Journal of Business Finance & Accounting, 33(7) & (8), 1189–1212, September/October 2006, 0306-686X
doi: 10.1111/j.1468-5957X.2006.00593.x
Debt Reclassification and Capital Market
Consequences
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 shortterm obligations as long-term debt in accordance with Statement of Financial Accounting Standard
No. 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 consequences
of a 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 Standard No. 6 (SFAS 6). Gramlich, McAnally and
Thomas (2001), (hereafter GMT) document that firms use the flexibility afforded
by SFAS 6 to smooth key liquidity and leverage ratios toward both industry benchmarks and prior-year levels. GMT demonstrate that firms shift short-term debt to
the 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 the
comments of Shane Dikolli, Michelle Hanlon, Karim Jamal, Ross Jennings, Bill Kinney, Lisa Koonce, Tom
Scott, Senyo Tse and Connie Weaver as well as from workshop participants at the University of Alberta and
the 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, Texas
A&M University, College Station, TX 77843, USA.
e-mail: MMcAnally@mays.tamu.edu
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Journal compilation and 350 Main Street, Malden, MA 02148, USA.
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firms move such debt back to the current category (i.e., ‘declassify’). These classification changes reduce the variability of firms’ current and long-term debt ratios
across time and mitigate the deviation of liquidity ratios from industry norms. We
extend GMT by directly addressing the economic relevance of debt classification.
In particular, we contend that debt classification is not an innocuous financial
reporting decision—managers do not accidentally reclassify debt—and some of the
motivation is apparently driven by the desire to portray the firm as healthier than
it actually is. We provide empirical evidence economically linking firms’ debt classification decisions to stakeholder wealth.
SFAS 6 permits a firm to reclassify short-term debt as long-term if a loan commitment
is obtained that extends for more than a year beyond the balance sheet date. Our
data reveal that each year from 1989 to 2000, about $26.7 billion of commercial
paper (which the SEC defines as short-term) is classified as long-term debt. This
averages $564 million per firm each year during our sample period. The disclosure
in Exhibit 1 typifies SFAS 6 debt reclassification. Firms must exclude short-term
obligations from current liabilities if the firm (1) intends to replace the maturing
short-term debt issue with another issue; and (2) has the ability to do so. 1 Firms
demonstrate ‘ability’ with credit-facility terms that extend beyond the term necessary
to support the short-term obligation. While the standard states that firms ‘shall’
reclassify, in practice, considerable discretion remains. To exercise discretion a firm
can either declare that it has no intent to replace the current debt issue or fail to
secure appropriate enabling credit facilities. Ceteris paribus, long-term credit facilities
are more costly than short-term facilities; thus, firms incur real costs to enable
reclassification. Sound economic reasons may exist for firms to obtain longer-term
loan commitments, such as less costly commercial paper rollovers and longer-term
invested capital. However, we consider the possibility that some firms strategically
reclassify and declassify debt and that these strategy decisions impact stakeholder
wealth.
To substantiate our claim that debt reclassification is not an innocuous financialreporting choice, we first develop and estimate a model that explains reclassifications. The evidence indicates that firms with lower leverage, current ratio, and
operating cash flows more frequently reclassify short-term debt as long-term. This
suggests that managers reclassify to obscure the firm’s true financial condition and
not to simply reveal the likely timing of debt repayments.
We also assess whether changes in classification systematically predict differences in the costs of debt and equity capital. Although other research ties earnings properties to shareholder wealth (e.g., Kormendi and Lipe, 1987; Ohlson,
1995; Sloan, 1996; and Barth, Beaver et al., 1999), we examine whether balance
sheet debt reclassification is associated with subsequent changes in debt ratings.
After controlling for demographic and financial variables known to influence
debt 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 intends
to refinance the obligation on a long-term basis and 2) the enterprise’s intent to refinance the shortterm obligation on a long-term basis is supported by an ability to consummate the refinancing (which
is) demonstrated . . . by a financing agreement that clearly permits the enterprise to refinance the short-term
obligation on a long-term basis on terms that are readily determinable and . . . the agreement does not expire
within 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).
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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
12/31/97
First mortgage and collateral trust bonds
Maturing 1999 through 2003/5.9%–9.5%
$ 816.4
$ 1,005.6
Maturing 2004 through 2008/5.7%–7.9%
1,032.7
632.7
Maturing 2009 through 2013/7%–9.2%
328.6
331.6
Maturing 2014 through 2018/8.3%–8.7%
98.4
100.9
Maturing 2019 through 2023/6.5%–8.5%
341.5
341.5
Maturing 2024 through 2026/6.7%–8.6%
120.0
120.0
Guaranty of pollution control revenue bonds
5.6%–5.7% due 2021 through 2023 (a)71
71.2
71.2
Variable rate due 2009 through 2013 (a) (b)
40.7
40.7
Variable rate due 2014 through 2024 (a) (b)
175.8
175.8
Variable rate due 2005 through 2030 (b)
450.7
450.7
Funds held by trustees
(7.4)
(9.1)
8.4%–8.6% Junior subordinated debentures due
175.8
175.8
2025 through 2035
Commercial paper (b) (d)
116.8
120.6
Other
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
SUBSIDIARIES
6.1%–12.0% Notes due through 2020
649.8
264.5
Australian bank bill borrowings and commercial paper (c) (d)
414.3
756.6
Variable rate notes due through 2000 (b)
11.6
12.1
4.5%–11% Non-recourse debt
–
160.7
Other
–
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 upon
management’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 that
declassifying 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 and
increases when firms cease reclassifying.
Taken together, our findings suggest that capital-market participants view reclassification as a ‘red flag’ indicative of management intervention in the reporting process.
