Debt reclassification, accrual and cash flow persistence,

and capital market consequences

Jeffrey D. GramlichUniversity of Southern Maine

William MayewUniversity of Texas at Austin

Mary Lea McAnally*Texas A&M University

August 2003

* Corresponding author:Accounting DepartmentWehner 401QMays Business SchoolTexas A&M UniversityCollege Station, TX, 77843Phone: (979) 845-5017Fax: (979) 845-0028Email: MMcAnally@cgsb.tamu.edu

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.

Debt reclassification, accrual and cash flow persistence,

and capital market consequences

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

Standard No. 6. We examine a sample of 1,684 firm-year observations to determine whether debt

classification decisions can be used to predict differences in the persistence of earnings, cash

flow and accruals, as well as differences in the cost of capital, as measured by bond ratings and

equity market values. We document that earnings become less persistent following the initial

balance sheet reclassification of debt from short-term to long-term, and that this reduced

persistence is the result of less persistent cash flows and accruals after debt reclassification. We

also find that initial reclassification increases the likelihood of a subsequent debt-rating

downgrade. Lastly, we find that the market value of equity 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 earnings

persistence, to debt-rating changes, and to subsequent stock values. Taken together, our results

show that debt classification is an important publicly-available indicator that may be useful to

capital market participants.

KEYWORDS: Debt classification, earnings persistence, economic consequences, debt ratings

Debt reclassification, accrual and cash flow persistence,

and capital market consequences

1. Introduction

We provide 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 (GMT) document that firms

use 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 firms move such debt back to the

current category (i.e., “declassify”). GMT show that these classification changes reduce the

variability of firms’ current and long-term debt ratios across time, and they mitigate the deviation

of firms’ 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 opportunistically reclassify debt. Further, we

argue that firms that reclassify debt also engage in other strategic financial reporting choices. We

document the economic consequences of debt reclassification by providing empirical evidence

that economically links firms’ debt classification decisions to firm stakeholders.

SFAS 6 permits a firm to reclassify short-term debt as long-term if it obtains a loan

commitment that extends 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 may

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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. 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, for

some firms, debt reclassification and declassification reflect management’s strategic use of

financial reporting and that this strategy has wealth implications for firm shareholders.

We develop and estimate a model that explains firms’ decisions to reclassify debt to

substantiate our claim that reclassification is not an innocuous reporting choice. The evidence

indicates that firms with lower leverage, current ratio, and operating cash flows are more likely

to 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 most likely timing of debt

repayments.

We explore whether firms that engage in balance sheet management via debt

reclassification also engage in income statement management, to substantiate our claim that

reclassification reflects managements’ view of the role of financial reporting. We use persistence

of reported accounting numbers as a broad test of income statement management. If some firms

initiate reclassification or declassification because of a shift in management’s reporting

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 short-term 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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philosophy, we should see subsequent decreases (increases) in the persistence of earnings, cash

flows and accruals. Our research approach neither assumes a particular managerial motivation

for reclassification nor isolates a particular method of income statement management (Fields,

Lys and Vincent 2001). Rather, we explore the association between reclassification and the

persistence of performance measures. We do not argue that the relation between reclassification

and persistence is causal but that the two are jointly determined by a common set of managerial

motives either to deliberately intervene into the reporting process or to back away from doing so.

The evidence shows that earnings become less persistent following the initial balance sheet

reclassification of debt from short-term to long-term. Moreover, we attribute this decrease in

persistence to both cash flows and accruals that become less persistent the year after a debt

reclassification.

To evaluate the economic consequences of debt classification, we assess whether changes

in classification systematically predict differences in cost of debt and equity capital. Although

other research ties earnings persistence to shareholder wealth (e.g., Kormendi and Lipe 1987;

Ohlson 1995; Sloan 1996; Barth, Beaver, Hand, and Landsman 1999), we directly examine

whether reclassification is associated with subsequent changes in debt ratings and stock prices.

