Debt Reclassification and Capital Market...

Journal of Business Finance & Accounting, 33(7) & (8), 1189–1212, September/October 2006, 0306-686Xdoi: 10.1111/j.1468-5957X.2006.00593.x

Debt Reclassification and Capital MarketConsequences

Jeffrey D. Gramlich, William J. Mayew and Mary Lea McAnally∗

Abstract: We provide initial evidence on the economic consequences of a relatively large, fully

disclosed, and apparently purposeful reporting decision: the balance sheet classification of short-

term obligations as long-term debt in accordance with Statement of Financial Accounting StandardNo. 6 . We examine a sample of 1,684 American firm-year observations between the years 1989

and 2000 to determine whether reclassification is associated with debt-ratings and equity values.

We find that reclassification increases the likelihood of a subsequent debt-rating downgrade. We

also find that market value decreases with increases in the amount reclassified, and that equity

value is higher after firms cease reclassifying short-term obligations as long-term debt, compared

with other firm-years in the sample. Thus, changes in debt classification are empirically linked in

predictable directions to subsequent changes in debt ratings and stock values. Taken together,

our results show that debt classification is an important publicly-available indicator that may

be useful to capital market participants. We discuss several research extensions including the

implications of our findings to European companies that convert to IAS in 2005.

Keywords: debt classification, economic consequences, debt ratings, market value of equity

1. INTRODUCTION

This paper provides initial evidence on the economic relevance and consequencesof a large, fully disclosed, and apparently purposeful reporting decision: the balancesheet classification of short-term obligations as long-term debt in accordance withStatement of Financial Accounting Standard No. 6 (SFAS 6). Gramlich, McAnally andThomas (2001), (hereafter GMT) document that firms use the flexibility affordedby SFAS 6 to smooth key liquidity and leverage ratios toward both industry bench-marks and prior-year levels. GMT demonstrate that firms shift short-term debt tothe long-term category (i.e., ‘reclassify’) in some years while in other years these

∗The authors are respectively, from the University of Southern Maine and Copenhagen Business School;the University of Texas at Austin; and Texas A&M University. This paper has benefited significantly from thecomments of Shane Dikolli, Michelle Hanlon, Karim Jamal, Ross Jennings, Bill Kinney, Lisa Koonce, TomScott, Senyo Tse and Connie Weaver as well as from workshop participants at the University of Alberta andthe 2002 University of Texas at Dallas Accounting and Finance Symposium. (Paper received January 2005,revised version accepted July 2005. Online publication April 2006)

Address for correspondence: Mary Lea McAnally, Accounting Department, Mays Business School, TexasA&M University, College Station, TX 77843, USA.e-mail: [email protected]

C© 2006 The AuthorsJournal compilation C© 2006 Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UKand 350 Main Street, Malden, MA 02148, USA. 1189

1190 GRAMLICH, MAYEW AND McANALLY

firms move such debt back to the current category (i.e., ‘declassify’). These classi-fication changes reduce the variability of firms’ current and long-term debt ratiosacross time and mitigate the deviation of liquidity ratios from industry norms. Weextend GMT by directly addressing the economic relevance of debt classification.In particular, we contend that debt classification is not an innocuous financialreporting decision—managers do not accidentally reclassify debt—and some of themotivation is apparently driven by the desire to portray the firm as healthier thanit actually is. We provide empirical evidence economically linking firms’ debt classi-fication decisions to stakeholder wealth.

SFAS 6 permits a firm to reclassify short-term debt as long-term if a loan commitmentis obtained that extends for more than a year beyond the balance sheet date. Ourdata reveal that each year from 1989 to 2000, about $26.7 billion of commercialpaper (which the SEC defines as short-term) is classified as long-term debt. Thisaverages $564 million per firm each year during our sample period. The disclosurein Exhibit 1 typifies SFAS 6 debt reclassification. Firms must exclude short-termobligations from current liabilities if the firm (1) intends to replace the maturingshort-term debt issue with another issue; and (2) has the ability to do so.1 Firmsdemonstrate ‘ability’ with credit-facility terms that extend beyond the term necessaryto support the short-term obligation. While the standard states that firms ‘shall’reclassify, in practice, considerable discretion remains. To exercise discretion a firmcan either declare that it has no intent to replace the current debt issue or fail tosecure appropriate enabling credit facilities. Ceteris paribus, long-term credit facilitiesare more costly than short-term facilities; thus, firms incur real costs to enablereclassification. Sound economic reasons may exist for firms to obtain longer-termloan commitments, such as less costly commercial paper rollovers and longer-terminvested capital. However, we consider the possibility that some firms strategicallyreclassify and declassify debt and that these strategy decisions impact stakeholderwealth.

To substantiate our claim that debt reclassification is not an innocuous financial-reporting choice, we first develop and estimate a model that explains reclassifi-cations. The evidence indicates that firms with lower leverage, current ratio, andoperating cash flows more frequently reclassify short-term debt as long-term. Thissuggests that managers reclassify to obscure the firm’s true financial condition andnot to simply reveal the likely timing of debt repayments.

We also assess whether changes in classification systematically predict differ-ences in the costs of debt and equity capital. Although other research ties earn-ings properties to shareholder wealth (e.g., Kormendi and Lipe, 1987; Ohlson,1995; Sloan, 1996; and Barth, Beaver et al., 1999), we examine whether balancesheet debt reclassification is associated with subsequent changes in debt ratings.After controlling for demographic and financial variables known to influencedebt ratings, firms that reclassify are more likely to experience a debt-rating

1 ‘A short-term obligation . . . shall be excluded from current liabilities only if 1) the enterprise intendsto refinance the obligation on a long-term basis and 2) the enterprise’s intent to refinance the short-term obligation on a long-term basis is supported by an ability to consummate the refinancing (whichis) demonstrated . . . by a financing agreement that clearly permits the enterprise to refinance the short-termobligation on a long-term basis on terms that are readily determinable and . . . the agreement does not expirewithin one year . . . and during that period the agreement is not cancelable by the lender or . . . investor.’(Financial Accounting Standards Board 1975, Statement 6 , paragraphs 9–11).

C© 2006 The AuthorsJournal compilation C© Blackwell Publishing Ltd. 2006

DEBT CLASSIFICATION AND MARKET CONSEQUENCES 1191

Exhibit 1

Excerpt from the Debt Footnote of Pacificorp’s 1998 Form 10-K

The Company’s long-term debt was as follows: (in $millions)

PACIFICORP 12/31/98 12/31/97First mortgage and collateral trust bonds

Maturing 1999 through 2003/5.9%–9.5% $ 816.4 $ 1,005.6Maturing 2004 through 2008/5.7%–7.9% 1,032.7 632.7Maturing 2009 through 2013/7%–9.2% 328.6 331.6Maturing 2014 through 2018/8.3%–8.7% 98.4 100.9Maturing 2019 through 2023/6.5%–8.5% 341.5 341.5Maturing 2024 through 2026/6.7%–8.6% 120.0 120.0

Guaranty of pollution control revenue bonds5.6%–5.7% due 2021 through 2023 (a)71 71.2 71.2Variable rate due 2009 through 2013 (a) (b) 40.7 40.7Variable rate due 2014 through 2024 (a) (b) 175.8 175.8Variable rate due 2005 through 2030 (b) 450.7 450.7Funds held by trustees (7.4) (9.1)

8.4%–8.6% Junior subordinated debentures due 175.8 175.82025 through 2035Commercial paper (b) (d) 116.8 120.6Other 21.9 25.1

Total 3,783.1 3,583.1

Less current maturities 297.6 194.9

Total 3,485.5 3,388.2

SUBSIDIARIES6.1%–12.0% Notes due through 2020 649.8 264.5Australian bank bill borrowings and commercial paper (c) (d) 414.3 756.6Variable rate notes due through 2000 (b) 11.6 12.14.5%–11% Non-recourse debt – 160.7Other – 1.4

Total 1,075.7 1,195.3

Less current maturities 1.9 170.5

Total 1,073.8 1,024.8

TOTAL PACIFICORP AND SUBSIDIARIES $4,559.3 $4,413.0

Footnotes (a) through (c) excerpted.(d) The Companies have the ability to support short-term borrowings and current debt being

refinanced on a long-term basis through revolving lines of credit and, therefore, based uponmanagement’s intent, have classified $531 million of short-term debt as long-term debt.

downgrade relative to firms that do not reclassify. But we find no evidence thatdeclassifying firms are more likely to experience a debt-rating upgrade. We do, however,find that the market value of equity decreases when firms begin reclassifying andincreases when firms cease reclassifying.