Our debt-rating and market-value results potentially provide firm managers with a
better understanding of the economic consequences of their reclassification decisions.
Additionally, our findings provide evidence concerning an accounting choice that
influences debt-covenant compliance. Firms and lenders could use these findings to
structure debt covenants that either explicitly allow or disallow the reclassified amount
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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 whether
the alleged attempts to manage financial disclosures by self-interested managers are
successful’ (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 Section
5 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 a
firm’s total liabilities, it simultaneously increases both the current ratio and the longterm debt ratio. Firms may reclassify because they believe that external parties monitor
firm liquidity via the current ratio, for example. These parties could include equity
analysts who appraise firms’ ability to meet obligations, lenders who set and enforce
debt 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 these
parties’ decisions is difficult: most credit-scoring systems are proprietary and typical
debt-covenant footnotes contain only boilerplate language. 3 Thus, to build our model,
we consider that credit-scoring models and debt covenants typically specify minimum
levels 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 more
likely to reclassify. Conversely, credit-scoring models and debt covenants typically specify
maximum levels for total leverage or long-term debt. While reclassification improves
liquidity measured by the current ratio, it increases leverage measured by long-term
debt ratios. Consequently, we expect only firms with long-term debt slack to reclassify
short-term debt to long-term. That is, reclassification may only be viewed as a viable
alternative 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 such
as operating cash flow and profitability would be negatively related to the reclassification
decision. Thus, we predict:
H1:
Firms with lower current ratios, lower long-term debt leverage, lower operating
cash flows and lower profitability are more likely to classify short-term
obligations as long-term debt.
2 Mester (1997) reports that 70 percent of banks use multivariate credit-scoring models to make commercial
lending decisions.
3 We searched Dealscan, Loan Pricing Corporation’s commercially available database of lending agreements
with over 100,000 transactions on global loans, high-yield bond, and private placements since 1986. Dealscan
summarizes specific loan information, including borrower, lender, amount, term, debt covenant data and
sinking fund requirements. Although the December 29, 1999, version contains 1,355 lending deals with
current 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 potential
debt-covenant violation hypotheses.
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(ii) Economic Consequences of Reclassification
Innocuous debt classification decisions would have no market consequences. On
the other hand, if debt classification imparts useful information to debt and equity
markets, capital-market activity may reveal these stakeholders’ views of forecasted future
cash flows. By testing for capital-market responses to debt classification, we ascertain
whether debt classification systematically predicts real economic effects to stakeholders.
Specifically, we examine the impact of reclassification and declassification on debt
ratings 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 and
Phelps glean private information in evaluating firms’ credit worthiness. 4 Prior research
establishes that credit analysts have economic incentives to reveal their private
information (Millon and Thakor, 1985). We argue that debt-rating agencies may
privately learn firms’ rationale for reclassifying and factor that information into their
debt 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 of
existing debt agreements, debt raters may discern whether a firm reclassified short-term
debt to avoid violating a debt covenant. This knowledge could directly affect a firm’s
debt rating because debt-covenant violation has the potential to impact cash flows.
Second, debt raters may ascertain that reclassification is related to other economic or
managerial factors that affect firms’ credit ratings. 5 This knowledge could indirectly
affect a firm’s debt rating. We consider both links below.
Debt-covenant information is more-readily available to credit analysts than to
financial statement readers. For example, consider the Pacificorp reclassification
presented in Exhibit 1. Dealscan reports that a 1998 debt covenant required that
Pacificorp Inc. maintain a current ratio of at least 1.1 to 1. This covenant was
not explicitly reported in the company’s financial statements that year, although
presumably debt raters access the same information used to develop the Dealscan
database. In 1998, Pacificorp reclassified $531 million of commercial paper from
current liabilities to long-term debt. The effect of this reclassification was to increase
the company’s 1998 current ratio from 0.827 to 1.105 – just above the 1.1 level
needed to avoid a covenant violation. Our conversations with several partners at public
accounting 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 percent
of survey respondents reported meeting with the agencies more than twice a year. Trade publications report
corroborative anecdotal evidence, ‘Larger companies are usually visited annually by Moody’s personnel with
supplemental visits by management to New York. We often arrange visits to the operations of individual
business 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 February
1991, AA-rated Shell Canada provided its rating agency with ‘advance insider information: Shell Canada’s
decision 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 domestic
Canadian agencies. . . . The analysts never blinked; Shell Canada was not downgraded at the time, nor was it
put on a dreaded credit watch’ (Picker 1991, p. 76).
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facing debt-covenant violations. The propensity to heed such advice may signal
management’s broader predilection to intervene in the reporting process.
Debt-raters also have access to private information gleaned in private discussions
with 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 and
overall philosophies. To the extent that reclassification is associated with this private
information, debt ratings will be associated with reclassification. Although we do not
posit that reclassification is a direct determinant of firm’s credit ratings, we argue
that reclassification serves as a proxy for credit analysts’ private information, including
insight into debt covenant proximity, economic conditions and management’s financial
reporting strategies. In other words, debt reclassification signals firm weakness that debt
raters can directly assess using private information. Thus, our second hypothesis is:
H2:
Debt ratings are negatively (positively) related to the reclassification (declassification) 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-day
loan commitment exceeds the cost of a 90-day commitment. However, in most cases
these costs are not likely to be large enough to have a statistically measurable impact
on firm value. Apart from the loan-commitment fee, reclassification does not directly
affect earnings nor does it appear to impact firm value. Nonetheless, as discussed
above, we maintain that reclassification is a value-relevant signal. Consistent with prior
research, we cannot specify the mechanism by which managers’ choice to reclassify
current 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 the
reclassification (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 the
period 1989 to 2000. 6 If a firm met the search criteria at any time within the 12-year
period, we collected short and long-term debt footnotes for that firm for the complete
period 1988 to 2000. 7 This approach identified a total of 3,080 firm-years. We gathered
additional 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 not
mention commercial paper.