We report that, after controlling for demographic and financial variables known to influence debt

ratings, firms that reclassify are more likely to experience a debt-rating downgrade relative to

firms that do not reclassify. However, we find no evidence that declassifying firms are more

likely to experience a debt-rating upgrade. But, consistent with our finding that performance

measures are more persistent after firms return short-term obligations to the current liability

section of the balance sheet, we find that the market value of equity increases when firms

declassify.

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Taken together, our findings suggest that capital-market participants ought to view

reclassification as a ‘red flag’ indicative of management’s intervention into the reporting process.

Our earnings persistence findings are of potential interest to analysts who must determine

whether and how to incorporate reclassified debt into their forecasts and recommendations. 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 in computing covenant levels or ratios

(Beatty, Ramesh, and Weber 2002).

The paper proceeds as follows. In section 2 we develop four hypotheses. In section 3 we

describe our data sample and our models. We discuss our results in section 4 and conclude in

section 5.

2. Hypotheses

Factors associated with debt reclassification

Our first model explains debt reclassification. While reclassification does not affect a

firm’s total liabilities, it simultaneously magnifies both the current ratio and the long-term 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

4

Thus, to build our model, we consider than credit-scoring models and debt covenants typically

specify minimum levels for current ratio or working capital measures (Altman 2000, 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 current

ratios, it worsens long-term debt ratios. Consequently, we expect only firms with long-term debt

slack will reclassify short-term debt to long-term. That is, reclassification is a viable alternative

for firms to meet a liquidity target but only if doing so does not impact a leverage constraint. If

firms reclassify to disguise worsening financial condition, then measures of performance such as

operating cash flow and profitability would be negatively related to the reclassification decision.

Thus, we predict:

HYPOTHESIS 1: 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.

Reclassification as an indicator of other strategic accounting choices

Fields, Lys and Vincent (2001) conclude that insight into managerial accounting choice is

hindered by two research design considerations. First, researchers typically assume a specific

managerial motivation or objective and explore whether accounting choices are consistent with

that assumed motivation. Second, researchers typically focus on a unique accounting choice and

study it in isolation. As Fields et al. (2001) argue, it is more likely that firms use a variety of

techniques to manage a number of financial reporting targets for diverse reasons. Consistent with

this view, we explore whether managers who engage in balance sheet management via debt

reclassification also engage in income statement management. We neither assume a particular

managerial motivation for reclassification nor isolate a particular method of income statement

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management. Instead, we test whether the decrease in balance-sheet quality arising from

opportunistic debt reclassification is associated with a decrease in income-statement quality (i.e.

earnings quality).

We use earnings persistence as a broad test of earnings quality. While only one measure

of earnings quality, persistence suits our research purposes for several reasons. First, the quality

of accounting information has long been measured by its predictive value. For instance, FASB’s

Conceptual Framework prescribes that a characteristic of accounting relevance is its ability to

predict future earnings (FASB 1980). Second, theoretic valuation models links earnings

persistence to abnormal earnings (e.g. Ohlson 1995). Third, significant empirical evidence

confirms the value-relevance of persistent earnings (Barth et al. 1999, Barth and Hutton 2003).

In developing a framework of earnings quality, Jonas and Blanchet’s (2000) first criterion is the

combination of predictive value and earnings persistence. In evaluating earnings quality, Jonas

and Blanchet suggest that “A shorthand way of thinking about earnings persistence is to ask

whether the information is useful in assessing the likely levels of recurring earnings, i.e., the

company’s sustainable earnings potential” (see, for examples, Barth et al. 1999, Barth and

Hutton 2003, Hanlon 2003). For these reasons, we use earnings persistence as a proxy for

earnings quality.

We do not argue that the relation between reclassification and persistence is causal but

that the two are jointly determined by a common set of managerial motives either to deliberately

intervene into the reporting process or to back away from doing so. Thus, we hypothesize:

HYPOTHESIS 2A: Persistence of earnings is negatively related to the reclassification of short-term obligations as long-term debt.

Schipper and Vincent (2002) argue that earnings persistence is not synonymous with

earnings quality because persistent earnings are not always representationally faithful. To

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address this concern, we disaggregate earnings into operating cash flows and accruals. Because

operating cash flows are less readily managed than accruals, cash flows are potentially more

representationally faithful of economic performance than earnings. If reclassification conveys

management’s foreknowledge of economic problems for their firm, we expect a decline in the

persistence of cash flows from operations after reclassification.

