduction

In 1975 the Financial Accountiug Standards Board (FAST) issued its sorltroversiai Stlstement of Finmcial Accounting Stalldards NO. 8 (WAS No. 8;): ‘Accounting for the Translation of Foreign Currency ‘I‘rmsnctious and Foreign Currency Financial Statements’. Opposition to the provisions of SFAS No. 8 focused on the resulting vofatility in both quarterly md annual net income and the translation of balance sheet accounts.* As a result of this opposition, in 1981 the FASB issued a revised foreign currency accounting standard (SFAS No. 52), which essentially reversed the major provisions of SFAS No. 8. *This paper is based on my Ph.D. dissertation at the University of lowa. I m i:rca:ly indebted to the members of my committee for their advice and encouragement: l?clu$as I&Jong:, hlichacl RozcfC Gerald Salamon. George Woodworth, and especially my chairman. Daniel L‘ol!ins. This paper has benefited from comments h!: Jerry Myers. Jerry Zimmerman. and from the participants of accounting workshops at the University of Arizona, University of British Columbia, University of Florida. University al Iowa, Michigan State University, and the State University of New York at Bull’alo. I also wish to thank William Ricks (the referee) for helpful comments. Any errors OF omissions are entire!y my gespnnsibility. I gratefully acknowledge the linancial support provided by the Ernst and Whinncy Foundation, the Graduate Coltege of the University of 1,nw;\,and the Karl Ellcr Center for the Study or the Private Market Economy at the Uniwrsity of Arizona. Finally, 1 am very grateful for the computer support provided by the University of Iowa. ‘B~sir~ss M/e& (February 13, 1979, p. 48), Cl~errtrcul I~eeL (March 9, 1979. p. 13). Cooper. Fra:;er, and Zchards (197P), Rodriguez (lY99), Megos (i979), and Stabler ti997).

0165SlOl/X9/!$3.5OSl989,

Elsevicr Science Publishers B.V. (North-Iiolland)

36

kl/.K. Salatka, Stoc,k price t$ect of CFAS No. 8

The alleged capital market consequences of SFAS No. 8 included a decline the price-earnings ratio of many multinationa! corporations [Revzin (1976)]. However, studies by Shank, Dilhrd, and Murdock (1979), Dukes (1978) and Makin (1977) suggest no evidence of a capital market reaction to SFAS No. 8. This is somewhat surprising if one believes that multinationals incurred costs for activities that supposedl, xr resulted from SFAS No. 8: hedging, renegotiation of debt from one currency to another, changed foreign investment criteria, and revised managerial performance evaluation/compensation systems.’ Previous studies of the stock price effect of SFAS No. b Jack an explicit theory as to why a capital market reaction would result [see Foster (198O)j. The present study is based on the theory that capital market effects of SFAS No. 8 were a direct function of its financial statement effects through contracts written in terms of accounting numbers and political lobbying. Accordingly, in this study, sample firms were chosen only if they exhibited translation gains or losses from SFAS No. 8. If capital market effects are a function of financial statement effects, the sampling, aggregation, and partitioning procedures used in previous studies obscure both average and cross-sectional capital market effects due to SFAS No. 8. This paper proposes and tests a theory concerning stock price esects of SFAS No. 8 for firms which adopted SFAS No. 8 prior to the required dLte (called ‘early adopters’ in this study) versus firms which waited until the required date (called ‘late adopters’). Both groups of firms were affected and, therefore, were expected to exhibit equity price changes. The theory implies that late adopters relied more than early adopters on foreign currency accounting choices to reduce the effect of translation adjustments. Thus, late adopters were expected to incur costs from contracts written in terms of accounting numbers and political lobbying. The overall testing s’_rategy is in two stages. The first stage compared daily excess returns between experimental groups affected by SFAS No. 8 and a matched control group of firms not affected by SIIAS No. 8. For the second phase, the methodology used was similar to that of keftwich (1981), Collins, RozeffS and Dhaliwal (1981) and Hughes and Ricks (1984), involving regression of excess returns on the variables used as surrogates for contracting costs and political costs incurred by firms forced to change to SFAS No. 8. The most significant excess two-day returns were observed during issuance of the Exposuar Draft of SFAS No. 8 (December 31, 1974 and January 2, 1975) for both early adopters (- 0.04) and late adopters ( - 0.94) compared to the matched control group firms. Both experimental groups continued to exhibit significantly negative excess returns relative to the control group after adjustments were made for the size effect in January. In contrast, positive in

*For example, see Business Week (Januar?, 26,1976. (1976), and Chemical Week (March 9, 1977, p. 13).

p. 48). Seidler (1979). Revzin (1376), Burns

W.K. Sahtka, Stock price efect

of WA %h;o.8

37

excess returns of lesser significance were observed during the release of the e.zrIier foreign currency Discussion Memorandum (February 21, 1974). Crosssectional regressions using independent variables as surrogates for contracting and political costs of late adopters provided weak support for the predictions. Similar cross-sectional results were observed for the early adopter group, suggesting that both groups of firms incurred contractual constraints resulting from SEAS No. 8. This paper extends capital market testing methodology by exploring two adjustments for the size effect in January and by comparing weighted leastsquares (WLS) to ordinary least-squares (OLS) cross-sectional regression. Hughes and Ricks (1984) use seemingly unrelated regression in testing signifcance of critical events. This study supplements the seemingly unreiated regression event period methodology by u&g WLS for testing cross-sectiona! explanations for each event period. The WLS Il:sthod yielded greater numbers of significant cross-sectional regressions than the C1.S method. However, the significance levels of the indiviatial coefficients for WLS regressions were not uniformly greater than the significance of the corresponding coefficients of the 8LS regressions. The remainder of this paper is organized as follows. Section 2 addresses the gaancial statement effects of SFAS No. 8. The distinctionb between the early and late adopting firms are made in section 3. The contracting costs and political costs consequent to and associated with the financial statement effects of SFAS No. 8 are discussed in section 4. The sample selection procedures and event period tests are presented in section 5. In section 6, the results of the cross-sectional regressions are reported. Section 7 contains a summary and discussion.

2. The financial statement effects of SFAS No. 8 SFAS No, 8 increased the volatility of both quarterly and annual net income and exposed balance sheet accounts to exchange rate fluctuations. Prior to SFAS No. 8, the vast majority of companies used translation methods which reduced the financial statement effects of fluctuating exchange rates and, as a minimum, deferred some portion of translation gains or losses [Pakkala (1975)]. SFAS No. 3 required all firms to use the temporal method of translation and to include translation gains and losses in income, thus increasing the potential variability of reported income. Multinational corporations that had previously used historial exchange rates to translate foreign long-term debt were, under SFAS No. 8, required to use current exchange rates. Since the value of the U.S. dollar declined relative to most foreign currencies both before and after the promulgation of SFAS No. 8, the amounts of foreign long-term debt effectively ir:ireased, thereby increas-

38

W.K. Saiarka, Stack price efecr of SF4 S No. 8

ing debt-to-equity ratios. 3 This changed the foreign currency exposure of many multinational firms from a net asset position to a net liability position [Pakkala (I975), Aggrawa! 1’1978), and Gray (197?!. On average, this chacge resulted in negative currency translation adjustments to net income. Prior to SFAS No. 8, many firms had translated foreign inventories at current rates and translated foreign current debt at historial exchange rates [Pakkala (1975)]. Ceteris paribus, changing from current rates to historic-j exchange rates resulted in a decrease in the translated inventory account. Changing the translation of current debt from historical exchange rates to current exchange rates resulted in an increase in current debt. The combined effect of these changes decreased working capital and working capital ratios. Thus, for the aver&e multinational fir-m, SFAS No. 8 had a negative effect on income, increased the variance of income, increased debt-to-equity ratios, and decreased working capital. 3. EarIy versus late adoption of WAS No. 8 SFAS No. 8 was required for fiscal years ending on or after January 1,1976. I-Iowever, early adoption was allowed. In the following discussion, those firms which adopted it prior to that date are referred to as early adopters. Firms which adopted it on or after the required date are called late adopters. Although multinational companies had always had the option of adopting foreign currency accounting provisions similar to SFAS No. 8, the fact tt.at few did indicates it was not an optimal accounting policy chdce. Assuming that prior accounting procedures were part of the optimal contracting technology in the absence of regulation, both early and late adopters would be expected to exhibit stock price decreases as a result of WAS No. 8. Ill,

Price Change: Multinational due to WAS No. 8.

corpa?ations

exhibit stock price declines

Adoption of SFAS No. 8 was, on average, an income-decreasing accounting method choice.4 The early or late timetable for adoption indicates that the costs and benefits of financial statement effects were weighed against other 3Sec Bernstein (1978). U.S.-based multinational invest,ments in France, Germany, Swiherland, 1971.. 1977. the average yearly exchange rate of Fretxh franc, 33% rceiative to the German mark, to the British pound.

corporations had substantial amounts of foreign and the United Kingdom. During the period the U.S. dollar gradually Fell 11% relative to the 41% rclativc to the SPss franc, and 28% relative

4For the sample of lirms discussed below, the sum of the currency translation adjustments standardized by total assets over the years 1977-1980 was - 0.006 for late adopters and - 0.008 for early adopters. A Mann-Whitney U test of this difference is significant at f = 1.67 ( p 2 0.05).

costs and benefits to the

firm.hge fimswere

be

cGsts

faced

with

pokicai

wilich

potentia&j

mire

likely than smsli firms to

couk!