7 Because certain variables require lagged data, we also gathered data for 1988.
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CRSP databases. Missing Compustat data and insufficient current-year and prior-year
footnote disclosures reduced our sample as indicated in Table 1, Panel A. The final
sample 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 1989
and 2000
Firm-years between 1989 and 2000 identified with search terma
Less: Firm-years not available on Compustat
Firm-years missing relevant Compustat and/or CRSP variables
Firm-years missing sufficient current debt footnote information
Firm-years missing sufficient prior-year debt footnote information
3,080
(462)
(465)
(346)
(123)
Final sample
1,684
Panel B: Industry Affiliation for Samples Resulting From Each Criteria
2-digit
Reclassifying
Non-reclassifying
Industry Group †
SIC Codes
Firm-Years
Firm Years
Clothing
Financial
Food
Media
Metallurgy
Miscellaneous manufacturing
Oil
Retail sales
Services and other
Transport
Utilities
Wood products
Healthcare
22–23
60–69
1–7,20–21
27,48
34
39
13,46
50–59
70–79,83,99
40–45,47
49
24–26
80
Totals
Panel C: Sample Distribution Across Years
Reclassifying
Year
Firm-years
Total
Sample
5
35
52
361
40
0
72
104
62
47
56
86
12
24
28
62
231
23
7
17
94
55
21
74
56
0
29
63
114
652
63
7
89
198
117
68
130
142
12
932
752
1,684
Non-reclassifying
Firm-years
Total
Sample
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
49
53
47
48
62
63
87
104
111
113
103
92
35
31
37
36
73
103
86
82
71
67
67
64
84
84
84
84
136
166
173
186
182
180
170
156
Totals
932
752
1,684
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Table 1(Continued)
Notes:
The table reports sample selection results. We applied the following search term to the AR group file within
the NAARS library of Lexis/Nexus for the years 1989 through 1994: ((DATE = 1989) AND (COMMERCIAL
PAPER W/200 (CLASSIF! W/25 (LONG-TERM OR NONCURRENT)) AND (COMMERCIAL PAPER
W/200 (DUE W/10 1990)))). From 1995 through 2000, we applied the term to the COMPNY group file
within 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 collected
that firm’s financial data for all years between 1989 and 2000, producing 3,080 firm-year observations. The
final sample contains only firms that reclassify short-term debt to long-term at some point during the 1989
to 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, including
the amount and type of short-term debt reclassified along with information pertaining
to the terms of supporting loan commitments, interest rates, fees and compensating
balances. We also searched the disclosures for information about debt covenants and
any 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 pursuant
to the ‘intent and ability’ paragraph of SFAS 6.
(ii) Models
We first attempt to explain firms’ decisions to reclassify and then we test for an
association between reclassification (and declassification), debt ratings, and market
value of equity. That is, we estimate pooled, cross-sectional time-series models using nonreclassification firm-years as a control for reclassification firm-years. In later sensitivity
analysis, we match reclassification firms with firms that exhibit no reclassification activity
at any point during the 1989–2000 period. The results we find with this matched sample
are 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’ decisions
to reclassify:
RECLASSi, t = β0 + β1 ROAi, t + β2 LEVi, t + β3 CRATIOi, t + β4 CFOi, t
+ β5 RECLASSi, t−1 + υi, t
(1)
where RECLASS i,t is a binary variable indicating one if firm i reclassified short-term
debt as long-term in year t, and zero otherwise; ROA is earnings before extraordinary
items 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 and
current ratio covenants, since such ratios are directly impacted by reclassification. Dealscan, however,
provided limited details regarding these covenants by our sample firms.
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current assets divided by current liabilities; CFO is cash flow from operations scaled
by total assets; and is the unexplained residual. Both CRATIO and LEV are calculated
after adjusting for reclassification.
Lev (1969) reports that firms’ financial ratios adjust toward the previous year’s
industry averages. Among the six financial ratios Lev examines, the quick and current
ratios exhibit the fastest and most significant adjustments toward industry averages. 9
Thus, it is plausible that firms reclassify to avoid deviation from industry benchmarks for
certain key metrics. To address this in our examination of hypothesis 1, we control for
industry norms by subtracting the annual industry median from each of our continuous
independent 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 )
+ β5 RECLASSi, t−1 + υi, t .
(2)
We compute industry medians for two-digit SIC codes using data from the entire
Compustat 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 logistic
regression of the direction of debt-ratings changes. 10 Our model includes demographic
and financial variables suggested by Ziebart and Reiter (1992). We read Standard and
Poor’s ‘Corporate Ratings Criteria’ (Standard and Poor’s 2001) to determine additional
factors that credit analysts deem relevant. Thus, the model includes a parsimonious set
of control variables suggested by theory and practice to test hypothesis 2.
RATINGΔi, t0+1 = β0 + β1 + β2 ROAΔi, t + β3 LEVΔi, t + β4 CRATIOΔi, t + β5 CFOΔi, t
19
+ β6 SIZEΔi, t + β7 RECLAMTΔi, t +
β j INDi + υi, t
j =8
(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 define
RATINGΔ as ‘1’ if the firm’s debt rating improves (upgrades), as ‘-1’ if the firm’s
rating deteriorates (downgrades); and as ‘0’ if the rating does not change. Thus,
RATINGΔ i,t0+1 , captures the cumulative directional change in RATING i , 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 problem
since ratio adjustment may be achieved in several ways. . . . there is no way to identify specific techniques
which probably differ from firm to firm’ (Lev 1969, p. 298). We hypothesize that reclassification may be one
such 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 firms
have commercial paper ratings and commercial paper ratings have fewer distinct ratings levels (7 possible
ratings for commercial paper compared to 27 possible ratings for long-term debt).