HYPOTHESIS 2B: Persistence of operating cash flow is negatively related to the reclassification of short-term obligations as long-term debt.

Dechow (1994), Sloan (1996), Dechow, Kothari, and Watts (1998), and Barth et al.

(1999) among others, document that accruals are less persistent than operating cash flows. The

explanations for this are that the accrual process requires numerous estimates and that many

accruals reverse quickly. Xie (2001) reports that discretionary accruals are less persistent than

non-discretionary accruals. By definition, firms manage earnings via discretionary accruals.

Thus, earnings management is associated with a decrease in the persistence of total accruals (i.e.,

discretionary plus non-discretionary accruals). We hypothesize:

HYPOTHESIS 2C: Accrual persistence is negatively related to the reclassification of short-term obligations as long-term debt.

Economic consequences of reclassification

If the classification of short-term debt were innocuous, one would expect no capital

market consequences associated with firms’ choices to reclassify or declassify. On the other

hand, debt classification may impart information to debt and equity markets that is useful in

forecasting the future cash flows to these stakeholders. By testing for capital market responses to

debt classification, we can ascertain whether classification systematically predicts real economic

effects on stakeholders. Specifically, we examine the impact of reclassification and

declassification on debt ratings and on the market value of equity.

Reclassification and debt ratings

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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). In this section, we argue that debt-rating agencies may privately learn

firms’ rationale for reclassifying and factor that information into 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 its short-term debt to avoid violating a debt covenant. This

knowledge could directly affect a firm’s debt rating. 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 (see Exhibit 1). Dealscan

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).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, we could not use the Dealscan data to test potential debt-covenant violation hypotheses.

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reports that a 1998 debt covenant required that Pacificorp Inc. maintain a current ratio of at least

1.1. This covenant was not explicitly reported in the company’s financial statements that year,

although presumably debt raters have access to 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. Our conversations with several CPA-firm

partners confirmed that they would recommend reclassification to clients facing debt-covenant

violations. The propensity to heed such advice may signal management’s broader predilection to

intervene in the reporting process.

Debt-raters 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 the depth, expertise, and historical track

record of the firm’s management as well as to management’s overall philosophy. To the extent

that reclassification is associated with credit analysts’ private information, debt ratings will be

associated with reclassification. Although reclassification is not posited to be 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. Thus, our third hypothesis is:

HYPOTHESIS 3: Debt ratings are negatively (positively) related to the reclassification (declassification) of short-term obligations as long-term debt.

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 material impact on firm value. Apart from the loan-

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commitment fee, reclassification does not directly affect earnings nor does it appear to impact

firm value. Nonetheless, we maintain that reclassification is value-relevant as a 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 et al. 2001). Instead, we

posit that a confluence of factors impact stock price. These factors may include earnings

persistence, which is a value-relevant attribute of earnings that market participants discern

(Kormendi and Lipe 1987; Dechow 1994; Ohlson 1995; Dechow et al. 1998; Barth et al. 2001).6

Consequently, if firms’ reclassification (declassification) decisions are associated with the

persistence of cash flows and accruals as in HYPOTHESIS 2, it follows that market values will fall

(rise). Other value-relevant factors may include changes in debt-ratings (Kliger and Sarig 2000;

Dichev and Piotroski 2001). Thus:

HYPOTHESIS 4: 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

Sample selection and data

Using Lexis/Nexus, we searched for firms that reclassified short-term debt during the

period 1989 to 2000.7 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 2000.8

This approach identified a total of 3,080 firm-years. We gathered additional financial variables,

debt ratings, and equity values from the Compustat and 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.

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We read and coded debt footnotes to obtain reclassification information, including

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

for disclosures regarding debt covenants and any violations thereof.9 We coded a firm-year as a

reclassification if commercial paper, notes, or other items of debt maturing within the following

year were classified as long-term pursuant to the “intent and ability” paragraph of SFAS 6.