Taken together, our findings suggest that capital-market participants view reclassifi-cation as a ‘red flag’ indicative of management intervention in the reporting process.Our debt-rating and market-value results potentially provide firm managers with abetter understanding of the economic consequences of their reclassification decisions.Additionally, our findings provide evidence concerning an accounting choice thatinfluences debt-covenant compliance. Firms and lenders could use these findings tostructure debt covenants that either explicitly allow or disallow the reclassified amount



in computing covenant levels or ratios (see also Beatty, Ramesh and Weber, 2002).Thus, our research responds to the call by Fields et al. (2001), for studies ‘on whetherthe alleged attempts to manage financial disclosures by self-interested managers aresuccessful’ (p. 258).

The remainder of the paper proceeds as follows. Section 2 develops three hypotheses.Section 3 describes the data and our models. Section 4 discusses the results and Section5 concludes and offers several research extensions.

2. HYPOTHESES

(i) Factors Associated with Debt Reclassification

Our first model explains debt reclassification. While reclassification does not affect afirm’s total liabilities, it simultaneously increases both the current ratio and the long-term debt ratio. Firms may reclassify because they believe that external parties monitorfirm liquidity via the current ratio, for example. These parties could include equityanalysts who appraise firms’ ability to meet obligations, lenders who set and enforcedebt covenants or suppliers and others who use credit scores such as Altman’s Z -score,in making credit decisions.2 Directly testing whether firms’ current ratios affect theseparties’ decisions is difficult: most credit-scoring systems are proprietary and typicaldebt-covenant footnotes contain only boilerplate language.3 Thus, to build our model,we consider that credit-scoring models and debt covenants typically specify minimumlevels for current ratio or working capital measures (Altman, 2000; and Mester, 1997).This implies that firms with lower current ratios or working capital are potentially morelikely to reclassify. Conversely, credit-scoring models and debt covenants typically specifymaximum levels for total leverage or long-term debt. While reclassification improvesliquidity measured by the current ratio, it increases leverage measured by long-termdebt ratios. Consequently, we expect only firms with long-term debt slack to reclassifyshort-term debt to long-term. That is, reclassification may only be viewed as a viablealternative for firms to meet a liquidity target if it does not impact a leverage constraint.If firms reclassify to disguise worsening financial condition, performance measures suchas operating cash flow and profitability would be negatively related to the reclassificationdecision. Thus, we predict:

H1: Firms with lower current ratios, lower long-term debt leverage, lower operatingcash flows and lower profitability are more likely to classify short-termobligations as long-term debt.

2 Mester (1997) reports that 70 percent of banks use multivariate credit-scoring models to make commerciallending decisions.3 We searched Dealscan, Loan Pricing Corporation’s commercially available database of lending agreementswith over 100,000 transactions on global loans, high-yield bond, and private placements since 1986. Dealscansummarizes specific loan information, including borrower, lender, amount, term, debt covenant data andsinking fund requirements. Although the December 29, 1999, version contains 1,355 lending deals withcurrent ratio covenants, only 20 of these, representing only nine unique firms, were among our reclassifiers.Consequently, with the small sample size, we could not use the Dealscan data to statistically test potentialdebt-covenant violation hypotheses.



(ii) Economic Consequences of Reclassification

Innocuous debt classification decisions would have no market consequences. Onthe other hand, if debt classification imparts useful information to debt and equitymarkets, capital-market activity may reveal these stakeholders’ views of forecasted futurecash flows. By testing for capital-market responses to debt classification, we ascertainwhether debt classification systematically predicts real economic effects to stakeholders.Specifically, we examine the impact of reclassification and declassification on debtratings and on the market value of equity.

(iii) Reclassification and Debt Ratings

Debt-rating agencies such as S&P, Moody’s, Fitch Investors Service, and Duff andPhelps glean private information in evaluating firms’ credit worthiness.4 Prior researchestablishes that credit analysts have economic incentives to reveal their privateinformation (Millon and Thakor, 1985). We argue that debt-rating agencies mayprivately learn firms’ rationale for reclassifying and factor that information into theirdebt ratings. We consider two possible links from private information to debt ratings,one direct link and the other indirect. First, from detailed knowledge of the terms ofexisting debt agreements, debt raters may discern whether a firm reclassified short-termdebt to avoid violating a debt covenant. This knowledge could directly affect a firm’sdebt rating because debt-covenant violation has the potential to impact cash flows.Second, debt raters may ascertain that reclassification is related to other economic ormanagerial factors that affect firms’ credit ratings.5 This knowledge could indirectlyaffect a firm’s debt rating. We consider both links below.

Debt-covenant information is more-readily available to credit analysts than tofinancial statement readers. For example, consider the Pacificorp reclassificationpresented in Exhibit 1. Dealscan reports that a 1998 debt covenant required thatPacificorp Inc. maintain a current ratio of at least 1.1 to 1. This covenant wasnot explicitly reported in the company’s financial statements that year, althoughpresumably debt raters access the same information used to develop the Dealscandatabase. In 1998, Pacificorp reclassified $531 million of commercial paper fromcurrent liabilities to long-term debt. The effect of this reclassification was to increasethe company’s 1998 current ratio from 0.827 to 1.105 – just above the 1.1 levelneeded to avoid a covenant violation. Our conversations with several partners at publicaccounting firms confirmed that they would recommend reclassification to clients

4 Cantwell (1998) reports that annual meetings with the rating agencies are the norm and that 30 percentof survey respondents reported meeting with the agencies more than twice a year. Trade publications reportcorroborative anecdotal evidence, ‘Larger companies are usually visited annually by Moody’s personnel withsupplemental visits by management to New York. We often arrange visits to the operations of individualbusiness segments to assess specialized areas firsthand’ (Harold H. Goldberg of Moody’s Investors Service,quoted in Credit, 1991).5 Picker (1991) reports the following example of rating agencies acquiring private information. In February1991, AA-rated Shell Canada provided its rating agency with ‘advance insider information: Shell Canada’sdecision to exit the coal business. The (rating) agencies approved of this material change in operations.And before the press release announcing the sale of the business went out in June, along with a resulting$120 million loss in earnings, (Shell Canada CEO) Darou’s staff phoned Moody’s, S&P and the two domesticCanadian agencies. . . . The analysts never blinked; Shell Canada was not downgraded at the time, nor was itput on a dreaded credit watch’ (Picker 1991, p. 76).



facing debt-covenant violations. The propensity to heed such advice may signalmanagement’s broader predilection to intervene in the reporting process.