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years t and t + 1. We examine both years t and t + 1 because empirical and anecdotal
evidence 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 are
often placed on Standard & Poor’s CreditWatch before the rating is formally changed.
The independent variables in equation (3) measure changes during year t in
previously-defined variables so that both the dependent and independent variables
reflect 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 change
in the dollar amount of reclassified short-term obligations scaled by total assets. We
also include a set of 12 indicator variables, IND j , to capture the firm’s industry. In their
Corporate Rating Criteria, S&P explicitly states that they take industry information into
account when forming a rating. Therefore, we assign firms to industry groups based
on S&P’s Global Industry Classification Standard. Table 1, Panel B provides the S&P
industry group definitions.
To further test hypothesis 2, we include terms to examine the effects of decisions
to begin and end the practice of reclassifying debt on the balance sheet (i.e.,
reclassification changes):
RATINGΔi, t0+1 = β0 + β1 + β2 ROAΔi, t + β3 LEVΔi, t + β4 CRATIOΔi, t + β5 CFOΔi, t
+ β6 SIZEΔi, t + β7 RECLAMTΔi, t + β8 STARTi, t
+ β9 STOPi, 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 START t−1 as ‘1’ if the firm began reclassifying short-term debt
during year t − 1, and STOP t−1 as ‘1’ if the firm stopped reclassifying all short-term debt
during year t − 1 (i.e., the firm reclassified debt in year t − 2 but did not reclassify in
year t − 1). The interaction terms multiply indicator variables START and STOP by the
amount of reclassified short-term obligations scaled by total assets.
Ceteris paribus, debt-rating upgrades (downgrades) are more likely for firms with
increasing (decreasing) profitability, liquidity and cash flow, so that the control variables
ROAΔ, CRATIOΔ and CFOΔ should have positive coefficients. In contrast, firms with
increasing (decreasing) leverage are less (more) likely to experience positive (negative)
debt rating changes; thus, we expect a negative coefficient for LEVΔ. We offer no
prediction for SIZEΔ, a growth measure. Consistent growth could signal healthy cash
flow and stable management, but rapid growth might also make the company too
difficult to manage or imply more future borrowing. 13 We expect that a firm is more
likely to receive a debt-rating downgrade when the amount reclassified increases during
the 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 not
change 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 from
companies’ actions to defend themselves against takeover (Picker, 1991).
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1199
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 likely
when 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 reclassified
liabilities are incrementally value-relevant to the amount of total liabilities (i.e,
hypothesis 3):
MVEi, t = β0 + β1 BVAi, t + β2 BVLi, t + β3 AEi, t + β4 RECLAMTi, t + υi, t .
(5)
We measure the market value of the firm’s common equity (MVE t ) three months
after the end of fiscal year t to ensure that the firm’s stock price impounds the
reclassification information reported in the footnotes to the financial statement for
fiscal year t. 14 BVA is the book value of total assets; BVL is the book value of total
liabilities including the reclassified amount. To calculate abnormal earnings AE, we
first calculate an expected return as twelve percent of the book value of equity at the
beginning of the year (i.e. 0.12 × [BVA t−1 − BVL t−1 ]). 15 AE is the difference between
reported earnings and the calculated expected return. RECLAMT t is the dollar amount
of short-term obligations reclassified as long term during year t. Consistent with many
prior studies (see Barth et al., 2001 for a summary), we expect that BVA and BVL will
indicate positive and negative coefficients, respectively. If reclassification is a signal
of management’s intervention in the financial reporting process, greater amounts of
reclassified obligations will be associated with lower firm value (i.e. the coefficient on
RECLAMT will be negative).
Alternatively, to test whether changes in reclassification have an even greater impact
on market values than the level of reclassified short-term obligations, we refine equation
(5) to estimate a model that separately examines coefficients for amounts initially
classified and declassified amounts. 16 When reclassification occurred in the prior year
but no reclassification occurs in the current year, the prior-year reclassification amount
is considered declassified:
MVEi, t = β0 + β1 BVAi, t + β2 BVLi, t + β3 AEi, t + β4 STARTi, t + β5 STOPi, 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 fiscal
year t and our inferences do not change.
15 Abarbanell and Bernard (2000) report consistent results for abnormal earnings calculated with discount
rates ranging from 9 to 15 percent. Their calculations hold rates constant across time and firms. As a
robustness test, we also calculate abnormal earnings using alternative rates and our findings are qualitatively
unchanged.
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 reclassified
amount (current minus lagged reclassified amount) in lieu of each variable separately. This facilitates the
interpretation of the estimated coefficient.
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We predict that initial reclassification will decrease equity values (i.e. β 4 < 0 and
β 7 < 0). Further, declassification provides a signal that economic conditions will
improve and therefore declassification is predicted to be associated with greater equity
values (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 sampleselection process and Panel B shows that 932 of the 1,684 sample firm-years indicate
reclassifications of short-term debt as long-term. As Panel B shows, the sample appears
to be heavily weighted in media industries (i.e., SIC 27 and 48) but both reclassification
and non-reclassification firm-years are fairly well distributed among the other industry
groups. Panel C reveals that reclassification activity increased substantially across
the decade, beginning with only 49 reclassifications in 1989 and peaking with 113
in 1998.