&me&se

under

early

patibus. b&hGL@ the ben&ts from early adoption ivonld be oRset by increased xriabiiiiy uf income due to translation adjlustments. it is likely that large firms were more capable than smalle? firms of dampening the effect of trans’lation adjustments on net income. For example, large firms are more likely than smaller firms to have diversified foreign investments. The cost and complexity of diversifying foreign investments and/or earmarking separate operationa! units to manage foreign exchange exposure is not likely to be as feasible for smaller firms as it is for larger firms. ln t~iditinn, large firms are more likely than smaller firms to hedge foreign exposures if there x1 iiscd costs of hedging. Thus, large firms were more likely to obtain benefits from early adoption than sma!!er firms. adopticrn,

H.2.

cetesis

Size: Multinarionais adopters.

adopting SFhS No. 8 early are iarger than 1atc

The above discussion suggests that firms electing early adoptiou of SFAS No. 8 controlled the variance of translati - I ad_justments and thus benefited from reduced politic,al costs. In addition, by controlling the vsriance of incomz, early adopters were lei s likely to be subject to contractua! constraints written in terms of accounting numbers. Because late adopters were smaller than early adopters, !atc adopters were more likely to rely on foreign currency accounting methods to avoid translation adjustments, given the fixed costs of production, investment, or financing activities. Therefore, the following discussion of the financial statement effects of SFAS No. 8 on contracts written in terms of generally accepted accounting principles and on politic:tE costs apphcs primarily to late adopters of SFAS No. 8.

4. Contracting and politicall cost hypotheses

Bonus plans generally specify the minimum Icvei of earnings ittld somr:timcs also place an upper limit on the amounts added to the bonus pool [Hcaly (19SS)J. SFAS No. 8 decreased income while increasing its variability, so that minimum earnings levels might aot be reached cj~, aiternativeiy. upper limits might 4e exceeded. Bonus payments might then be delayed or pertnanentiy lost. If the compensation committiee did not fully adjust the bonus plan for the financial statement e%x!s of SFAS No. 8; managers had incentives to avoid decreases in t.heir compensation b]; changing production, ir,vestment, and

JO

I+: K Saloikn,

.Src/rk price c$kr

financing activities. These chances manifested in stock price declineg5 14.3.

of SF.4 S ?;c.8

reduce the value of the firm and are

,~u~~~~~~~~~~~~~~~n~~r~~~Agreemettr: Multinationals with earningsbased management compensation plans exkibii greater stock price declines due to SFAS No. 8 than muZeinations without earnings-based compensation plans.

The increased tightness of debt-to-equity ratios, interest coverage ratios, and working capital ratios due to SFAS No. 8 induced managers to take costly actions to avoid a possible default, restrictions on the issuance of additional debt, and/or restrictions in the investment activities of the firm. These costs reduce the present value of future cash flows to equity holders and thus decrease stock price. H.4a.

Debt-to-Equity Ratio: Multinationals with debt-to-equity ratios close to cons,traints specified in bond indenture agreements exhibit greater stock price declines due to SFAS No. 8 than multinationals with less binding debt-to-equity constraints.

IY.4b;

Interest Cooerago Ratio:

Multinationals with ratios of net income to interest charges close to limits specified in bond indeature agreements exhibit greater stock price declines due to SFAS No. 8 than multinationals with less bi;mding interest coverage ratios.

H.4c.

Working Capital Ratio: Multinationals with working capital ratios close to contractually specified limits exhibit greater stock price declines due to SFAS; No. W than multinationals with less binding working capital ratios.

If the inventol*y of fc?tlds available for payment of dividends decreased as a resuii of SFAS No. 8, managers would be more likely to invest in projects with a negative net present value. Alternatively, manages would act to reduce the vasiability of net income. In either case, costs would b\e imposed that result in _ a shar c; price decrease. “Bowen, No:eer?, and Lacey (1981) point out that using the above arguments to justiiy i hypothesis H.3 assumes that the managemen! commxhation agccment is the primary contr~~r-t between the manager and the firm. II implicit contracts arc assumed. or il !hcy exist. those contracts arc assumed to bc of less importance than the compensrtion agreement. The absence of a formal compensation contract written in terms ~4 accounting numbers does not imply that management compensation is unrelated to earnings changes. Furthermore, as Fama (I $80) implies, the existence of cx post settling up reduces ,tbe z&uon berl;;=cn management co,Gip::;*ktion and changes in eq?lity prices of the firm’-= cxurities. As a result, the above r‘ypothesis is probably a we& tz;; 0; iile &ect of compensat: 102 egrxments on managers’ actions.

Watts and Zimmerman (1978) suggest that the politicak process ~;tn he viewed ‘IS a wealth transfer mechanism whercb>; Firms, indivIduaEs. or co:~litions attempt to transfer wealth from corporations to themselves. Eari:c Fnirrive increases in reported earnings or very higEl levels nf earnings for a single quarter or year arz: used by political coalitions as examples of earning ‘excessive’ pro&s. These esamphes are used to justify :arpumcilts ihat tar@? visible firms are likely to be subject to greater regt!lation and/or t;lxatio~~. Tttiz is Cotlsislent With Zi~tWXtW~~l (1983), which pz:Wi&S WidelESe tc? Support the link between firm size and taxation. Consequently, an increase in tErc vari:t~tt.: of reported earnings from SFRS No. S incrcascs the likelihood th;nt E:ta*Re !ir ti13 wit1 incur poiiticali costs, thereby decreasing stock rctm~s.

income variability and foreign baiance sheet cxposuru arc summ;\lY iiii :I sures that have implications for both contractir,p costs and politicl:~EG:CYA I, .2, large foreign exposure inc; :ases the likelihood of income variability, whiz,r i11 turn has a direct impact upon (I) variance of managers’ compensa~icv. (2) !ikely tightness of interest coverage ratios, (3) increased variability LI the inventory uf Etinds avsi!ab!e for the dividend payment, and (4) increased expectancy of poiiticai costs. in addition, a large exposnrc I:? f
i.dotr that the rd~ov~”procedure daea not rewlt in a strict one-to-one’ match. This procedure 15 more appropriately drsctibed as a frequency cratching procedure.

the expected returfi as ro”ilo\vs:

For each event period, Student r-testswere used to assess the significance of the excess returns for three ps:tfoip?ios:(1) early adopters. (2) late edopters. and

(3) controf i%-ms;and for three portfolio return differences: (1) late vs. early adopters, (2) early adopters vs. control firms, and (3) late adopters vs. ccmtroI firms. Excess returns E, are defined differently for portfdios and porrfdio dXerences. Portfoho excess returns are calculated for each day i as

where the test is beiween the excess returns of one portfolio (etrr) versus the excess returns of a second portfolio (e2,,) and Iv’ is the number of firms in the portfolio. The numerator of the r-test is the sum of the excess returns F, for a given portfolio or portfolio difference over the number of trading days in the event period. The denominator of the l-test is the standard deviation of the excess returns:

where I’ is the number of trading days in the event period; T is the number of nonmissing excess returns in a 60-day period, the middle of which is the event period; and x :I- cr_ ,X,/T. Note that the above procedure implicitly controls for cross-sectional correiation across firms because the excess returns of all sample firms are averaged for a given time I. Student I-tests for cumulative excess returns over all 16 events were calculated using exactly the same procedure as above except that the excess return r, in the above equations is equal to the sum of all I6 event period excess returns.