Debt-raters also have access to private information gleaned in private discussionswith management, detailed supplementary financial information, and on-site visits(Butler and Rodgers, 2002). This private information pertains to management’s depth,expertise, and historical track record, as well as to management’s strategies andoverall philosophies. To the extent that reclassification is associated with this privateinformation, debt ratings will be associated with reclassification. Although we do notposit that reclassification is a direct determinant of firm’s credit ratings, we arguethat reclassification serves as a proxy for credit analysts’ private information, includinginsight into debt covenant proximity, economic conditions and management’s financialreporting strategies. In other words, debt reclassification signals firm weakness that debtraters can directly assess using private information. Thus, our second hypothesis is:

H2: Debt ratings are negatively (positively) related to the reclassification (declassi-fication) of short-term obligations as long-term debt.

(iv) Reclassification and the Market Value of Equity

There are cash-flow consequences to reclassification because the cost of a 366-dayloan commitment exceeds the cost of a 90-day commitment. However, in most casesthese costs are not likely to be large enough to have a statistically measurable impacton firm value. Apart from the loan-commitment fee, reclassification does not directlyaffect earnings nor does it appear to impact firm value. Nonetheless, as discussedabove, we maintain that reclassification is a value-relevant signal . Consistent with priorresearch, we cannot specify the mechanism by which managers’ choice to reclassifycurrent liabilities as long-term debt affects firm value (Fields, Lys and Vincent, 2001).Instead, we posit that a confluence of factors impact stock price (for similar hypotheses,see Kliger and Sarig, 2000; and Dichev and Piotroski, 2001). Thus we hypothesize:

H3: The market value of firms’ equity is negatively (positively) related to thereclassification (declassification) of short-term obligations as long-term debt.

3. DATA AND MODELS

(i) Sample Selection and Data

Using Lexis/Nexus, we searched for firms that reclassified short-term debt during theperiod 1989 to 2000.6 If a firm met the search criteria at any time within the 12-yearperiod, we collected short and long-term debt footnotes for that firm for the completeperiod 1988 to 2000.7 This approach identified a total of 3,080 firm-years. We gatheredadditional financial variables, debt ratings, and equity values from the Compustat and

6 Our Lexis/Nexus search term specified the word ‘reclass’ within 200 words of the term ‘commercial paper’in firms’ annual reports of Forms 10K. The term is conservative in that it did not identify firms that did notmention commercial paper.7 Because certain variables require lagged data, we also gathered data for 1988.



CRSP databases. Missing Compustat data and insufficient current-year and prior-yearfootnote disclosures reduced our sample as indicated in Table 1, Panel A. The finalsample is 1,684 firm-year observations between the years 1989 and 2000.

Table 1

Sample Selection

Panel A: Criteria for Sample of Firm-years that Reclassified at Least Once Between 1989and 2000

Firm-years between 1989 and 2000 identified with search terma 3,080Less: Firm-years not available on Compustat (462)

Firm-years missing relevant Compustat and/or CRSP variables (465)Firm-years missing sufficient current debt footnote information (346)Firm-years missing sufficient prior-year debt footnote information (123)

Final sample 1,684

Panel B: Industry Affiliation for Samples Resulting From Each Criteria2-digit Reclassifying Non-reclassifying Total

Industry Group† SIC Codes Firm-Years Firm Years Sample

Clothing 22–23 5 24 29Financial 60–69 35 28 63Food 1–7,20–21 52 62 114Media 27,48 361 231 652Metallurgy 34 40 23 63Miscellaneous manufacturing 39 0 7 7Oil 13,46 72 17 89Retail sales 50–59 104 94 198Services and other 70–79,83,99 62 55 117Transport 40–45,47 47 21 68Utilities 49 56 74 130Wood products 24–26 86 56 142Healthcare 80 12 0 12

Totals 932 752 1,684

Panel C: Sample Distribution Across YearsReclassifying Non-reclassifying Total

Year Firm-years Firm-years Sample

1989 49 35 841990 53 31 841991 47 37 841992 48 36 841993 62 73 1361994 63 103 1661995 87 86 1731996 104 82 1861997 111 71 1821998 113 67 1801999 103 67 1702000 92 64 156

Totals 932 752 1,684



Table 1(Continued)

Notes:The table reports sample selection results. We applied the following search term to the AR group file withinthe NAARS library of Lexis/Nexus for the years 1989 through 1994: ((DATE = 1989) AND (COMMERCIALPAPER W/200 (CLASSIF! W/25 (LONG-TERM OR NONCURRENT)) AND (COMMERCIAL PAPERW/200 (DUE W/10 1990)))). From 1995 through 2000, we applied the term to the COMPNY group filewithin the COMPNY library. We modified the search term accordingly for each of the subsequent 10 years.Once we identified a firm as meeting the search term at any point within the 1989–2000 period, we collectedthat firm’s financial data for all years between 1989 and 2000, producing 3,080 firm-year observations. Thefinal sample contains only firms that reclassify short-term debt to long-term at some point during the 1989to 2000 period and report sufficient financial and market data for the analyses.†Our search term did not identify any debt reclassifications in the following industry sectors: automobiles(37), chemicals (28–29), consumer goods (15–16), electrical (36,38), equipment (35), healthcare (80,82),material (32–33), mining (10,14), or professional service (87).

We read and coded debt footnotes to obtain reclassification information, includingthe amount and type of short-term debt reclassified along with information pertainingto the terms of supporting loan commitments, interest rates, fees and compensatingbalances. We also searched the disclosures for information about debt covenants andany violations thereof.8 We coded a firm-year as a reclassification if commercial paper,notes, or other debt maturing within the next year were classified as long-term pursuantto the ‘intent and ability’ paragraph of SFAS 6 .

(ii) Models

We first attempt to explain firms’ decisions to reclassify and then we test for anassociation between reclassification (and declassification), debt ratings, and marketvalue of equity. That is, we estimate pooled, cross-sectional time-series models using non-reclassification firm-years as a control for reclassification firm-years. In later sensitivityanalysis, we match reclassification firms with firms that exhibit no reclassification activityat any point during the 1989–2000 period. The results we find with this matched sampleare substantively the same as our main results.

(iii) Factors that Explain the Decision to Reclassify

The following logistical regression model evaluates factors related to firms’ decisionsto reclassify:

RECLASSi, t = β0 + β1ROAi, t + β2LEVi, t + β3CRATIOi, t + β4CFOi, t

+ β5RECLASSi, t−1 + υi, t (1)

where RECLASSi,t is a binary variable indicating one if firm i reclassified short-termdebt as long-term in year t, and zero otherwise; ROA is earnings before extraordinaryitems scaled by total assets; LEV is the ratio of long-term debt to assets; CRATIO is

8 We examined the Dealscan database to obtain additional information related to working capital andcurrent ratio covenants, since such ratios are directly impacted by reclassification. Dealscan, however,provided limited details regarding these covenants by our sample firms.



current assets divided by current liabilities; CFO is cash flow from operations scaledby total assets; and is the unexplained residual. Both CRATIO and LEV are calculatedafter adjusting for reclassification.

Lev (1969) reports that firms’ financial ratios adjust toward the previous year’sindustry averages. Among the six financial ratios Lev examines, the quick and currentratios exhibit the fastest and most significant adjustments toward industry averages.9

Thus, it is plausible that firms reclassify to avoid deviation from industry benchmarks forcertain key metrics. To address this in our examination of hypothesis 1, we control forindustry norms by subtracting the annual industry median from each of our continuousindependent variables, as follows:

RECLASSi, t = β0 + β1(ROAi, t − MedianROAi, t ) + β2(LEVi, t − MedianLEVi, t )

+ β3(CRATIOi, t − MedianCRATIOi, t ) + β4(CFOi, t − MedianCFOi, t )

+ β5RECLASSi, t−1 + υi, t .(2)

We compute industry medians for two-digit SIC codes using data from the entireCompustat database for each year. Other variables are as previously defined.