Table 2 compares reclassifying and non-reclassifying firm-years on several dimensions. When firms reclassify, they are larger, as measured both by the mean and
median 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 growth
rates (p < 0.01). The amount reclassified is statistically significant, as indicated by the
$564 million mean ($252 million median) amount reclassified (p < 0.01). Reclassification significantly changes long-term debt and current ratios: mean and median longterm debt ratios, as reported on the balance sheet, are greater for reclassifying firm
years than for non-reclassifying firm years (p < 0.01). But backing out the reclassified
amount reverses the direction of the difference: reclassifiers’ mean and median longterm debt ratios are statistically smaller than those of non-reclassifiers (p < 0.01). More
dramatic, however, is the comparison of the current ratio before and after the effects
of reclassification. In particular, the mean current ratio without reclassification is less
than 1, reclassification boosts the mean above 1 (mean = 1.202). Comparing the nonreclassifying and reclassifying firm years, we see that reported mean and median current
ratios are lower in reclassifying firm years than in non-reclassifying firm years (1.20
compared to 1.48). Without reclassification the differences are startling (0.95 compared
to 1.48).
(ii) Findings Regarding Firms’ Reclassification Decisions
Table 3 reports results for our logistic regressions (equations (1) and (2)) that explain
firms’ reclassification decisions. Both equations compare firm-years without reclassification 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 current
ratio, the more likely the firm is to reclassify, as indicated by the negative coefficient
on CRATIO (p < 0.01). The negative coefficient on LEV (p < 0.01) also indicates that
firms reclassify when they can afford to—when leverage is low and can afford to have the
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Assets, $ millions
Market value of equity,
$ millions
Return on assets
Cash from operations
to assets
Asset growth
Reclassified amount,
$ millions
Long-term debt to assets,
as reported
Long-term debt to assets,
without reclassification
Current ratio, as reported†
Current ratio, without
reclassification†
Number (percent) of
debt-rating downgrades
Attributes
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Journal compilation 1.546
1.190
0.221∗∗
1.266∗
1.266∗∗
0.234∗∗
1.480∗∗
1.480∗∗
(17.0%)
1.018
0.740
0.269
0.221
4.1%
0
4.1%
9.4%
3,449
2,297
Median
0.963
0.963
0.135
0.135
−2.8%
0
1.9%
5.5%
1,607
1,087
25 th
Percentile
Firm-years Without Reclassification
(N = 752)
0.234
8.6%
0
4.1%
9.3%
6,179
6,489
(19.0%)
1.202
0.950
1.330
1.012
0.111
0.344
15.1%
590
7.2%
13.5%
11,466
9,357
Mean
128
0.195
0.204
0.184
−1.2%
100
2.0%
6.5%
2,158
1,837
75 th
Percentile
177
0.268∗∗
5.6%∗∗
252∗∗
21.9%∗∗
564∗∗
0.275∗∗
4.2%
9.8%∗
5,022
3,761∗∗
∗∗
Median
4.4%
10.1%∗
9,332
8,217∗
∗∗
Mean
25 th
Percentile
Firm-years With Reclassification
(N = 932)
Table 2
Comparison of 1,684 Firm-years With and Without Reclassification Between 1989 and 2000
1.744
1.744
0.311
0.311
12.0%
0
7.6%
13.3%
6,495
5,302
75 th
Percentile
DEBT CLASSIFICATION AND MARKET CONSEQUENCES
1201
(12.0%)
(10.6%)
Mean
90
Median
99
Mean
75 th
Percentile
Median
25 th
Percentile
Firm-years Without Reclassification
(N = 752)
75 th
Percentile
Notes:
The table reports mean, median, 25th and 75th percentile levels for 12 attributes for 932 reclassifying firm-years and 752 non-reclassifying firm-years. Assets and the
market value of equity are measured at the end of the year. Return on assets is income before extraordinary items divided by end-of-year total assets. Cash from
operations is as reported on the cash flow statement, divided by end-of-year total assets. Asset growth is the percentage change in total assets from the beginning to
end of year. The reclassified amount is the dollar amount of short-term debt reclassified as long-term pursuant to SFAS 6, as reported in the firm’s financial statement
footnote. Long-term debt is divided by end-of-year total assets, and is shown two ways: first as reported on the financial statement and second after removing the
amount of short-term debt reclassified to long-term. Current ratio is current assets divided by current liabilities and is shown two ways: first as reported on the financial
statement and second after replacing the amount of short-term debt reclassified to long-term.
† Compustat does not report values of both current assets and current liabilities for all firms in the sample. Of 932 (752) reclassification (non-reclassification)
firm-years, 867 (707) report data sufficient to compute the current ratio.
∗ (∗∗) Indicates that either ‘firm-years with reclassification’ or ‘firm-years without reclassification’ measure is larger and significant at the 0.05 (0.01) level using a
two-sample t-test of means, a Wilcoxon signed-rank tests of medians or, in comparing proportions of firm-years with debt rating changes, a non-parametric binomial
test. Significance levels are reported assuming a two-tail distribution.