3.4. ,l&mIis

of ectm period

&?SlS

The average daily excess returns for evenls 1 FhrouglB 16 and sssocinted shown in Fabk 5 for aI1 por&“h @x?rlps and f?orFfdio difkrenccs. Not a11 F-mm were inchsdcd En every everlt becausr of missing dara: 11owever. in t-m case were there fewerthan 101 firmsin any group. QveraEl, the results sriipporthpothesis PI.1 whiclm predicts a declit1e it1 the stock price of multination& aB?ectedby SFAS No. 8. The most significant

r-statistics are

result in table 5 concerns event 12. the release of the Exposure Draft of SFAS No. 8. The late adopter portfolio, e&y adopter portfoho, the portfolio bif&Xct~lCfZ bI%hWr, Mdy iid@ptelX iHId CO~td tiFEW%and the porthho difrerence between late ,?dqmS and cnntrr4 Lirms arc sip:~i~ic;tnt md negative. In cmtrast, positive and statisticahy significant excess returus are observed for

late adopters in eveut 10, the release of the Discussion Mem~rnndum concerning foreign currency accounting. None of the control firm excess returns are significant. The cumulative sum of all excess returns over ah 16 ewmts is negative and significant for the early adopter portfoho and the early a kpter versus control firm portfoiio difference. Without further analysis, however, these results shou! d be interpreted cautiously because several other expkmatic7ns for this resuil are possible, including: (I) the influence of autlier excess returns, (2) th.: eRects of an excess return methodologv versus a difference in retur;; methodology, (3) the e&et of foreign exchange rates, and (4) the size effect in the month of Sanuary. Histograms were used to investigate the int?ucnce of outher excess returns 02 event 12 (a two-day period) compared to surrounding nonevent periods. For each histogram, a total of 17’7 two-day nonevent period excess returrks were constructed over a 180-day period before and after event 1%. excluzhng three days either side of event 12. Histograms of excess returns wcrc made for and the control group. The histograms show that both experime ma! grq~ event 12 excess returns appear nonnormal and somewhat fat-tailed relative to nonevent excess returns, especially for the experimental groups, but outliers do not affect the distribution of excess returns of any group.’ Beaver (1981) explores the econometric properties of portfolio excess returns versus the difference in portfolio returns methodology. He &monslr:aFes that ati excess return methodology has a smaller variance relative to a difference-m-return methodology under the assumption of zero cross-sectional correlation of firm returns. In contrast, the dil~crence-in-rst~~rn upproac!t has a smaller variance than the excess return method assuming perfect cross-sectional correlatkn. In order to examine these issues in the conki; cil”this study, Iire z~alysis in table S was replicated using raw returns (not reported here). The relevant comparisons are between the portfolio excess returns of the late

so

W. K. Sularka, Stock price efect of SFA S h’o. 8

Table 5 Daily excess returns surrounding events which led to the issuance o? SPAS No. 8.a

event no.

NC. of trading daysh

1

2

2

2

3

8

___ Late

2

Control

- 0.003 ( - 0.94)

O.Wl (0.32)

- 0.002 ( - 0.93)

- 0.004 ( - 0.87)

0.003 (0.75)

- 0.006 (-2.09)**

- 0.003 (-0.76)

- 0.00001 (-0.15)

- 0,003 ( - 0.85)

- 0.002 ( - 0.54)

--c 005 (- L.38)

- 0.002 ( - 0.43)

- 0.002 (-0.31)

0.002 (0.24)

(-0.005)

- 0.002

O.ooool (0.04)

(0.16)

- 0.002 ( - 0.93)

0.002 (0.45)

0.001 (0.15)

0.003 (0.59)

0.001 (0.62)

0.008 (1.67)*

- 0.008 (-1.24)

0.001 (0.14)

- 0.008 (-1.18)

- 0.008 (- 1.34)

2

0.002 (0.45)

-0.006 (-1.06)

7

2

- 0.005 ( - 0.94)

( -0.78)

- 0.004

0.393 (1.27,)

- 0.001 (-0.12)

- 0.003 ( - 0.34)

0.009 (0.99)

- 0.00;1 ( - 0.32)

- 0.012 ( - 2.42)**

0.01 (1.07)

( - 0.23)

0.005 (0.54)

0.007 (0.71)

- 0.001 ( - 0.16)

- 0.002

( - 0.30)

0.007 (0.73)

0.006 (0.60)

0.012 (1.51)

- 0.002 ( - 0.47)

0.001 (0.12)

0.013 (1.47)

0.014 (1.99)**

- 0.007 (- l.46)

- 0.002

( - 0.82)

0.008 (1.76)*

- 0.005 ( -’ 0.X7)

0.003 (0.59)

- 0.04 - 0.05 - 0.003 (- 5.76)*** (- 5.97)*** ( - 1.04)

0.002 (0.42)

- 0.04 i-4.94)***

( - 4.76)**

0.004 (0.96)

- 0.004 (-0.75)

0.001 (0.12)

- 0.002

0.007 (1.65)*

0.005 (1.14)

0.003 (0.64)

- 0.00001 ( - 0.004)

10

4

0.012 (2.02)**

11

3

0.001 (O.lS)

2

13

3

0.002 (0.29)

- 0.003 ( - 0.56)

0.001 (0.33)

14

2

0.004 (0.77)

0.006 (1.13)

- 0.002 ( - 0.61)

0.001 (0.11)

0.003 (0.68)

0.001 (0.24)

( - 0.82)

0.005 (1.65)**

(::g

0.001 (0.61)

0.004 (1.17)

- 0.06 - 0.01 (- 2.68)*** (- 1.02)

0.02 (1.58)

15 16 Cum‘

2 2 42

0.&?1

- 0.006 (-1.19)

6

12

- O.\%KMl

0.007 (1.54)

- 0.001 ( - 0.25)

2

Late-control ~- O.OOl ( -0.32)

0.002 (0.94)

0.001 (0.17)

9

Early-contmi.

- 0.004 ( - 0.78)

2

2

La! e-early

0.003 (0.74)

5

8

Portfolio difkrcnces

F&iy

( - 0.28) 4

Portfolios

-0.04 (- 1.53)

~_-._.__

! -0.61) - 0.003

o.OcOO1 (0.05) - 0.05 (- 1.87)*

__. .__-.___________

- 0.002

-. 0.04

o.UO4 ,‘1.02) .- -7.03 ( - C).94)

“All f-tests are two-tailed: * 111;r 1.65. p 5 0.10: ** 1112 1.96, p s 0.0s; l ** IfIr 2.56, 175 0.01. hThe reported rctum for each event is the sum of excess returns on day - 1 and day 0, where day 0 is the calendar date of the event, cxccpt for events 3, 10, 11, and 13 where day 0 is the first calendar date of the event. ‘The cumulative txcess return is the sum of all excess returns for events 1-16.

adopter and early adopter groups in table 5 (an excess return methodology) with the difference-in-portfolio returns between the early adopters versus control hrms and late adopters versus control firms in the raw return rephcation (a difference-in-return methodology). The results of using raw returns are similar to table 5 in that negative return diherences for event 12 are most significant, fo!!cwed by the positive return differences for event IO. However, the raw return differences for the cumulative returns are not significant. The behavior of excess returns for the experimental firms is likely to be affected by changes in foreign exchange rates. 3n panel A of table 6 the changes in exchange rates of the British pound. French franc. German mark, and Swiss franc relative to the United States dollar are tabulated. In particular, all of the significant event 12 (event 10) excess returns are negative (positive) and all of the currency exchange rate changes are negative (positive). Presented in panel B of table 6 are Pearson product-moment correlations between excess returns of the three portfolios (late adopter, early adopter, and control firms) and the change in foreign currency exchange rates per United States dollar over ah sixteen events. Late adopter firms exhibit the highest correlations for each currency change and average currency change, indicating that the excess returns of late adopters is the most sensitive of all portfoho groups to changes in rates of foreign exchange. As expected, the excess returns of control firms are not correlated to changes in foreign currency exchange. In general, the magnitude of the correlations for the early adopter group are between the magnitudes of the late adopter group and the control group. Keim (1983) finds the negative relation between excess returns and firm size is most pronounced in the first five trading days of January. Event 12. the Exposure Draft release period, consists of two trading days, December 31, 1974 and January 2, 1975. Given the significantly larger size of both experimental portfolios relative to the size of the control portfolio, the negative excess return ditTerences between the experimental and control groups for event 12 as observed in table 5 is consistent ,with both a January size etFect and an effect from SFAS No. 8. As a result, it was necessary to control for tbe January effect when attempting to isolate the effects of SFAS No. 8 on the experimental firms. 5.3. January egect adjustments Two adjustments for the January effect were performed. Adjustment 1 was initiated by identifying all firms common to both the 1985 Annual (Expanded) Compustat tape and the 1986 CRSP Daily Stock Returns tape. FOF each firm i, the average market value (AM&) was computed, defined as the year-end market value of common stock plus the book value of long-term debt over the years 1973-1976. The 61-day period centered around day t = i) of event 12 (January 2, 1975) was divided into seven contiguous eight-day periods (j =