(iv) Models of the Association Between Reclassification and Debt Ratings

We test for an association between reclassification and debt-ratings using a logisticregression of the direction of debt-ratings changes.10 Our model includes demographicand financial variables suggested by Ziebart and Reiter (1992). We read Standard andPoor’s ‘Corporate Ratings Criteria’ (Standard and Poor’s 2001) to determine additionalfactors that credit analysts deem relevant. Thus, the model includes a parsimonious setof control variables suggested by theory and practice to test hypothesis 2.

RATINGΔi, t0+1 = β0 + β1 + β2ROAΔi, t + β3LEVΔi, t + β4CRATIOΔi, t + β5CFOΔi, t

+ β6SIZEΔi, t + β7RECLAMTΔi, t +19∑j=8

β j INDi + υi, t

(3)

where RATING is the S&P discrete numeric senior-debt rating that ranges from 2,corresponding to a ‘AAA’ rating, to 27, corresponding to a ‘D’ rating.11 We defineRATINGΔ as ‘1’ if the firm’s debt rating improves (upgrades), as ‘-1’ if the firm’srating deteriorates (downgrades); and as ‘0’ if the rating does not change. Thus,RATINGΔi,t0+1, captures the cumulative directional change in RATINGi , across the

9 Lev (1969) does not explain how the ratio adjustments are performed, just that they occur, noting that,‘. . . the techniques by which firms adjust their ratios were not investigated. This is a very complex problemsince ratio adjustment may be achieved in several ways. . . . there is no way to identify specific techniqueswhich probably differ from firm to firm’ (Lev 1969, p. 298). We hypothesize that reclassification may be onesuch technique.10 See Ederington (1985), for a review of this empirical approach.11 We explored S&P commercial paper ratings as an alternate dependent measure. However, fewer firmshave commercial paper ratings and commercial paper ratings have fewer distinct ratings levels (7 possibleratings for commercial paper compared to 27 possible ratings for long-term debt).



years t and t + 1. We examine both years t and t + 1 because empirical and anecdotalevidence suggests that changes in debt-ratings occur with some time lag (Reiter 1990;Ziebart and Reiter, 1992; and Standard and Poor’s, 2001).12 For example, firms areoften placed on Standard & Poor’s CreditWatch before the rating is formally changed.

The independent variables in equation (3) measure changes during year t inpreviously-defined variables so that both the dependent and independent variablesreflect intertemporal changes. As a control for change in firm size, we include SIZEΔ,the change in the natural log of total assets. The test variable, RECLAMTΔ, is the changein the dollar amount of reclassified short-term obligations scaled by total assets. Wealso include a set of 12 indicator variables, INDj , to capture the firm’s industry. In theirCorporate Rating Criteria, S&P explicitly states that they take industry information intoaccount when forming a rating. Therefore, we assign firms to industry groups basedon S&P’s Global Industry Classification Standard. Table 1, Panel B provides the S&Pindustry group definitions.

To further test hypothesis 2, we include terms to examine the effects of decisionsto begin and end the practice of reclassifying debt on the balance sheet (i.e.,reclassification changes):

RATINGΔi, t0+1 = β0 + β1 + β2ROAΔi, t + β3LEVΔi, t + β4CRATIOΔi, t + β5CFOΔi, t

+ β6SIZEΔi, t + β7RECLAMTΔi, t + β8STARTi, t

+ β9STOPi, t + β10(STARTi, t × RECLAMTi, t )

+ β11(STOPi, t × RECLAMTi, t−1) +23∑

j=12

β j INDi + υi, t .

(4)

In this equation, we code STARTt−1 as ‘1’ if the firm began reclassifying short-term debtduring year t − 1, and STOPt−1 as ‘1’ if the firm stopped reclassifying all short-term debtduring year t − 1 (i.e., the firm reclassified debt in year t − 2 but did not reclassify inyear t − 1). The interaction terms multiply indicator variables START and STOP by theamount of reclassified short-term obligations scaled by total assets.

Ceteris paribus, debt-rating upgrades (downgrades) are more likely for firms withincreasing (decreasing) profitability, liquidity and cash flow, so that the control variablesROAΔ, CRATIOΔ and CFOΔ should have positive coefficients. In contrast, firms withincreasing (decreasing) leverage are less (more) likely to experience positive (negative)debt rating changes; thus, we expect a negative coefficient for LEVΔ. We offer noprediction for SIZEΔ, a growth measure. Consistent growth could signal healthy cashflow and stable management, but rapid growth might also make the company toodifficult to manage or imply more future borrowing.13 We expect that a firm is morelikely to receive a debt-rating downgrade when the amount reclassified increases duringthe year as well as when reclassification begins. Consequently, we predict negative

12 Robustness tests using only debt rating changes in year t reveal somewhat weaker results but do notchange our conclusions about the effect of reclassification on debt ratings.13 Nearly one-third of credit downgrades between 1984 and 1989 resulted from hostile acquisitions or fromcompanies’ actions to defend themselves against takeover (Picker, 1991).



coefficients on both RECLAMTΔ and on the interaction term START × RECLAMT(i.e., β 7 < 0 and β 10 < 0). We also expect that debt-rating upgrades are more likelywhen firms declassify and predict a positive coefficient on STOP × RECLAMT (i.e.β 11 > 0).

(v) Models of the Association Between Reclassification and Market Value of Equity

We use the following model based on Ohlson (1995) to test whether reclassifiedliabilities are incrementally value-relevant to the amount of total liabilities (i.e,hypothesis 3):

MVEi, t = β0 + β1BVAi, t + β2BVLi, t + β3AEi, t + β4RECLAMTi, t + υi, t . (5)

We measure the market value of the firm’s common equity (MVEt) three monthsafter the end of fiscal year t to ensure that the firm’s stock price impounds thereclassification information reported in the footnotes to the financial statement forfiscal year t.14 BVA is the book value of total assets; BVL is the book value of totalliabilities including the reclassified amount. To calculate abnormal earnings AE, wefirst calculate an expected return as twelve percent of the book value of equity at thebeginning of the year (i.e. 0.12 × [BVAt−1 − BVLt−1]).15 AE is the difference betweenreported earnings and the calculated expected return. RECLAMTt is the dollar amountof short-term obligations reclassified as long term during year t. Consistent with manyprior studies (see Barth et al., 2001 for a summary), we expect that BVA and BVL willindicate positive and negative coefficients, respectively. If reclassification is a signalof management’s intervention in the financial reporting process, greater amounts ofreclassified obligations will be associated with lower firm value (i.e. the coefficient onRECLAMT will be negative).

Alternatively, to test whether changes in reclassification have an even greater impacton market values than the level of reclassified short-term obligations, we refine equation(5) to estimate a model that separately examines coefficients for amounts initiallyclassified and declassified amounts.16 When reclassification occurred in the prior yearbut no reclassification occurs in the current year, the prior-year reclassification amountis considered declassified:

MVEi, t = β0 + β1BVAi, t + β2BVLi, t + β3AEi, t + β4STARTi, t + β5STOPi, t

+ β6(RECLAMTi, t − RECLAMTi, t−1) + β7(STARTi, t × RECLAMTi, t )

+ β8(STOPi, t × RECLAMTi, t−1) + υi, t .