Number (percent) of
debt-rating upgrades
Attributes
25 th
Percentile
Firm-years With Reclassification
(N = 932)
Table 2(Continued)
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(−)
(−)
(−)
(−)
(+)
Intercept
ROA i,t
LEV i,t
CRATIO i,t
CFO i,t
RECLASS i,t−1
Model likelihood Chi-square
Psuedo-R 2
(Predicted sign)
Variable
Equation (2)
Estimate (χ 2 )
−1.044∗∗∗
(125.26)
0.986
(0.63)
−2.612∗∗∗
(23.01)
−1.534∗∗∗
(94.21)
−0.230
(0.027)
3.006∗∗∗
(445.42)
840.84
41%
Equation (1)
Estimate (χ 2 )
0.870∗∗∗
(11.23)
0.105
(0.01)
−2.703∗∗∗
(27.15)
−1.299∗∗∗
(90.53)
0.253
(0.03)
3.012∗∗∗
(449.07)
834.94
41%
Sample 1 (N = 1,574)†
1.123∗∗
(5.49)
−3.335
(1.98)
−3.329∗∗∗
(8.03)
−1.081∗∗∗
(14.82)
−3.321∗∗
(6.21)
17.527
(0.01)
651.41
59%
Equation (1)
Estimate (χ 2 )
−1.445∗∗∗
(109.11)
−3.226
(1.88)
−2.980∗∗
(6.12)
−1.190∗∗∗
(14.90)
−3.417∗∗
(6.37)
17.463
(0.01)
651.36
59%
Equation (2)
Estimate (χ 2 )
Sample 2 (N = 724)‡
Equation (2) : RECLASSi,t = β0 + β1 (ROAi,t − MedianROAi,t ) + β2 (LEVi,t − MedianLEVi,t )
+β3 (CRATIOi,t − MedianCRATIOi,t ) + β4 (CFOi,t − MedianCFOi,t ) + β5 RECLASSi,t−1 + υi,t
Equation (1) : RECLASSi,t = β0 + β1 ROAi,t + β2 LEVi,t + β3 CRATIOi,t + β4 CFOi,t + β5 RECLASSi,t−1 + υi,t
Table 3
Factors Explaining the Decision to Reclassify Short-term Debt as Long-term
DEBT CLASSIFICATION AND MARKET CONSEQUENCES
1203
(Predicted sign)
88%
Equation (1)
Estimate (χ 2 )
88%
Equation (2)
Estimate (χ 2 )
95%
Equation (1)
Estimate (χ 2 )
95%
Equation (2)
Estimate (χ 2 )
Sample 2 (N = 724)‡
Notes:
The table reports parameter estimates, χ 2 statistics, and explanatory power of a logistical regression using data for each firm i and year t.
Sample 1 includes 1,574 of the 1,684 observations described in Table 2 because 110 observations did not report sufficient data to compute current ratio.
Sample 2 includes the subset of 362 Sample 1 reclassification observations that can be matched with 362 firm-years that did not indicate reclassification during the
period from 1989 to 2000.
RECLASS is a binary variable indicating ‘1’ if the firm reclassified short-term debt to long-term, and zero otherwise.
ROA is income before extraordinary items divided by end-of-year assets.
LEV is long-term liabilities measured without the effect of any debt reclassification, divided by end-of-year assets.
CRATIO is current assets divided by current liabilities measured without the effect of any debt reclassification.
CFO is cash flow from operations reported on the cash flow statement divided by end-of-year assets.
Equation (2) adjusts each of these variables except RECLASS by subtracting the firm’s industry median (see industry classifications in Table 1, Panel B).
∗∗∗ 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.
% correctly predicted
Variable
Sample 1 (N = 1,574)†
Table 3(Continued)
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1205
leverage ratio increase from the reclassification. 17 Taken together, our results suggest
factors that motivate firms to reclassify and provide a backdrop to our empirical tests
concerning the economic consequences of firms’ reclassification decisions.
It might be argued that using firms as their own control could bias the results. To
address 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 sample
period. We label this matched-pairs sample, Sample 2. Table 3 shows that results are
somewhat stronger for Sample 2. In particular, coefficients are more significant and
CFO becomes significant in the predicted direction; the negative coefficient on CFO
indicates 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 and
reclassification variables on the likelihood of a firm experiencing a debt upgrade (or
the likelihood of NOT experiencing a downgrade or no change in rating). Thus, in
Table 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), we
find that upgrades are less frequent than downgrades—the intercept for downgrades
is 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 with
prior findings (Ziebart and Reiter, 1992), although not all of the coefficients are
statistically significant. We find evidence that reclassification increases the likelihood
of a debt-rating downgrade—the coefficient on RECLAMTΔ is negative in equation
3 (p < 0.01). Distinguishing between the directions of changes in reclassification
behavior (i.e., initial reclassification, ongoing reclassification and declassification) in
equation (4), significantly improves the power of the model. Specifically, the chi-square
model likelihood increases from 82.45 (equation (3)) to 112.85 (equation (4)). The
coefficient on initial reclassified amounts (START × RECLAMT) is negative and highly
significant (β 10 = −12.098, p < 0.01), implying that credit analysts view reclassification
as a ‘red flag,’ perhaps because they have private information about debt covenant
violations or other economic factors related to the firms’ credit risk, or information
about management’s intent to manage the firms’ financial reports. Contrary to
expectations, we find a statistically insignificant coefficient on the interaction that
captures declassification (STOP × RECLAMT). Thus, firms previously punished for
reclassifying (with lowered bond ratings) do not appear to be rewarded when they
declassify.
17 We also consider lagged debt ratings in these regressions as well as changes in debt ratings including
the important drop from investment grade. These debt-rating coefficients (untabulated) were weak and
mixed. Thus we conclude that firms do not appear to consider past debt-rating levels or changes in making
reclassification 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 association
with debt-rating changes and that reclassification explains downgrades more than upgrades. We find weaker
evidence that reclassification makes a difference in explaining upgrades compared to no changes in debt
ratings. Our findings corroborate prior research that reports stronger evidence for downgrades than for
upgrades (Hand et al., 1992; and Barron et al., 1997).
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(?)
(+)
(−)
(+)
(+)
(?)