_ W/.K. Salatka, Stock price c4ecr q* SFAS No. 8

Table 6 Panel A: Change of foreign exchange rates per U.S. aoliar during time perio& surrounding events in !ab!e 4 which led to issuance of SFAS No. 3

Foreign currency3 pound

French franc

German mark

Swiss franc

1 2 3 4

- 0.007 0.00 0.00 0.0002

- 0.002 0.007 - 0.002 0.002

- 0.%x 0.003 - 0.0006 0.002

- 0.003 MO2 - 0.002 0.006

- 0.003 0.003 - 0.001 0.002

5 6 3 8

0.007 0.007 0.003 - 0.006

0.03 0.01 0.006 - 0.00.

0.01 0.007 0.003 - 0.006

c.01 0.003 i).
0.02 0.007 0.004 - 0.005

9 If! I? 12

- 0.009 0.01 - 0.001 - 0.005

- 0.005 0.02 0.001 -0.0004

- 0.003 0.004 0.W - 0.001

0.00 0009 0009 _ 0.002

- 0.002 0.01 0.0005 - OX!02

13 14 15 16

- 0.0001 - 0.001 - 0.001 0.005

- 0.001 -0.004 0.0009 - 0.0009

-- 0.003 -0.004 0.00 0.0002

0.0003 - 0.002 - 0.0003 0.003

- 0.001 - 0.003 - 0.00009 0.002

BdtiSh

Event

Average changeb

Panel B: Pearson product-moment correlutions between portfolio excess returns and changes in foreign exchange rates per US. dollar over ail 16 events in table 5

Late ador tersC

0.31 (0.24)

0.22 (0.41)

0.29 (0.27)

0.64 (0.007)

0.43 (0.09)

Early adc Jters

0.19 rn49)

0.13 (8.62)

0.15 (0.59)

0.54 (0.03)

0.30 (0.26)

Control firms

0.26 (0.34)

-0.17 (0.53)

0.05 (0.84)

0.2i

0.07

(0.44)

(0.79)

‘Ctaanees were computed by (FC, .- IX_ ,)/FC,_ ,, where FC, is the closing quote at the endbf tht event period and FC,_, is the closing quote on the day pnor to the event period. Average change is the sum of each foreign c-~rcacg chxge divided by four. CSignificance levels of the correlations are shown in parentheses.

1, . . . ,‘?) and one five-day period (j = 8). For each firm i, the excess returns E,, frtirn eq. (1) were averaged over the days 1 within each time period j, resulting in a mean excess return ( MERij) for firm i in time period jS For each of the j time periods eight separate cross-sectional regressions of the following form were performed: MERij=ajS/3jln(RM?<)

+ vii.

Thus, eight estimates of the intercept hj and slope (I were obtaiaed corresponding to the g time periods, S = 1, . . . ,8. Adjustment I excess returns en, for each sample firm i, for each day t, thirty days either side of day t = 0 of the event 12 time period, -were computed as follows for f ~j: %f

=Eif-

($i-ri,ln(AMV;)),

(41

where ei, is from eq. (1). Note that in e,ach calculation, elil. day I is contaimed within time period j, so that sj and /Ii correspond to the E,, to be adjusted. This procedure allows the size effect to vary over time, as is suggested by the findings of Keim (1983). -4djustment 2 was suggested by Kothari and Wasley (1985). Using the same data bases as were used for adjustment 1, all firms commsn to CRSP and Compustat were identified. These firms were ranked from lowest to highest average market value (AM’J). Ten portfolios were then formed from the ranked firms. For each portfolio, an equa%ly-weignted index of firm returns was computed, Rmpl, where p = 1,. . . , 10 and day t is 100 days either side of event 12. Estimates of the intercept spr and slope & from a market model were obtained using 100 days of return data surrounding event X2, excluding ten days either side of event ‘12.The market index used (R,,,,) corresponds to the size of firm i. Excess returns &Zirfor adjustment 2 were computed for all sample firms using the following equation:

where R n,pt is the *market model index p+ which corresponds to the size of firm l,..., 10, and ei, is from eq. (1). The methodology used in computing the numerator and denominator hr Student I tests of portfolio returns and portfolio differences was performed exactly as described in section 5.3, except that the excess returns .elit from adjustment 3 and e2ir from adjustment 2 were used in pIace of unadjusted excess returns eif. The results of the adjustments are shown in table 7. Both adjustments reduce the magnitude and significance of portfolio diff‘erence excess returns for the early adopter vs. control firms and the late adopter vs. control firms, which is consistent with removing a size effect in January from excess returns. However, these portfolio return differences continue to carry negative signs. Furthermore, event 12 excess returns continue to be more significant than any other event. Both adjustments cause all of the cumulative excess :returns to be insignificant except for ctintrol firm excess returns in adjustment 1. Adjustment 1 and adjustment 2 have dissimilar effects on excess returns for late adopter and early adopter portfolios, as well as for portfoho differences i, p=

Table 7 Daily excess rietums adjusted for the size effect in Jan~ary.~ .___-Late

Portfo!ios _Early &en1

Unadjusted (from table 5)

-0.04 (-5.76)*“*

Adjustment lb

0.01 (2.04)**

Adjustment 2’

- 0.02

- 0.05 (-5.97)*** - 0.0010001 (-0.02) - 0.009

(- 3.25;* ** (- 2.14)“*

Control

__.__ Late-early

Portfolio differences Early-control

Late-contmi

0.002 (0.42)

-0.04 (-4.94)***

- 0.04 (-4.76)***

0.01 (l.Y8)**

- 9.03 (-&IO)***

- 0.02 (-2.36)**

II excess returns

- 0.003 (-1.04) 0.03 (3.65)*** 0.008 (2.34)**

---

-0.009 (-- 1.61) -_-

*- -0.02 ( -~3.28)*** -.--

- 0.03 ( - 4.22)***

C~4mulutioeexcess reIt4rd

Unadjusted (from table 5)

- OS?4 ( - 1.53)

Adjustment 1

0.02 (0.65)

Adjustment 2

- 0.01 (-040)

-- 0.01 (- 1.02)

0.02 (1.58)

- 0.05 (- i.87j*

( - 0.94)

- 0.009 (-0.34)

0.025 (1.75)’

0.023 (1.46)

- 0.03 (- 1.12)

- 0.01 ( - 0.37)

- 0.02 (-0.80)

0.001 (0.07)

0.007 (0.47)

- 0.02

( -0.68)

- 0.01 ( - 0.37)

- 0.06 (-2.68)***

-- 0.03

“All r-tests are tw&iled: * ]tl> 1.65, JI $0.10; *’ 1112 1.96, p _<0.05; *** ItI 2 2.56, p < 0.01. ‘Srte ~dJu.rtmenr I: The thirty-day period either side of the midpoint event 12 is divided into jcven separate eight-day periods and one five-day period for a total af eight different time periods. This was performed for all tirms common to the 1985 Annual (Expanded) Compustat tape and the 1986 CRSP Daily Stock Returns tape. For each time period, the mean return, computed over the days within that time period, was regressed on the log of the firm’s average market value. In total, eight regressions were performed yielding a different intercept and slope coefficient for each of the eight time periods. The slope coefficien t for a given time period was multiplied by the log of the firm’s average inarket uah~ and. together with the inicrcept for that time period, was subtracted from the unadjusted excess return for the corresponding tims period, yielding an adjusted excess return. ?Gre Adjusrmeat 2: All firms common to the 1986 CRSP Daily Stock Returns tape and 1986 Annual (Expanded) Compustat data bases were identilied and ranked by the log of average market value. Ten portfolios were formed from !he !os+vest to highes: market value and an equally-weighted index computed for each portfolio. Excess returns for the experimental and control firms in the sample were computed using a market model where the market index was one of the ten equally-w&rued indexes which correspond to the size of the sample firm. Ten days surrounding the event 12 period were excluded from the estimation of these market model coefiicients. dThe cumulative excess return is the sum of all of the excess returns for events 1-16, where the excess return for event 12 is the size-adjusted excess return.

between late adopters and early sdupters. These results are most likely due @o the different assumptions used in each adjustment method. In adjustment 2 the estimate of the firm’s market sensitivity to the size effect varies by firm, whereas in adjustment 1 each firm’s sensitivity to the size effect is constrained to be the same for all firms in a given time period. The effect of both adjustments on the control group excess returns vis-a-vis the unadjusted excess

return

is probably

adjustments

due

to the interaciionof the .kmuary e!Tect and tlze

for size.