Models

We first estimate a model that explains firms’ decisions to reclassify and then test for an

association between reclassification and earnings persistence, debt ratings, and market value of

equity. Each model includes all the available observations for each firm in the sample. That is,

we estimate pooled, cross-sectional time-series models using non-reclassification 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 we report.

Factors that explain the decision to reclassify

We employ the following logistical regression model to evaluate 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+i,t (1)

where RECLASSi,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 long-term debt to assets; CRATIO is 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 removing the effects of reclassification.

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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.10 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 estimate a model that controls 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 ) + β5RECLASSi,t-1 + i,t

(2)

We compute industry medians using data from the entire Compustat database for each year.

Other variables are as previously defined.

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The association between reclassification and persistence of earnings, cash flows and accruals

The following model applies an approach developed by Hanlon (2002) to test for

association between reclassification and earnings persistence (i.e., HYPOTHESIS 2A):

ROAi,t = O + 1 RECLASSi,t-1+ 2 ROAi,t-1 + 3 (ROAi,t-1 RECLASSi,t-1) + i,t (3)

where the variables are as defined for equation (1). The coefficient on ROAt-1 measures the

persistence of earnings. To test our argument that reclassification is a leading indicator of

reduced earnings persistence, we examine the interaction term of prior-year variables, ROA

RECLASS, to determine whether earnings persistence is lower in the year following

reclassification. We expect a negative coefficient for this interaction term.

We disaggregate earnings into its component parts, cash flows and accruals, to test

HYPOTHESES 2B and 2C. This permits us to determine whether changes in earnings persistence

are attributable to changes in cash flow persistence or changes in accrual persistence, or

both.

ROAi,t = 0 + 1 RECLASSi,t-1 + 4 CFOi,t-1+ 5 ACCi,t-1+ 6 (CFOi,t-1 RECLASSi,t-1) + 7 (ACCi,t-1 RECLASSi,t-1) +i,t

(4)

where CFO is cash flows from operations as reported on the cash flow statement scaled by total

assets, and ACC is ROA minus CFO. A negative coefficient for the CFO RECLASS interaction

term would suggest that reclassification is a leading indicator of falling operating cash

performance. A negative coefficient on the ACC RECLASS interaction term would indicate

management interference in the reporting process.

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.11 Our model includes demographic and

financial variables suggested by prior research (Reiter and Ziebart 1992). We read Standard and

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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 3.

RATINGΔi,t0+1 = β 0 + β1 + β2 ROAΔi,t + β3 LEVΔi,t + β4 CRATIOΔi,t + β5 CFOΔi,t

+ β6 SIZEΔi,t + β7 RECLAMTΔi,t + + υi,t

(5)

where RATING is the S&P discrete numeric senior-debt rating that ranges from 2 (which

corresponds to a ‘AAA’ rating) to 27 (which corresponds to a ‘D’ rating).12 We define RATING

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, RATINGi,t0+1, captures the

cumulative directional change in RATINGi,t during 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 and Ziebart 1992; Standard and Poor’s 2001).13 For example, firms are

often placed on CreditWatch before the rating is formally changed.

The independent variables in equation 5 measure changes during year t in previously-

defined variables. thus our dependent and independent variables are congruent. We include

SIZE, the change in the natural log of total assets, and RECLAMT , the change in the dollar

amount of reclassified short-term obligations scaled by total assets. Consistent with S&P’s

Corporate Rating Criteria, we also include a set of 12 indicator variables, IND, with subscript j to

capture the firm’s industry. Industry definitions are provided in Table 1, Panel B.

To further test HYPOTHESIS 3, equation 6 includes terms that measure changes in a

firm’s reclassification activity:

12 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).13 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.

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RATINGΔi,t0+1 = β 0 + 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) + + υi,t

(6)

In this model we code STARTt-1 as ‘1’ if the firm began reclassifying short-term debt during year

t-1, and STOPt-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 ROA, CRATIO and CFO

should have positive coefficients. In contrast, firms with greater leverage are less (more) likely to

experience positive (negative) debt rating changes; thus, we expect a negative coefficient for

LEV. We offer no prediction for SIZE, 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.14 We expect a firm to be 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 coefficients on both

RECLAMTΔ and on the interaction term START RECLAMT (i.e., β7 < 0 and β10 < 0). We also

expect that debt-rating upgrades are more likely when firms declassify and therefore predict a

positive coefficient on STOP RECLAMT (i.e. β11 > 0).