Intercept for downgrade
ROAΔ i,t
LEVΔ i,t
CRATIOΔ i,t
CFOΔ i,t
SIZEΔ i,t
(Predicted sign)
Intercept for upgrade
Variable
j =12
Equation (4)
Estimate (χ 2 )
−2.144∗∗∗
(75.72)
1.200∗∗∗
(25.04)
4.656∗∗∗
(19.77)
−6.648∗∗∗
(48.18)
0.009
(0.02)
1.083
(0.82)
−0.144
(0.18)
Equation (3)
Estimate (χ 2 )
−2.14∗∗∗
(77.78)
1.171∗∗∗
(24.71)
4.693∗∗∗
(20.30)
−6.220∗∗∗
(44.62)
−0.031
(0.31)
1.000
(0.71)
−0.207
(0.39)
Equation (4) : RATINGΔi,t0+1 = β0 + β1 + β2 ROAΔi,t + β3 LEVΔi,t + β4 CRATIOΔi,t + β5 CFOΔi,t + β6 SIZEΔi,t
+ β7 RECLAMTΔi,t + β8 STARTi,t + β9 STOPi,t + β10 (STARTi,t × RECLAMTΔi,t )
23
β j INDi + υi,t
+ β11 (STOPi,t × RECLAMTΔi,t ) +
j =8
Equation (3) : RATINGΔi,t0+1 = β0 + β1 + β2 ROAΔi,t + β3 LEVΔi,t + β4 CRATIOΔi,t + β5 CFOΔi,t + β6 SIZEΔi,t
19
+β7 RECLAMTΔi,t +
β j INDi + υi,t
Table 4
Association Between Reclassified Amounts and Debt Rating Changes
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(−)
(+)
(−)
(+)
START i,t
STOP i,t
START i,t × RECLAMTΔ i,t
STOP i,t × RECLAMTΔ i,t
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Journal compilation 84.45
7.5%
65.0.%
∗∗
−4.348
(17.23)
−12.098
(11.00)
−3.412
(2.08)
112.85
8.9%
65.5%
∗∗∗
0.762
(7.11)
−0.023
(0.89)
∗∗∗
∗∗
−4.805
(14.93)
Notes:
The table reports parameter estimates, χ 2 statistics, and explanatory power of a logistical regression using data for each firm i and year t.
The sample consists of 1,298 observations: 722 reclassifying firm-years and 576 non-reclassifying firm-years. The sample includes 177 upgrades (RATINGΔ i,t0+1 = +1),
285 downgrades (RATINGΔ i,t0+1 = −1) and 836 observations with no rating change (RATINGΔ i,t0+1 = 0).
RATINGΔ t+1,2 is +1 for a Standard & Poors senior debt rating upgrade in year t + 1 or year t + 2, −1 for a debt rating downgrade, and 0 for a debt rating that remains
unchanged.
ROAΔ t is the change from year t − 1 to year t in the ratio of earnings before extraordinary items divided by total assets.
LEVΔ t is the change from year t − 1 to year t in the ratio of long-term liabilities measured without the effect of any debt reclassification, divided by total assets.
CRATIOΔ t is the change from year t − 1 to year t in the ratio of current assets to current liabilities measured without the effect of any debt reclassification.
CFOΔ t is the change from year t − 1 to year t in the ratio of cash flow from operations reported on the cash flow statement, divided by end-of-year assets.
SIZEΔ t is the change from year t − 1 to year t in the natural log of total assets.
RECLAMTΔ is the change from year t − 1 to year t in the amount of short-term debt reclassified as long-term, scaled by total assets.
START is a binary variable indicating ‘1’ if short-term obligations are reclassified in the current year but not in 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 in the current year, and zero otherwise.
IND j is a binary variable indicating ‘1’ if the firm operates in industry j, and zero otherwise. Table 1 Panel B provides industry definitions, the wood products industry
is the omitted base group, and coefficients on the industry variables are suppressed.
∗∗∗ 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.
Model likelihood Chi-square
Psuedo-R 2
% correctly predicted
(−)
RECLAMTΔ i,t
DEBT CLASSIFICATION AND MARKET CONSEQUENCES
1207
1208
GRAMLICH, MAYEW AND McANALLY
(iv) Results of Tests for an Association Between Reclassification and Market Value of
Equity
Table 5 reports results for our ordinary least squares regression of market values of
equity on book values of assets and liabilities, and abnormal earnings (i.e., equation
(5)). Contrary to our expectation, the coefficient on RECLAMT is not statistically
significant. Thus, whether a firm reclassifies short-term obligations does not appear to
be an equity value-relevant signal.
However, we find significant results when we distinguish among reclassification
behaviors: equation (6) includes indicator variables and interaction terms to examine
the separate effects of initial reclassification and declassification. Neither of the
coefficients for the indicator variables START or STOP is significantly different from
zero. However, we find that the market value of equity decreases when the amount of
reclassified debt increases (β 6 = −1.243, p = 0.0259). On average, for every additional
dollar of debt reclassified, equity market value decreases by $1.24, controlling for the
levels of assets, liabilities and abnormal earnings.
Table 5 also shows that when firms cease reclassification, market value increases significantly in relation to the amount declassified; the coefficient on STOP × RECLAMT
is 2.659 (p < 0.01). This implies that for every dollar of short-term obligations returned
to the short-term liability section of the balance sheet, the average firm’s market value
increases 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 the
act of reclassification that impacts firm value. Investors apparently pay attention to
the magnitude of the change in the reclassified amount. We reiterate that we cannot
explicitly identify the mechanism by which reclassification and declassification affect
market value. However, by identifying reclassification and declassification as valuerelevant signals, we have identified a leading indicator that is publicly available to
investors and other market participants.
(v) Robustness Tests
We performed a number of tests to assess the robustness of our findings. None
of the tests described here changed our inferences. We omitted outliers identified
using methods advocated by Belsley, Kuh, and Welsch (1980) in all the regression
models reported in Tables 3 through 5. We winsorized observations in the lowest and
highest percentile for the continuous variables in the logistic and ordinary least-squares
regression models to remove the potentially powerful effects of extreme observations.