6. Cross-sectionai

regressions

Cross-sectional regressions are used to explain the behavior of the excess returns of the late adopter, early adopter, and control groups. Lcfrwich (1981). Holthausen (1981), Collins, Rozeff, and Bhaliwnl (19X1), Lys (1934). aud Hughs and Ricks (1984) used ordinary !east-squares (OLS) cross-sectional regressions in attempting to explain the capital market effects observed. However, OLS regression assumes that all firms have the same variance of excess returns. which is not the case for either of the experimental groups or the control group. to An OLS regression in this contexi will result in unbiased estimates of the regression coefficients. However, in the presence of unequal excess return variances which are known, WLS will provide a more powerful cross-sectional test than OLS by reducing the variance of the estimated regression coefficients [see Theil (1971, pp. 247-248)j. In this study, it was necessary to estimate the variance of excess returns for eacn firm. Consequently, the power of OLS versus WLS is an empirical issue. The basic design of the cross-sectional regression model is represented in matrix form by

where e, (en,. . . , E,,,,)’ represents the N x 1 vector of excess returns for N experimentai firms in event period t; ?P is z;‘r N X k matrix of k indeperrdent variables for each of the N firms. Each of the independent variables. which are discussed below, represent the costs imposed on firms suggested by the contracting cost and political cost theories. B, is a k X I vector of coeficients for each of the independent variables in event period t. The N X 1 vector of 2 \’ is assumed to be multivariate normal, with a disturbances qr (a,,, . . . 5VN,l mean of zero and variance-covariance structure represented by 9, which is dimensioned N x N. The diagonal elements of 92 are the N variances of the “Assuming that each firm’s excess re!ums variance is constant over time, the variance of the excess returns of each sample firm :omm.tted over the nonevent period used in section 5.4 was ranked according to average market value
individual firms, while the off-diagonal elements are assumed to be ~ro.rr It is assumed that the excess returns variance of each firm is stationary throughout the estimation period and the test period. The diagonal matrix L? is estimated by using the excess returns from estimates of the market model of eq. (1) over a period of T= 396 days: 198 days before the time period of the event tested and 198 days after. Excess returns three days either side of the time period of each critical event are excluded from the calculation, The estimated variance-covariance matrix is computed by using the diagonal. from also

wkfe

5 is an N X Z matrix of excess returns.”

6.2. Selection r3f cross-sectional variables

Nine variables standing for the contracting and political costs imposed on firms as a result of SFAS No. 8 were combined in a cross-sectional regression model. Each of the independent variables was measured over the period 1976 thro,ugh 19gO because: (I) 1976 was the earliest year currency translation adjustments were available; (2) it was desirable to capture the financial statement efiects of SFAS No. 8 iu the independent variables; and (3) it was preferable to measure the variables over a common time period. It is assumed thali capital market agents formed rational expectations of future values for all variables measured using post-SFAS No. 8 data. Varying the time frame of the independent variables did not appreciably affect the results.13 The form of the “As is discussed below, the cross-sectional regressions were evaluated both on the basis of an empirical distribution of nonevent day excess returns and assuming normal, independent, and identically disttibuted excess returns. No material differences in the interpretation of the regression results were noted, which suggests that among other distributional characteristics, cross-:Icctional correlation of the excess returns did not have a major irnluence on the regression results. ‘“Schipper and Thompson (1983, p. 199) point out: ‘Inverted covariance matrix follows an inverted Wishart distribution which has undesirable properties when the number of time-series observations is not greater than one plus the number of firms.’ ‘The length of the time-series used here (r = 396) was greater than one plus the number of firms in any of the test groups. t%o test the sensitivity of the results to the year in which the independent variable was measured, the cross-sectional regressions were performed using two differe iit specitkaiions of the independent variabies. In the first, independent variables were constructed for one year at a time over the period 1973-‘.YgO beginning with the year data were rirst avilabk. When the !ast year of data was encountered for a given variable, that year of data was contimued until all variables had incremented to their last year of data. ‘fhc second specification test used variable averages over three years of data beginning eiiher when data were first available or in 1973, wbichezr came later. The last year of data was dropped and the next year of da!a added for each varkble until the last year of valid data was encountered. The results of both tests were essentiailv the same a; the reported results.

where rzir is the excess returns for firm i. cumulated over the tirncperiod of event t calculated from cq. (1). CumuIative ~~324s W~WW adjusted fo;rrrthe January etkct are also used. These cnusist of cunu~lativc mccss returns from eq. (4) (adjustmmh 1) aud cumulative excess t=eturus fron:~ eq. (5) (adjustment 2). The event p~kd tests indicate that event TO aud evem 16 r,aused in~vtm.ws to fawer their estimated probability of SFAS No. ii. Accordingby, the excess returns for event i0 and event “a6are multiplied by - 1, rendering coeficicnt estimates which have predicted signs giveu by hypotheses E-?--H.? [see Eeftwiclk (1981)]. 1 if firm i has a tuimagemeut cotupeusatiou contract noted in a 10-K report for the years 1975 or 1976; = MGTCi equals zero otherwise. Earn kp-based compensation plans were the predotninabt cotupensatiou vehicle during the early to mid-seventies [Smith and Watts (19S4)].

z

net income before extraordinary items, discontinued operations, and taxes,, ~_ ~ interest expense,,

min

r-1976,....1960

i 1980

=

current assetsi, 1 i,_ ____^_ __~_,_~_ \ i_ )_ 1976current liabitities,, 11 _ * c

I

dividends declared or paid ,i

1

unrestricted re~umnedearnings,, 1’ 250

TOTRSK,

L-

C (flj,--Rij2/m I= -2.w

(1 C-DE,),

where r = Cl is the second day of the Exposure Draft of SFAS No. 8 release period (Januac- 2. 1975); R:, is the retrrrn of firm i a? time :: and R, is the average market, return of firm i over the period I = - 250 to t = 250.

year-end market value of

1980

c common stock plus book value of i = 1976 long-term debt ,,

SIZE,

= In

PCSmE,

= (OS-

a,)/uI;.

where crOis the standard deviation of residnal earnings per share (H’S) (se:: below) in the time period after the adoption of SFAS No. 8; uh is the standard deviation of residual EBS in the time period before the adoption of SFAS No. 8. For each time period, the standard deviation of EPS was calculated over at least eight quarter: but no more than twelve quarters of EPS data. In an attempt to control for intertemporal, economy-wide, and industry-wide elects, the firm’s reported EPS is regressed on an ecjually-weighted. industry EPS index and on an economy-wide EPS indi:x, both of which were constructed from a 1982 Compustat PDE tape. The residuals from that regression (called residual EPS above) were usled to calculate the percentage change in the standard derviatiou of EPS.

The ratio), gested of the ‘4-rlu, “SC

form of the variables DE (dfebt equity ratio), IN TC (interest coverage WC (working capital ratio). and DC (dividend payout ratio) is sugby Smith and Warner (10’79, pp. 136-137).14 TOTRSK is the total risk iirm from Lys (1984). .m~:“L.~pc. nc .YL~I~” LL1 UY,

IAITC-L s_, m,.

w/., .

, .nnri . .. .

ni’ ._.

!?-. rPYr?l! ._p__._._.