(6)

14 As a robustness test, we estimate our market-value models using market value of equity at the end of fiscalyear t and our inferences do not change.15 Abarbanell and Bernard (2000) report consistent results for abnormal earnings calculated with discountrates ranging from 9 to 15 percent. Their calculations hold rates constant across time and firms. As arobustness test, we also calculate abnormal earnings using alternative rates and our findings are qualitativelyunchanged.16 Econometrically, a model with an interaction term should also include both main-effect variables. Thus,our model should include both current and lagged reclassified amount. We include the change in reclassifiedamount (current minus lagged reclassified amount) in lieu of each variable separately. This facilitates theinterpretation of the estimated coefficient.



We predict that initial reclassification will decrease equity values (i.e. β 4 < 0 andβ 7 < 0). Further, declassification provides a signal that economic conditions willimprove and therefore declassification is predicted to be associated with greater equityvalues (i.e. β 5 > 0 and β 8 > 0).

4. RESULTS

(i) Sample and Descriptive Statistics

Table 1 presents descriptive statistics for our sample. Panel A explains the sample-selection process and Panel B shows that 932 of the 1,684 sample firm-years indicatereclassifications of short-term debt as long-term. As Panel B shows, the sample appearsto be heavily weighted in media industries (i.e., SIC 27 and 48) but both reclassificationand non-reclassification firm-years are fairly well distributed among the other industrygroups. Panel C reveals that reclassification activity increased substantially acrossthe decade, beginning with only 49 reclassifications in 1989 and peaking with 113in 1998.

Table 2 compares reclassifying and non-reclassifying firm-years on several dimen-sions. When firms reclassify, they are larger, as measured both by the mean andmedian book value of assets (p < 0.01) and the market value of equity (mean p < 0.05;median p < 0.01). Reclassifying firms exhibit higher mean and median asset growthrates (p < 0.01). The amount reclassified is statistically significant, as indicated by the$564 million mean ($252 million median) amount reclassified (p < 0.01). Reclassifica-tion significantly changes long-term debt and current ratios: mean and median long-term debt ratios, as reported on the balance sheet, are greater for reclassifying firmyears than for non-reclassifying firm years (p < 0.01). But backing out the reclassifiedamount reverses the direction of the difference: reclassifiers’ mean and median long-term debt ratios are statistically smaller than those of non-reclassifiers (p < 0.01). Moredramatic, however, is the comparison of the current ratio before and after the effectsof reclassification. In particular, the mean current ratio without reclassification is lessthan 1, reclassification boosts the mean above 1 (mean = 1.202). Comparing the non-reclassifying and reclassifying firm years, we see that reported mean and median currentratios are lower in reclassifying firm years than in non-reclassifying firm years (1.20compared to 1.48). Without reclassification the differences are startling (0.95 comparedto 1.48).

(ii) Findings Regarding Firms’ Reclassification Decisions

Table 3 reports results for our logistic regressions (equations (1) and (2)) that explainfirms’ reclassification decisions. Both equations compare firm-years without reclassi-fication to firm-years with reclassification for the same set of firms (i.e., ‘Sample 1’).Clearly, current ratio influences the reclassification decision—the lower the currentratio, the more likely the firm is to reclassify, as indicated by the negative coefficienton CRATIO (p < 0.01). The negative coefficient on LEV (p < 0.01) also indicates thatfirms reclassify when they can afford to—when leverage is low and can afford to have the



Tab

le2

Co

mp

aris

on

of

1,6

84

Fir

m-y

ear

sW

ith

and

Wit

ho

ut

Recl

assi

fica

tio

nB

etw

een

1989

and

2000

Firm

-yea

rsW

ithR

ecla

ssifi

catio

nFi

rm-y

ears

With

outR

ecla

ssifi

catio

n(N

=93

2)(N

=75

2)

25th

75th

25th

75th

Attr

ibut

esM

ean

Med

ian

Perc

entil

ePe

rcen

tile

Mea

nM

edia

nPe

rcen

tile

Perc

entil

e

Ass

ets

,$

mil

lio

ns

9,3

32

∗∗5,0

22

∗∗2,1

58

11,4

66

6,1

79

3,4

49

1,6

07

6,4

95

Mar

ket

valu

eo

feq

uit

y,8,2

17

∗3,7

61

∗∗1,8

37

9,3

57

6,4

89

2,2

97

1,0

87

5,3

02

$m

illi

on

sR

etu

rno

nas

sets

4.4

%4.2

%2.0

%7.2

%4.1

%4.1

%1.9

%7.6

%C

ash

fro

mo

pera

tio

ns

10.1

%∗

9.8

%∗

6.5

%13.5

%9.3

%9.4

%5.5

%13.3

%to

asse

tsA

sset

gro

wth

21.9

%∗∗

5.6

%∗∗

−1.2

%15.1

%8.6

%4.1

%−2

.8%

12.0

%R

ecl

assi

fied

amo

un

t,564

∗∗252

∗∗100

590

00

00

$m

illi

on

sL

on

g-t

erm

deb

tto

asse

ts,

0.2

75

∗∗0.2

68

∗∗0.1

84

0.3

44

0.2

34

0.2

21

0.1

35

0.3

11

asre

po

rted

Lo

ng-t

erm

deb

tto

asse

ts,

0.2

04

0.1

95

0.1

11

0.2

69

0.2

34

∗∗0.2

21

∗∗0.1

35

0.3

11

wit

ho

ut

recl

assi

fica

tio

nC

urr

en

tra

tio

,as

rep

ort

ed†

1.3

30

1.2

02

1.0

18

1.5

46

1.4

80

∗∗1.2

66

∗0.9

63

1.7

44

Cu

rren

tra

tio

,w

ith

ou

t1.0

12

0.9

50

0.7

40

1.1

90

1.4

80

∗∗1.2

66

∗∗0.9

63

1.7

44

recl

assi

fica

tio

n†

Nu

mb

er

(perc

en

t)o

f177

128

deb

t-ra

tin

gd

ow

ngra

des

(19.0

%)

(17.0

%)



Tab

le2

(Co

nti

nu

ed

)

Firm

-yea

rsW

ithR

ecla

ssifi

catio

nFi

rm-y

ears

With

outR

ecla

ssifi

catio

n(N

=93

2)(N

=75

2)

25th

75th

25th

75th

Attr

ibut

esM

ean

Med

ian

Perc

entil

ePe

rcen

tile

Mea

nM

edia

nPe

rcen

tile

Perc

entil

e

Nu

mb

er

(perc

en

t)o

f99

90

deb

t-ra

tin

gu

pgra

des

(10.6

%)

(12.0

%)

Not

es:

Th

eta

ble

rep

ort

sm

ean

,m

ed

ian

,25

than

d7

5th

perc

en

tile

leve

lsfo

r12

attr

ibu

tes

for

932

recl

assi

fyin

gfi

rm-y

ear

san

d752

no

n-r

ecl

assi

fyin

gfi

rm-y

ear

s.A

ssets

and

the

mar

ket

valu

eo

feq

uit

yar

em

eas

ure

dat

the

en

do

fth

eye

ar.

Retu

rno

nas

sets

isin

com

eb

efo

reextr

aord

inar

yit

em

sd

ivid

ed

by

en

d-o

f-ye

arto

tal

asse

ts.

Cas

hfr

om

op

era

tio

ns

isas

rep

ort

ed

on

the

cash

flo

wst

atem

en

t,d

ivid

ed

by

en

d-o

f-ye

arto

tal

asse

ts.