In our debt-rating models (Table 4), we deflated the continuous financial variables
by book value as well as by market value of firms’ equity instead of by total assets. We
considered a number of other financial variables in our debt-rating models, including
variables that measured interest expense and interest coverage, working capital levels
rather than current ratio, and indicator variables for each year in the time-series; none
of 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 not
innocuous balance sheet presentation. The reclassification decision appears to be a
deliberate financial reporting strategy with economic implications and consequences.
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Table 5
Association Between Reclassified Amounts and Market Value of Equity
Equation (5) : MVEi,t =
β0 + β1 BVAi,t + β2 BVLi,t + β3 AEi,t + β4 RECLAMTi,t + υi,t Equation (6) :
MVEi,t = β0 + β1 BVAi,t + β2 BVLi,t + β3 AEi,t + β4 STARTi,t + β5 STOPi,t
+β6 (RECLAMTi,t − RECLAMTi,t−1 )
+β7 (STARTi,t × RECLAMTi,t )
+β8 (STOPi,t × RECLAMTi,t−1 ) + υi,t
Variable
(Predicted sign)
Intercept
(?)
BVA i,t
(+)
BVL i,t
(−)
AE i,t
(+)
RECLAMT i,t
(−)
START i,t
(−)
STOP i,t
(+)
RECLAMT i,t − RECLAMT i,t−1
(−)
START i,t × RECLAMT i,t
(−)
STOP i,t × RECLAMT i,t−1
(+)
Adjusted R 2
Equation (5)
Estimate (t-statistic)
∗∗∗
911.453
(3.10)
∗∗∗
2.945
(27.45)
∗∗∗
−3.099
(−23.12)
∗∗∗
12.870
(24.05)
0.139
(0.41)
55.9%
Equation (6)
Estimate (t-statistic)
∗∗∗
1, 102.450
(3.47)
∗∗∗
2.964
(28.10)
∗∗∗
−3.132
(−23.46)
∗∗∗
13.170
(24.39)
−905.634
(−1.08)
−1, 434.492
(−1.48)
∗∗
−1.243
(−2.23)
1.052
(1.00)
∗∗∗
2.659
(2.18)
56.3%
Notes:
The table reports parameter estimates and t-statistics for an OLS regression using data for each firm i and
year 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 net
book value (i.e., BVA minus BVL).
RECLAMT t 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 not
in 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 in
the 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 industry
benchmark) and when they can afford to (i.e., when overall leverage is lower than
in the previous year or lower than the industry benchmark).
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GRAMLICH, MAYEW AND McANALLY
We find that firms’ debt ratings are negatively affected by reclassification. In
particular, initial reclassification increases the likelihood of a rating downgrade. This
evidence implies that although managers may strategically classify short-term debt
as 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’ equity
is associated with the decision to declassify short-term obligations, but not with the
decision 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 cost
saving cannot explain the magnitude of the price changes we document.
Firm reclassification decisions are not likely to have caused changes in debt ratings
or market values. Rather, the economic consequences that we document are likely
caused by other factors that are correlated with firms’ reclassification decisions. So
while our models may suffer from correlated omitted variables problems, we contend
that the publicly available reclassification and declassification actions proxy for these
unspecified unobservable variables.
Overall, our results imply that debt reclassification signals bad news—it is a red flag to
capital market participants. Conversely, declassification signals good news. As such, our
findings are important to creditors, investors, and other market participants who seek
information concerning debt and equity prices. Moreover, this study contributes to the
general understanding of the determinants and consequences of accounting choice
as it pertains to the balance sheet. Much of the extant research on accounting choice
focuses on earnings management (see Holthausen and Leftwich, 1983; and Fields et al.,
2001), while comparably little has been written about balance sheet management. This
perhaps is because managerial motivation is less obvious for balance sheet accounts
than for earnings (although see Imhoff and Thomas, 1988; Mohr, 1988; and Hopkins,
1996).
Our findings suggest several avenues for further research. For example, one
might explore whether firms that engage in balance sheet management via debt
reclassification also engage in income statement management. In particular, future
research could explore whether the quality of firms’ earnings, perhaps measured by
the persistence of earnings, cash flows and/or accruals, is related to decisions to initiate
or cease reclassification. Alternately, one might assess whether firms that reclassify do
so in conjunction with deliberate economic choices that impact earnings. These sorts
of studies would provide evidence on the simultaneous management of the balance
sheet and income statement and would speak to management’s broader reporting
philosophy.
Future research might profitably extend our study by using international data
particularly when firms adopt IAS standards in 2005. IAS 1, which addresses the
phenomenon we document in this paper, is more stringent in some respects than
SFAS 6. 19 Consequently, the market is likely to learn of potential liquidity problems
sooner. In the extreme, this IFRS mandate for classifying short term may be too
conservative, causing covenant breaches and potentially causing firms that have no
liquidity 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 covenant
breaches 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 after
the balance sheet date but before the release date of the financial statements.
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1211
equity and debt markets will view classification toward short term debt under IFRS
as a ‘problem’ or not is an empirical question that future research could investigate.
Conversely, compared with many domestic accounting standards, IAS standards provide
more leeway in measuring and classifying balance sheet items that affect debt covenants.
Specifically, the increased discretion provided by IAS standards regarding accruals and
the definition of current versus non-current assets and liabilities, may enable firms
to avoid debt covenant violations (Ormrod and Taylor, 2004). Thus, future research
could investigate the extent to which firms exercise discretion over the measurement
and classification of current or non-current items in the new IAS regime, and the debt
and equity market consequences of such decisions.
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