IhP ._._



__

nn .:::

firm%‘ .~ =I=. G xI,

I,I .rQ,nPt .:_..

debt colxnant provisions (hypothcsc~s I-I&I through H.4d). While the hypothcscs arc cxprcsscd in terms ol the firm’s distance from the constraint, the variables included in the cross-sectional regression arc’ in terms of the rcportc(J level of the variable refcrcnced in the covenant. Several definitions of d&t-to-equity ratios involviing dillixnces from industry means were constructed and used in place of the variable D.E in the cross-sectional regressions. The results were no: sensitive to the specification of DE. This is consistent with EXkzar, Lilien, and Pastena (1%6), ivho form leverage proxies based on the difference betvx:n ihe iirm’s ratio and the mean/median industry ratio. They find that sxh a proq does as wc31as the unconditional tirm ra?;?). of

DE

INTC WC I)C TOTRSK SIZE PCSTDE PFS

0.12 1.12 0.13 0.42 0.13 4.76 - 0.02 1.80 0.20 2.98 1.47 2.19 -0.12 0.84 0.10 0.33 0.08 O.OOO4 0.0002 0.0001 9.91 0.38 13.83 12.15 3.36 0.49

0.85 0.25

- 0.02 0.12

-0.46 -0.06 0.50 0.24 -0.24 -0.17 0.45 0.:!2 0.34 0.26

0.28 - 0.05

-0.05

0.10 0.15 0 I6

0.50 -0.07

L.07

0.18 - 0.008 0.06 0.003 O.r!? 0.16 .--...-___ .__.- -.~..-.._-. .. . _

___---

-

-0.15

0.07 0.55 0.60

.- Q.02 ~.

&,&?I B: Man!?- W??ir!!~r’ iJ 2e.s.r .fiW:i~~WW.:.i~s hiVrrYcr:c0r& ciiid ii;:,*“.l’I/WJ Tl?7 AfCTC

c;4

INTi‘

WC

1x

RS,Y

PCST _..

SiZE __~ _,

.-..

I?[:‘

PFS

1.W I.16 P-statistic -I 0.37 0.77 - 0.25 0.04 1.37 - 0.88 1.02 -____---.--.-._. . ..____.. ..~_ _ .~. _--_-.---_-~-“Distribution statistics are based on actual values of the indepcmdcnt variables. hPearson product-moment correlation coeticients arc based on weighted Icast-squares values of the independent variables (i.e., the value of the variatJc divided by the standard deviation of the firm’s market model residuals). MGTC = management compensation contract: DE = debt equity ratio: INTC = interest coverage ratio; WC = working capital ratio; DC = dividend constraint; TOTRSK = total risk: SIZE = size; PCSTIIE = percent change standard deviation of earnings/share; PFS = percent foreign sa!es. ‘Management compensation agreements were disclosed in 10-K reports by 52 late and 46 early auu&; &air...

Data for the debt equity, interes: coverage, and working capital ratios, and for dividend constraint and size were obtained from the I?82 Amxa! !nd~striai Compustat tape. Returns data for total risk were obtained from the 1982 CRSP daily returns tape. Data for the percentage change in the standard deviation of EP.7 and percent foreign sales were obtained from Vaiue Line and from 10-K reports. & the late adopter and early A comparison 0E LLe itiaepetlduir vii..~Giz~ adopter firms is shown in panel A of tabie 8. For the most part, the

distribution of independent variables shows only nomtnal numerical diRerences between the groups. A Mann-Whitney U te:st reported in panel I3 of table 8 indicates that the early adopter group has significantiy greater values of the variables for size and percent change :n carnmgs than the late adopter group. The difference in size supports hypothesis H.2. In addition, if late adopters of SFAS No. 8 were early adopters of SPAS No. 52, observing that late adopters are significantly smaller than early adopters is consistent with the results of Ayres (1986). Early adopters have a greater percentage change in the standard deviation of earnings than late adopters. This is consistent with the median variance of currency translation adjustments divided by total assets from 1977 to 1980 being significantly larger ( p 5 0.10) for early adopters than late adcpters. It is also consistent with the percentage of foreign sales being larger (not significant) for early adopters than late adopters, indicating that early adopters have more foreign operations which are likely to result in translation adjustments. Also presented in panel A of table 8 are Pearson product-moment correlations between the independent variables based on a weighted least-squares transformation of the independent variables (i.e., the original value of the variable divided by the standard deviation of the firm’s market model residuals). Overall, the magnitude of the correlations appear to be low. Discussed below ale additional tests for multicollinearity which corroborate this casual observation. 6.3. Cross-sectional

regression

results

The OLS and WLS cross-sectional regressions for la% adopters are shown in tables 9a and 9b, respectively. OLS and WLS regressions were periormed for all sixteen events enumerated in table 4; however, regression results are reported only when the F-value of the regression equation is significant at p -< 0.10, a two-tail test. A greater number of significant regression equations is observed for the WLS method in table 9b than for the OLS method reported in table 9a. In addition, the WLS results always show a greater adjusted r-square and usually a larger regression F-value than the OLS results for regressions common to tables 9a and 9b. The significance of the individual coefficients? however, is not uniformly greater for the WLS regressions. Coef3cients am significant when the coefhcient carries the predicted slzn and the t-statistic exceeds 1.28 (p 5 O.lO), a one-tail test. Over all OLS regressions reported in table 9a, from one to four significant coefficients are observed in each. The corresponding range over all WLS regressions is from zero to three significant coefficients. Thus, the cross-sectional resu!ts tend to support the predictions. However, no coefficient is consistently significant

0.09

(2.37)” - aGo4 ( - 0.70)

- MO3

( - 0.46) 0.0008 (2.10)* - 0.01 ( - 2.41) - 0.003 ( - C.EO)

0.15

0.w (I.01)

(2.53)” -- 0.01 t - 1.32)* 0.002 (0.56) Dmw4 (0.07) .-- o.o(li, ( - 0.79) o.cKKQ (0.03‘)

( -

om? B.wl

iuw

o.c!3

(ik;.?)

(l.Xi)

O.OtNS (%%I!*

0.001 (0.84)

.- O.QOOB

(- 1.34) - o.o@z ( - 0.49)

(0.47)

- Q.fKa7 : - l.so:*

- 0.006 ( - 0.73)

0.04

0.01 (#.6X) 0.005 (0.38)

3O.t?O (0.61)

6.12 (0.30)

44.M (0.59)

-- 0.009 ( - 2.02y

0.003 (1.48)

-- 0.01 (-- 1.38)’

- 0.0009

- 0.01 (- 2.X)*

-- o.oKI (- X6)*

- 0.008 (-2.2Cy

-0.004 (- 1.51)*

-0.m ( - 0.23)

- O.Oa2 ( - 0.83)

0.004 (2.39)

- 0.007 (- 2.tXy

- 0.02 (-l.12)

- 0.09

: - 2.81)*

- O.(?oS ( - 6.24)

( - 1.03)

0.0x

0.0x

2.1;

- O.CO1

( -0.39) O.iN8 (2.22)

34.51 (1.29)

f&03]

cr.004 (0.‘,2)

(0.87)

- 50.71 ( - i.31)*

1.69 [O.O9]

O.OL

(0.10)

O.hkQ? (Q.OS)

0.25)

- 16.5 ( - 0.68)

0.05

- 0.05 ( - 1.56)*

- 0.04

( -0.62) O.Ci (0.90)

---(?.K? ( - 0.48)

2.11

[O.OS]

a While cross-sectional regr&o:.s were performed for all events in table 4, only those with F-values significant at p I 0.10 (a two&! test) are reported. IMXCPT= intercept: AIGTC = manageme& compensation contract; DE = dcb! equity ratio; INTC = intcrest coverage ratio; WC = working capital ratio; DC = dividend constraint: RX‘?iSK = total risk: SIZE = size; PCSTDE = percent change standard deviation of earnings/share; PFS = percent foreign sales. ‘The dependent variable used in the cumulative regressions is the sum of the sigtriticant Iate adopter portfolio excess returns (PER,) in table 5 using the following algorithm: Cum = PER, ~PER,,

f PER,,

- PER,,.

‘Below each variable abbreviation is the predicted sign. In parentheses arc f-statistics. Except for the intercept, * indicates that the coeffkknt carrics the predicted sign and the r-statistic exceeds 1.28 ( p s O.l$ a one-tail test.

/ I / ,

I

I // / !

!