Ass

et

gro

wth

isth

ep

erc

en

tage

chan

ge

into

tal

asse

tsfr

om

the

beg

inn

ing

toen

do

fye

ar.T

he

recl

assi

fied

amo

un

tis

the

do

llar

amo

un

to

fsh

ort

-term

deb

tre

clas

sifi

ed

aslo

ng-t

erm

pu

rsu

ant

toSF

AS

6,as

rep

ort

ed

inth

efi

rm’s

fin

anci

alst

atem

en

tfo

otn

ote

.L

on

g-t

erm

deb

tis

div

ided

by

en

d-o

f-ye

arto

tal

asse

ts,

and

issh

ow

ntw

ow

ays:

firs

tas

rep

ort

ed

on

the

fin

anci

alst

atem

en

tan

dse

con

daf

ter

rem

ovi

ng

the

amo

un

to

fsh

ort

-term

deb

tre

clas

sifi

ed

tolo

ng-t

erm

.C

urr

en

tra

tio

iscu

rren

tas

sets

div

ided

by

curr

en

tli

abil

itie

san

dis

sho

wn

two

way

s:fi

rst

asre

po

rted

on

the

fin

anci

alst

atem

en

tan

dse

con

daf

ter

rep

laci

ng

the

amo

un

to

fsh

ort

-term

deb

tre

clas

sifi

ed

tolo

ng-t

erm

.† C

om

pu

stat

do

es

no

tre

po

rtva

lues

of

bo

thcu

rren

tas

sets

and

curr

en

tli

abil

itie

sfo

ral

lfi

rms

inth

esa

mp

le.

Of

932

(752)

recl

assi

fica

tio

n(n

on

-recl

assi

fica

tio

n)

firm

-year

s,867

(707)

rep

ort

dat

asu

ffic

ien

tto

com

pu

teth

ecu

rren

tra

tio

.∗

(∗∗

)In

dic

ates

that

eit

her

‘fir

m-y

ear

sw

ith

recl

assi

fica

tio

n’

or

‘fir

m-y

ear

sw

ith

ou

tre

clas

sifi

cati

on

’m

eas

ure

isla

rger

and

sign

ific

ant

atth

e0.0

5(0

.01

)le

vel

usi

ng

atw

o-s

amp

let-

test

of

mean

s,a

Wil

cox

on

sig

ned

-ran

kte

sts

of

med

ian

so

r,in

com

par

ing

pro

po

rtio

ns

of

firm

-year

sw

ith

deb

tra

tin

gch

ang

es,

an

on

-par

ametr

icb

ino

mia

lte

st.S

ign

ific

ance

leve

lsar

ere

po

rted

assu

min

ga

two

-tai

ld

istr

ibu

tio

n.



Tab

le3

Fac

tors

Exp

lain

ing

the

Deci

sio

nto

Recl

assi

fySh

ort

-term

Deb

tas

Lo

ng-t

erm

Eq

uat

ion

(1)

:R

EC

LA

SS

i,t=

β0+

β1R

OA

i,t+

β2L

EV

i,t+

β3C

RA

TIO

i,t+

β4C

FO

i,t+

β5R

EC

LA

SS

i,t−

1+

υi,

t

Eq

uat

ion

(2)

:R

EC

LA

SS

i,t=

β0+

β1(R

OA

i,t−

Med

ian

RO

Ai,

t)+

β2(L

EV

i,t−

Med

ian

LE

Vi,

t)

+β3(C

RA

TIO

i,t−

Med

ian

CR

AT

IOi,

t)+

β4(C

FO

i,t−

Med

ian

CF

Oi,

t)+

β5R

EC

LA

SS

i,t−

1+

υi,

t

Sam

ple

1(N

=1,

574)

†Sa

mpl

e2

(N=

724)

‡

Equa

tion

(1)

Equa

tion

(2)

Equa

tion

(1)

Equa

tion

(2)

Vari

able

(Pre

dict

edsi

gn)

Estim

ate

(χ2)

Estim

ate

(χ2)

Estim

ate

(χ2)

Estim

ate

(χ2)

Inte

rcep

t(?

)0.8

70

∗∗∗

−1.0

44

∗∗∗

1.1

23

∗∗−1

.445

∗∗∗

(11.2

3)

(125.2

6)

(5.4

9)

(109.1

1)

RO

Ai,

t(−

)0.1

05

0.9

86

−3.3

35

−3.2

26

(0.0

1)

(0.6

3)

(1.9

8)

(1.8

8)

LE

Vi,

t(−

)−2

.703

∗∗∗

−2.6

12

∗∗∗

−3.3

29

∗∗∗

−2.9

80

∗∗

(27.1

5)

(23.0

1)

(8.0

3)

(6.1

2)

CR

AT

IOi,

t(−

)−1

.299

∗∗∗

−1.5

34

∗∗∗

−1.0

81

∗∗∗

−1.1

90

∗∗∗

(90.5

3)

(94.2

1)

(14.8

2)

(14.9

0)

CF

Oi,

t(−

)0.2

53

−0.2

30

−3.3

21

∗∗−3

.417

∗∗

(0.0

3)

(0.0

27)

(6.2

1)

(6.3

7)

RE

CL

ASS

i,t−

1(+

)3.0

12

∗∗∗

3.0

06

∗∗∗

17.5

27

17.4

63

(449.0

7)

(445.4

2)

(0.0

1)

(0.0

1)

Mo

del

likeli

ho

od

Ch

i-sq

uar

e834.9

4840.8

4651.4

1651.3

6P

sued

o-R

241%

41%

59%

59%



Tab

le3

(Co

nti

nu

ed

)

Sam

ple

1(N

=1,

574)

†Sa

mpl

e2

(N=

724)

‡

Equa

tion

(1)

Equa

tion

(2)

Equa

tion

(1)

Equa

tion

(2)

Vari

able

(Pre

dict

edsi

gn)

Estim

ate

(χ2)

Estim

ate

(χ2)

Estim

ate

(χ2)

Estim

ate

(χ2)

%co

rrect

lyp

red

icte

d88%

88%

95%

95%

Not

es:

Th

eta

ble

rep

ort

sp

aram

ete

rest

imat

es,

χ2

stat

isti

cs,an

dexp

lan

ato

ryp

ow

er

of

alo

gis

tica

lre

gre

ssio

nu

sin

gd

ata

for

eac

hfi

rmia

nd

year

t.S

amp

le1

incl

ud

es

1,5

74

of

the

1,6

84

ob

serv

atio

ns

desc

rib

ed

inT

able

2b

eca

use

11

0o

bse

rvat

ion

sd

idn

ot

rep

ort

suff

icie

nt

dat

ato

com

pu

tecu

rren

tra

tio

.S

amp

le2

incl

ud

es

the

sub

set

of

36

2S

amp

le1

recl

assi

fica

tio

no

bse

rvat

ion

sth

atca

nb

em

atch

ed

wit

h3

62

firm

-year

sth

atd

idn

ot

ind

icat

ere

clas

sifi

cati

on

du

rin

gth

ep

eri

od

fro

m1989

to2000.

RE

CL

AS

Sis

ab

inar

yva

riab

lein

dic

atin

g‘1

’if

the

firm

recl

assi

fied

sho

rt-t

erm

deb

tto

lon

g-t

erm

,an

dze

roo

therw

ise.