I

/

I /

W. R Salarka, Stock price efect of SFA S No. 8

h?

across all regressions, although size is sigrtifkant with greater frequency than any other coefficient. In both tables Sa and 9b, the cross-sectional regressions having the highest explanatory power are event IO, event 12, and the cumulative regression, The adjusted r-square of event 12 is 0.41 for the OLS regression and 0.70 for the WLS regression. Furthermore, the coefficient of the variable SIZE is highly significant in both regressions, suggesting that the January effect is confounding the results. The WLS regression in event 1%was replicated asing the excess returns from adjustment 1 and adjustment 2 as the dependent vaciable. This procedure, which is not reported here, reduces the adjusted f-square to 0.11 for adjustment I and to 0.18 lo adjustment 2. and for both adjustments the coefficient for SIZE became insignificant. For comparison purposes, tables 9 were replicated for both early adopters and control firms (neither reported here). Qualitatively, the results for early adopters are similar to the tables 5, results for late adopters with regard to explanatory power, comparisons between OLS and WLS estimation, the number of significant coefficients consistent with the theory, and the pattern of significance across regressions. In contrast, the control group WLS cross-sectional regression equations are predominantly insignificant.” Among the control group regressions having F-values significant at p I 0.10, most coefficients are not significant or carry signs opposite to the theoretical predictions. Thus, as expected, no effect from SFAS No. 8 on control firms is suggested from the pattern of coefficient significance. Consequently, greater confidence can be placed in previous tests where the control group is used for comparison purposes. 6.4. Diagnostic tests Several diagnostic tests were performed because of a concern that the previous cross-sectional results might be an artifact of some other phenomenon. The diagnostics focused on the WLS cross-sectional regression results reported in table IO for late adopters and on unreported early adopter regressions. 4.4.1. Omitted industry variables If industry membership is a significant factor in explaining excess returns, industry groupings should not be omitted from the cross-sectional model. ‘“fn the control group cross-sectional regressions, percent ioreign sales (PFS) was zero for all firms. Management compensation plan (MGTC) was not included because data were not available. Percent change/earnings (PCSTEE) was computed in two ways: (I) assuming that a control firm hypothetically adopted SFAS No. 8 one fiscal year prior to the required date, snaloeous to the adontion dates of the early adopter tirms, and (2) assuming that SFAS NO. 8 was hypothetically adopted by the control firms at the required date, corresponding to late adoption dates. The two alterneJtives yielded almost identical regression results.

64

iV.1~.Snlatka, Stock price effect o,fSr’AS No. P

Accordingl;!, one-&t SIC codes were used to construct eight indicator variables whirl;, classified Arms into nint kdustry groups. These eight indicator variablec ;or industry membership were added to the crosssectional regressions performed in tables 9. The coefficients from regressions in event 12 and the coefficients from regressions on cumulative excess returns were reiatively uilaffected when the industry variables were introduced except for the intercepts, which became insignificant. All other events exhibited similar characteristics. These results hold for early adopters, late adopters, returns adjusted for the January effect, and unadjusted returns. 64.2. A test for infhentiab fmns The diagnostic tests developed by Bdsley, Kuh, and Welsch (1980) were used to identify firms that had a dispropu.fonate icf,uencc on the crosssectional regressions. The method involves (1) identifying influential firms by rqression diagnostics and partial-regression leverage plots, (2) removing the influential firms from the sample, and (3) repeating steps (1) and (2) on the reduced sample until more than 50% of the original sample firms had been removed. These diagnostics (which are not reported here) were applied to late adopter and early adopter cross-sectional regressions for event IO, event 12, and the regressions on cumulative excess returns, as well as for the latter two regressions adjusted for the January effect. The results of each cross-sectional regression after the removal of infiuential firms was essentially the same as the regression results reported in tables 9. Each of the iterations exhibited a stable pattern of significance for both regression zF-va!ues as well as coe,fficient estimates.

A test developed by Beisley, Kuh, and Welsch (19%)) GYvas us& to investigate the effect of multicollinearity between tw o or more kdependent variables for both late adepter and early adopter groups. Thk rest also gives an indication of the unreliability of the regression co&kknts resulting from multicoll&rearity, The test cons&s of computin g the eigenvalues and principal components of the regression (X’X) matrix of independent variables. For r:dch .i/ariable, the proportion sf the variance of the estimate accounted for bg each principal component was calculated. In addition, condition indices were obtained by taking the square roots of the ratio of the largest eigenvalue to each individual eigenvalue. if a high condition index is associated with a principal component which contributes strongly to the variance of two or more variables, the multicollinear relation will tend to increase the variance of the estirmated regression coefkienrs. The guidelines of Eelsiey, Kuh, and We&h (1980, pp_

W.K. Salatka, Stock price effect of SFAS No. 8

65

153-161) were used to assess the cutoffs for condition indices and variance proportions. For both early and late adopters, only one condition index was deemed ‘high’, which corresponded to a pair-wise correlation between the variable SI’ZE and the intercept. [Note that all regression variables were divided by the standard deviation of the market model in eq. (2), which is a weighted least-squares transformation. Thus, under weighted least-squares, the intercept is no longer a constant; it is equal to one divided by the standard deviation of the market model.] Removing the intercept from the regression does not appreciably affect the coefficient of SIZE in any of the regressions reported in tab!ec 9 or for the early adopter group. However, removing the SIZE variable from the regressir-u substantially changed the coefficient of the intercept in most instances, especially for the regression on cumulative excess returns. This corroborates the previous regression results where the intercept is driven to insignificance when industry classifications were included in the modz!, but none of the other coefficients are appreciably affected. Only one condition index was deemed ‘moderate’, between the variables working capital (V’C) and total risk (TQTRSK )_ Removing either of the variables does not appreciably afFect the regression results. No collinearity between three or more variables was detected. Thus, multicollinearity does not appear to have an nppreciable effect on the cross-sectional results.

6.5. Cross-sectional regressions in ncvwent periods me significance of the event period cross-sectional regression results reported in tables 9 was evaluated against a distribution of cross-sectional regressions where nonevent excess returns were the dependent variable. Such an analysis explores the effect of violating ;he assumptions of normality, independence, and identical distriburion of excess returns. Tab!e 10 reports summary statistics for individual coefficient t-statistics and regressian F-vahres that result from the nonevent cross-sectional regressions for late adopter WLS and OLS regressions. The dependent variable is the same two-day cumu”iative nonevent excess return used in section 5.4 to plot histograms. The 90th percentile OLS regression F-value (2.28) and the 90th percentile WLS regression F-value (2.22) from table 10 were used to evaiuafz the significance of the cross-sectional regressions in table 9.*6Events 10 and 42 fr~r the OLS regressions and events 4, 10, 12, and 16 for the WLS regressions in tables 9 exceed the 90th percentile F-values. Fewer significan; regressions ue “Using the ?Oth percentile of the empirical distribution in table 10 to evaluate th signiticsncs of the OLS and WLS cross-sectional regression resuits is eqGvalent to a p 2 0.10 significance level.

W.K. Salatka, Stock price e&3 of SFAS No. d

66

Table 10 Empirica! distribution

of nonevent regression coefficient t-statistics and regression late adopters.= Percentiles of OLS nol:event regression coefficient t-statistics

F-values for

Percentiles of WLS nonevent regression coefficient r-statistics

Variable

Max. 100%

90%

50%

10%

Min. 0%

Max. 100%

90%

50%

10%

Min. 0%

INTIiCPT MGTC DI? INTC U’C DC TOT’RSK SlZE i’e;“TDE iJFS

4.22 208 2.75 1.82 3.82 3.56 4.83 4.20 3.93 2.31

2.13 1.35 I.39 0.98 X4 1.42 2.32 2.40 1.42 1.32

0.10 0.05 0.34 0.05 !X?s 0.07 0.17 -0.19 -0.27 -0.01

-213 - 1.19 -1.44 - 0.69 - 1.75 - 1.05 - 2.39 -2.33 -1.35 -1.07

-3.16 -2.;‘7 -2.75 -2.94 -3.03 -4.42 -4.20 -4.84 -2.16 -1.94

4.50 2.53 2.83 2.24 3.41 2.44 3.23 4.61 2.77 2.69

1.89 1 53 1.38 1&.>_ 24 1.33 1.40 1.74 1.76 1.18 1.20

0.01 O.GB 0.07 0.06 0.09 0.01 0.03 -0.01 -0.18 0.16

- 1.88 -1.38 - 1.35 -LOO - 1.48 -1.14 - 1.78 - 1.76 -1.23 - 1.22

- 3.30 - 3.18 - 2.68 -4.30 - 3.21 -2.19 - 4.61 - 3.24 -1.98 - 2.22

Regression F-value

3.99

2.28

1.03

0.52

4.44

2.22

1.17

0.45

0.13

0.20

“Nonevent excess rezums were defined as the sum of daily excess returns over contiguous two-day periods. Daily returns were generated using a market model over t( period 180 days prior to, 180 days after, and excluding t-bee days either side of event 12. Thus, a total of 177 two-day returns were used as depzndent variables in constructing the above distributions. iNT.Wi;T= intercept; MCTC ==management compensation contract; DE = debt equity ratio; INTC = interest coverage ratio; WC = working capital ratio; DC = dividend constraint; TOTRSK = total risk; SIZE = size; PCSTDE = percent change standard deviation of earnings/share; PFS = percent foreign sales.