RO

Ais

inco

me

befo

reextr

aord

inar

yit

em

sd

ivid

ed

by

en

d-o

f-ye

aras

sets

.L

EV

islo

ng-t

erm

liab

ilit

ies

meas

ure

dw

ith

ou

tth

eeff

ect

of

any

deb

tre

clas

sifi

cati

on

,d

ivid

ed

by

en

d-o

f-ye

aras

sets

.C

RA

TIO

iscu

rren

tas

sets

div

ided

by

curr

en

tli

abil

itie

sm

eas

ure

dw

ith

ou

tth

eeff

ect

of

any

deb

tre

clas

sifi

cati

on

.C

FO

isca

shfl

ow

fro

mo

pera

tio

ns

rep

ort

ed

on

the

cash

flo

wst

atem

en

td

ivid

ed

by

en

d-o

f-ye

aras

sets

.E

qu

atio

n(2

)ad

just

seac

ho

fth

ese

vari

able

sex

cep

tR

EC

LA

SS

by

sub

trac

tin

gth

efi

rm’s

ind

ust

rym

ed

ian

(see

ind

ust

rycl

assi

fica

tio

ns

inT

able

1,

Pan

el

B).

∗∗∗

Sig

nif

ican

tat

the

1%

leve

lin

atw

o-t

aile

dte

st.

∗∗S

ign

ific

ant

atth

e5

%le

vel

ina

two

-tai

led

test

.∗

Sig

nif

ican

tat

the

10

%le

vel

ina

two

-tai

led

test

.



leverage ratio increase from the reclassification.17 Taken together, our results suggestfactors that motivate firms to reclassify and provide a backdrop to our empirical testsconcerning the economic consequences of firms’ reclassification decisions.

It might be argued that using firms as their own control could bias the results. Toaddress this concern, we identify a size- and industry-matched control sample of firms’(matched by year) that do not reclassify at any point during the 1989 to 2000 sampleperiod. We label this matched-pairs sample, Sample 2. Table 3 shows that results aresomewhat stronger for Sample 2. In particular, coefficients are more significant andCFO becomes significant in the predicted direction; the negative coefficient on CFOindicates that firms with declining cash flows are more likely to reclassify debt. Thus,our results are robust to estimation on a wider sample.

(iii) Results of Tests for an Association Between Reclassification and Debt Ratings

We use logistic regressions to estimate the effect of changes in the financial andreclassification variables on the likelihood of a firm experiencing a debt upgrade (orthe likelihood of NOT experiencing a downgrade or no change in rating). Thus, inTable 4, positive (negative) coefficients imply that an upgrade is more (less) likely.18

Consistent with prior studies (Ziebart and Reiter, 1992; and Hand et al., 1992), wefind that upgrades are less frequent than downgrades—the intercept for downgradesis significantly greater than the intercept for upgrades in both equations (3) and (4).This may be driven, in part, by the upper bound on debt ratings.

The estimated coefficients on the included financial variables are consistent withprior findings (Ziebart and Reiter, 1992), although not all of the coefficients arestatistically significant. We find evidence that reclassification increases the likelihoodof a debt-rating downgrade—the coefficient on RECLAMTΔ is negative in equation3 (p < 0.01). Distinguishing between the directions of changes in reclassificationbehavior (i.e., initial reclassification, ongoing reclassification and declassification) inequation (4), significantly improves the power of the model. Specifically, the chi-squaremodel likelihood increases from 82.45 (equation (3)) to 112.85 (equation (4)). Thecoefficient on initial reclassified amounts (START × RECLAMT) is negative and highlysignificant (β 10 = −12.098, p < 0.01), implying that credit analysts view reclassificationas a ‘red flag,’ perhaps because they have private information about debt covenantviolations or other economic factors related to the firms’ credit risk, or informationabout management’s intent to manage the firms’ financial reports. Contrary toexpectations, we find a statistically insignificant coefficient on the interaction thatcaptures declassification (STOP × RECLAMT). Thus, firms previously punished forreclassifying (with lowered bond ratings) do not appear to be rewarded when theydeclassify.

17 We also consider lagged debt ratings in these regressions as well as changes in debt ratings includingthe important drop from investment grade. These debt-rating coefficients (untabulated) were weak andmixed. Thus we conclude that firms do not appear to consider past debt-rating levels or changes in makingreclassification choices.18 We also estimate the models in Table 4 using ordered logistic regressions as suggested by Ederington(1985). Results (not reported here) confirm that initial reclassification exhibits the strongest associationwith debt-rating changes and that reclassification explains downgrades more than upgrades. We find weakerevidence that reclassification makes a difference in explaining upgrades compared to no changes in debtratings. Our findings corroborate prior research that reports stronger evidence for downgrades than forupgrades (Hand et al., 1992; and Barron et al., 1997).



Tab

le4

Ass

oci

atio

nB

etw

een

Recl

assi

fied

Am

ou

nts

and

Deb

tR

atin

gC

han

ges

Eq

uat

ion

(3)

:R

AT

ING

Δi,

t0+1

=β

0+

β1+

β2R

OAΔ

i,t+

β3L

EVΔ

i,t+

β4C

RA

TIO

Δi,

t+

β5C

FO

Δi,

t+

β6SIZ

EΔ

i,t

+β7R

EC

LA

MTΔ

i,t+

19 ∑ j=8

βjI

ND

i+

υi,

t

Eq

uat

ion

(4)

:R

AT

ING

Δi,

t0+1

=β

0+

β1+

β2R

OAΔ

i,t+

β3L

EVΔ

i,t+

β4C

RA

TIO

Δi,

t+

β5C

FO

Δi,

t+

β6SIZ

EΔ

i,t

+β

7R

EC

LA

MTΔ

i,t+

β8ST

AR

Ti,

t+

β9ST

OP

i,t+

β10(S

TA

RT

i,t×

RE

CL

AM

TΔ

i,t)

+β

11(S

TO

Pi,

t×

RE

CL

AM

TΔ

i,t)

+23 ∑ j=12

βjI

ND

i+

υi,

t

Equa

tion

(3)

Equa

tion

(4)

Vari

able

(Pre

dict

edsi

gn)

Estim

ate

(χ2)

Estim

ate

(χ2)

Inte

rcep

tfo

ru

pgra

de

(?)

−2.1

4∗∗

∗−2

.144

∗∗∗

(77.7

8)

(75.7

2)

Inte

rcep

tfo

rd

ow

ngra

de

(?)

1.1

71

∗∗∗

1.2

00

∗∗∗

(24.7

1)

(25.0

4)

RO

AΔ

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(iv) Results of Tests for an Association Between Reclassification and Market Value ofEquity

Table 5 reports results for our ordinary least squares regression of market values ofequity on book values of assets and liabilities, and abnormal earnings (i.e., equation(5)). Contrary to our expectation, the coefficient on RECLAMT is not statisticallysignificant. Thus, whether a firm reclassifies short-term obligations does not appear tobe an equity value-relevant signal.

However, we find significant results when we distinguish among reclassificationbehaviors: equation (6) includes indicator variables and interaction terms to examinethe separate effects of initial reclassification and declassification. Neither of thecoefficients for the indicator variables START or STOP is significantly different fromzero. However, we find that the market value of equity decreases when the amount ofreclassified debt increases (β 6 = −1.243, p = 0.0259). On average, for every additionaldollar of debt reclassified, equity market value decreases by $1.24, controlling for thelevels of assets, liabilities and abnormal earnings.

Table 5 also shows that when firms cease reclassification, market value increases sig-nificantly in relation to the amount declassified; the coefficient on STOP × RECLAMTis 2.659 (p < 0.01). This implies that for every dollar of short-term obligations returnedto the short-term liability section of the balance sheet, the average firm’s market valueincreases by $2.66. Investors apparently perceive declassification as a very positive signal.