ohest~d using the empirical distribution as opposed to a normal distribution, indicating the presence of positive cross-correlation among excess returns. However, the mzjor results observed in previotls tests are manifest: the WLS method exhibits greater numbers of significant cross-sectional regressions compared to the OLS method; and events 10 and 12 exhibit high levels of significance. The significance of the individual regression coefficients in tables 9 was evaluated l&g the i-statistic cutoffs in table 20. If the predicted sign of tile coeikient v:as positive, the 90th percentile t-statistic cutoff was used; if negative, the 10th percentile cutoff was used. The resulting number aiid pattern of significant coe!?icients are similar to what is reported in tables 9. Thus, the conclusiozls obtained from examining results of rk ~tn** r=-*:- --~’ -*u.h,-*rv‘ir’ilal regressions evaluated on the basis of an empirical distribution of norievent excess returns does not materially diSfer from ?he conclusions derived from the same regressions under the assumption that excess returns are normal, independent, and identically distributed.

W.K. Salarku, Stock price eflecr of SFAS No. 8

61

7. Sumnary and discussion

Significant excess returns were \>bserved on the SF.4S No. 8 E.xposure Draft release date and to a lesser extent on the Discussion Memorandum release date. However, the Exposure Draft release coincides with the beginning of January. After performing two adjustments for the size effect in January during the Exposure Draft release date, both experimental groups continued to exhibit significant negative excess returns compared to a control group of domestic firms matched on size and industry. The late adopter cross-sectional regression results tend to support the contracting cost theory. QuaUatively, the cross-sectional regressions for esrsy adopters are similar to the late adopter regressions. As expected, the crosssectional results of the control group were not consistent with an erect from accounting constraints due to SFAS No. 8. The use of weighted least-squares regression resulted in greater numbers of significant regressions relative to the ordinary least-squares regressions for each sample group. However, the coefficients of the weighted least-squares regressions Jvere not uniformly more significant than the corresponding coefficients of the ordinary least-squares regressions. None of the diagnostic tests appreciably affected the reported results. The theory developed tmplies that late adopters more than early ndopters relied upon accounting methods to dampen the financial statement efTects of translation adjustments prior to SFAS No. 8. As a result, stock price changes for late adopters are directly attributed to the increase in contractual constraints defined in terms of accounting numbers an,1 pohtical costs. Hn con-. trasz, early adopters probably used production, investment, and/or financing de&ions as the primary means to avoid currency translation adjustments prior to SFAS No. 8. Thus, it is likely that share m-ice changes for early adopters did not result from costs of contractual constraints directly, but from changes in production, investment, and/or financing choices in response to impending contractual and political constraints. Obsening that the results of early adopter cross-sectional regressions show evidence of an eR’ect ‘2om contractual constraints and political costs is a necessary but not suRc,cient condition to interpret early adopter stock price changes. Further resea ch is needed to explore the differences beiween early adopters and Iate adopters in production, investment, and financing activities such as hedging. foreign investment strategy, and debt renegotiation. Several caveats should boti rn**r:A.= -- ’ when bnterpre!.ing the rest&s. First, the rvl10_7_5_~~~ January effect concurrent with the Exposure Draft of SFAS No 8 was the largest January effect among the years tested in K&XI <1483). !V’i;hout kc~wing the cxxz of the January effect, it is unclear how and why an adjustment for size controls 2r the January effect. Second. to the extent the ef%cr of C-c :._, excllalLge L-VU rates- Ir 11PC3&._ . th.9 ;$ ?A+ l(i ‘1’Yh]l?W . rPtIumc 4-l tb luleif;n z.Vcrorrtrn. 1 jlilVlllU -_._ .CVI,~~ WIj3_W ._____ ___ ____mn ____L_r* __._

68

W K. Saiatka, Stock price eflect of SFAS No. 8

observed excess returns are affected by changes in foreign currency exchange rates. Third, the measurement of the accounting constraints in the crosssectional regressions can contain errors which are uncorrelated with the constraint but nevertheless may be correlated with excess returns. Fourth, the stock price changes in any event day could be caused by other economic events occurring at the same time as the event of interest. For example, the Wail Street Journal disclosure of the release of the Exposure Draft of SFAS No. 8 is embedded in a much longer article concerning an Exposure Draft on inflation accounting. To the extent that the firms in this sample are systematically affected by inflation, changes in stock prices may result which have no direct connection to SFAS No. 8.

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69

Hughes, John and William Ricks, 1984, Accounting for retail land sales: Analysis of a mandatLd change, Jcurnal of Accounting and Economics 6, lOl--132. Keim, Donald, 1983, Size related anomalies and stock return seasonality: Further empirical evide;rce, Journal of Financial Economics 5, 13-32. Kothati, S.P. and Charles Wrs!ey, 1985, Measuring security price performance using daily :etums: .A comparison of a ‘size-adjusted’ model with the market model, Working paper (University of Iowa, Iowa City, !A). Leftwich, Richard, 1981, Evidence of the impact of mandatory changes in accounting principles on corporate loan agreements, Journal of Accounting and Economics 3, 3-36. Lorensen, Leonard, 1972, Reporting foreign operations of U.S. companies in U.S. dollarj, Accounting research study no. 12 (American Institute of Certified Public Accounts, New Yo.k, N-Y). Lys, Thomas, 1984, Mandatory accounting changes and debt covenants: The case of oii ar.d gas accounting, Juurnai of Accounting and Economics 6, 39-63. Makin, John, 1977, Flexible exchange rates, multinational corporations and accounting stanci?-rds, Federal Reserve Bank of San Francisco Economic Review, Fall, 44-55. Merjos, Anna, 1977, For better or worse: FASB-8 continues to play hob with zarporate earninl.,s, Barron’s, Aug. 8, D-23. Pakkala, A., 1975, Foreign exchange dccocnting of multinational corporations, Financial Anal! &s Journal, March/April, 32-i6. Revsin, Philip, 1976, Bitten exchange: New accounting rule makes multinationals al~i thsir strategies, The Wall Street Journal, Dec. 8, 1. Ricks, William, 1982, &u&i assessment of alternative accountin: rr,&ods: A review oi the empirical evidence, Journal of Accounting Literature, Spring, 59-99. Rodriguez, Rita, 1977, FASB-8: What has it done for MS?, Financial Analysts Journal, March/April, 40-47. Scbipper, Catherine and Rex Thompson, 1983, The impact of m.erger-related regulations on !he shareholders of acquiring firms, Jodmal of Accoudting Research, Spring, 184-220. Seidler, Lee, 1979, Changes in FASB No. 8 suggzsted by study showing harmful impacts of translation accounting, Bear Stearns Accour,nng Issues, Bear Stearns and Company Investment Reseaeh, Jan. 1830-33. Shank. John, Jessie Dillard, and Robert Murdock, 1979, Assessing the economic impact of FASB-8 (Financial Executives Research Foundation, New York, NY). h&h, Cli~Tor~ and Jcrold Warner, 1979,Cn fin an&al contracung: An analysis of bond covenants, Journal of Financial Economics 7,117-161. Smith, ClitTord and Ross Watts, 1984, The structure of executive compensation contracts and ihe control of management, Working paper (University of Rochester Rochester, NY). Stabler, Charles, 1977, Adding it up: How recently issued acsounting rules aie affecting corporate annual reports, The Wall Street Journal, March 30, 34. Theil, Henri, 1971, Principles of econometrics (Wiley, New York, NY) 247-248. Watts, Ross and Jerold Zimmerman, 1978, T,owsrds a positive theory of the de?ermination of accounting standards, ;flne L Accountirr~ Review !-q, 112-134. Zimmerman, Jerold, 1983, Taxes and fu-rnsize, Jo&&l of Accounting and Economics 5,119-l-19.