Comparing the results of equations (5) and (6), we learn that it is not merely theact of reclassification that impacts firm value. Investors apparently pay attention tothe magnitude of the change in the reclassified amount. We reiterate that we cannotexplicitly identify the mechanism by which reclassification and declassification affectmarket value. However, by identifying reclassification and declassification as value-relevant signals, we have identified a leading indicator that is publicly available toinvestors and other market participants.

(v) Robustness Tests

We performed a number of tests to assess the robustness of our findings. Noneof the tests described here changed our inferences. We omitted outliers identifiedusing methods advocated by Belsley, Kuh, and Welsch (1980) in all the regressionmodels reported in Tables 3 through 5. We winsorized observations in the lowest andhighest percentile for the continuous variables in the logistic and ordinary least-squaresregression models to remove the potentially powerful effects of extreme observations.In our debt-rating models (Table 4), we deflated the continuous financial variablesby book value as well as by market value of firms’ equity instead of by total assets. Weconsidered a number of other financial variables in our debt-rating models, includingvariables that measured interest expense and interest coverage, working capital levelsrather than current ratio, and indicator variables for each year in the time-series; noneof these variables improved the models’ explanatory power or changed our inferences.

5. CONCLUSION

This study finds that firms’ reclassification of short-term debt to long term is notinnocuous balance sheet presentation. The reclassification decision appears to be adeliberate financial reporting strategy with economic implications and consequences.



Table 5

Association Between Reclassified Amounts and Market Value of Equity

Equation (5) : MVEi,t =β0 + β1BVAi,t + β2BVLi,t + β3AEi,t + β4RECLAMTi,t + υi,t Equation (6) :

MVEi,t = β0 + β1BVAi,t + β2BVLi,t + β3AEi,t + β4STARTi,t + β5STOPi,t+β6(RECLAMTi,t − RECLAMTi,t−1)

+β7(STARTi,t × RECLAMTi,t)

+β8(STOPi,t × RECLAMTi,t−1) + υi,t

Equation (5) Equation (6)Variable (Predicted sign) Estimate (t-statistic) Estimate (t-statistic)

Intercept (?) 911.453∗ ∗ ∗

1, 102.450∗ ∗ ∗

(3.10) (3.47)BVAi,t (+) 2.945

∗ ∗ ∗2.964

∗ ∗ ∗

(27.45) (28.10)BVLi,t (−) −3.099

∗ ∗ ∗ −3.132∗ ∗ ∗

(−23.12) (−23.46)AEi,t (+) 12.870

∗ ∗ ∗13.170

∗ ∗ ∗

(24.05) (24.39)RECLAMTi,t (−) 0.139

(0.41)STARTi,t (−) −905.634

(−1.08)STOPi,t (+) −1, 434.492

(−1.48)RECLAMTi,t − RECLAMTi,t−1 (−) −1.243

∗ ∗

(−2.23)STARTi,t × RECLAMTi,t (−) 1.052

(1.00)STOPi,t × RECLAMTi,t−1 (+) 2.659

∗ ∗ ∗

(2.18)Adjusted R2 55.9% 56.3%

Notes:The table reports parameter estimates and t-statistics for an OLS regression using data for each firm i andyear t.The sample consists of 1,684 observations: 932 reclassifying firm-years and 752 non-reclassifying firm years.MVE is the market value of common equity measured three months after the end of year t.BVA is end-of-year total assets in $ millions.BVL is end-of-year total liabilities in $ millions.AE is abnormal earnings measured by earnings before extraordinary items less 12 percent of prior-year netbook value (i.e., BVA minus BVL).RECLAMTt is the amount of short-term debt reclassified as long-term in year t.START is a binary variable indicating ‘1’ if short-term obligations are reclassified in the current year but notin the prior year, and zero otherwise.STOP is a binary variable indicating ‘1’ if short-term obligations are reclassified in the prior year but not inthe current year, and zero otherwise.∗∗∗ Significant at the 1% level in a two-tailed test.∗∗ Significant at the 5% level in a two-tailed test.∗ Significant at the 10% level in a two-tailed test.

Consistent with prior research, we show that firms reclassify when they need to (i.e.,when current ratio is lower than in the previous year or lower than the industrybenchmark) and when they can afford to (i.e., when overall leverage is lower thanin the previous year or lower than the industry benchmark).



We find that firms’ debt ratings are negatively affected by reclassification. Inparticular, initial reclassification increases the likelihood of a rating downgrade. Thisevidence implies that although managers may strategically classify short-term debtas long-term, credit analysts do not reward this behavior with increased ratings or,consequently, lower cost of capital. We also find that the market value of firms’ equityis associated with the decision to declassify short-term obligations, but not with thedecision to initially reclassify debt. Although declassification saves the firm real costs(i.e., the elimination of long-term loan commitment fees), the magnitude of this costsaving cannot explain the magnitude of the price changes we document.

Firm reclassification decisions are not likely to have caused changes in debt ratingsor market values. Rather, the economic consequences that we document are likelycaused by other factors that are correlated with firms’ reclassification decisions. Sowhile our models may suffer from correlated omitted variables problems, we contendthat the publicly available reclassification and declassification actions proxy for theseunspecified unobservable variables.

Overall, our results imply that debt reclassification signals bad news—it is a red flag tocapital market participants. Conversely, declassification signals good news. As such, ourfindings are important to creditors, investors, and other market participants who seekinformation concerning debt and equity prices. Moreover, this study contributes to thegeneral understanding of the determinants and consequences of accounting choiceas it pertains to the balance sheet. Much of the extant research on accounting choicefocuses on earnings management (see Holthausen and Leftwich, 1983; and Fields et al.,2001), while comparably little has been written about balance sheet management. Thisperhaps is because managerial motivation is less obvious for balance sheet accountsthan for earnings (although see Imhoff and Thomas, 1988; Mohr, 1988; and Hopkins,1996).

Our findings suggest several avenues for further research. For example, onemight explore whether firms that engage in balance sheet management via debtreclassification also engage in income statement management. In particular, futureresearch could explore whether the quality of firms’ earnings, perhaps measured bythe persistence of earnings, cash flows and/or accruals, is related to decisions to initiateor cease reclassification. Alternately, one might assess whether firms that reclassify doso in conjunction with deliberate economic choices that impact earnings. These sortsof studies would provide evidence on the simultaneous management of the balancesheet and income statement and would speak to management’s broader reportingphilosophy.

Future research might profitably extend our study by using international dataparticularly when firms adopt IAS standards in 2005. IAS 1, which addresses thephenomenon we document in this paper, is more stringent in some respects thanSFAS 6 .19 Consequently, the market is likely to learn of potential liquidity problemssooner. In the extreme, this IFRS mandate for classifying short term may be tooconservative, causing covenant breaches and potentially causing firms that have noliquidity problems appear as if they do (Ormrod and Taylor, 2004). Whether the

19 Specifically, under IAS 1, Presentation of Financial Statements, firms that have long term debt covenantbreaches or debt maturing within the next fiscal year must classify the debt as current even if the firm 1)obtains a waiver on the covenant breach after the balance sheet date or 2) actually refinances the debt afterthe balance sheet date but before the release date of the financial statements.



equity and debt markets will view classification toward short term debt under IFRSas a ‘problem’ or not is an empirical question that future research could investigate.Conversely, compared with many domestic accounting standards, IAS standards providemore leeway in measuring and classifying balance sheet items that affect debt covenants.Specifically, the increased discretion provided by IAS standards regarding accruals andthe definition of current versus non-current assets and liabilities, may enable firmsto avoid debt covenant violations (Ormrod and Taylor, 2004). Thus, future researchcould investigate the extent to which firms exercise discretion over the measurementand classification of current or non-current items in the new IAS regime, and the debtand equity market consequences of such decisions.

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