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 (HYPOTHESIS 4):

MVEi,t = 0 + 1 BVAi,t + 2 BVLi,t + 3 AEi,t + 4 RECLAMTi,t + i,t (7)

15

We measure the market value of the firm’s common equity (MVEt) 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.15 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 [BVAt-1 – BVLt-1]).16 AE is the

difference between reported earnings and the calculated expected return. Because reclassification

does not affect the book value of assets or of liabilities, we do not modify our model or our

calculation of abnormal earnings to include any unrecorded assets or liabilities (Hand and

Landsman 2000). RECLAMTt is the dollar amount of short-term obligations reclassified as long

term during year t.

Consistent with prior research, we expect that BVA and BVL will have positive and

negative coefficients respectively. If reclassification is a signal of management’s intervention in

the financial reporting process, the existence of reclassified short-term obligations will decrease

firm value (i.e. the coefficient on RECLAMT will be negative).

Alternatively, to test whether changes in reclassification has an even greater impact on

market values than the level of reclassified short-term obligations, we refine equation 7 and

estimate a model that allows separate coefficients for amounts classified for the first time, and

declassified amounts.17 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 = 0 + 1 BVAi,t + 2 BVLi,t + 3AEi,t + 4 STARTi,t + 5 STOPi,t + (RECLAMTi,t - RECLAMTi,t-1 )+ 7 (STARTi,t RECLAMTi,t)

(8)

16

+ 8 (STOPi,t RECLAMTi,t-1) + i,t

We predict that initial reclassification will decrease equity values (i.e. 4 < 0 and 7 < 0). We also

predict that declassification provides a signal that economic conditions will improve and

therefore declassification will be associated with increased equity values (i.e.5 > 0 and 8 > 0).

4. Results

Sample and descriptive statistics

Table 1 presents descriptive statistics for our sample. Panel A explains the sample-

selection process and Panel B shows that 752 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 long-term 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 eliminates the difference: reclassifiers’ mean and median long-term debt

ratios are statistically smaller than those of non-reclassifiers (p<0.01). More dramatic, however,

17

is the comparison of the current ratio before and after the effects of reclassification. Mean and

median current ratios as reported on the balance sheet are greater in non-reclassifying firm years

than in reclassifying firm years (p<0.01), but without reclassification the differences .

18

Findings regarding firms’ reclassification decisions

Table 3 reports results for our logistic regressions (equations 1 and 2) that seek to 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, that is, when they

have lower leverage and can afford to have the leverage ratio increase from the reclassification.18

Taken together, our results suggest some factors that motivate firms to reclassify and provide a

backdrop to our empirical tests concerning earnings persistence and 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’ 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 than with Sample 1 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 (p<0.01). Thus, our results are robust to estimation on a

wider sample of firms.

Results of tests for an association between reclassification and earnings persistence

Table 4 reports the results of our persistence tests. Consistent with prior research, we find

that earnings are mean-reverting—the estimated coefficient on ROAt-1 is positive, but less than

1.0 (Dechow 1994, Sloan 1996, and Hanlon 2002). The interaction term (ROA RECLASS)

19

measures the incremental earnings persistence for firms that reclassify, and it has a negative

estimated coefficient (3 = -0.163, p < 0.01), consistent with our hypothesis that reclassification

is a leading indicator of reduced earnings persistence.

Equation 4 tests HYPOTHESES 2B and 2C. To ascertain whether the subsequent decrease

in earnings persistence is due to a decrease in the persistence of cash flows, accruals, or both, we

examine two interaction terms: CFOt-1 RECLASSt-1 and ACCt-1 RECLASSt-1. The negative

estimated coefficient on CFO RECLASS (p<0.01) is consistent with the notion that

reclassification predicts a general decline in the firm’s economic condition. The negative

coefficient on ACC RECLASS (p<0.10) indicates a forthcoming change in the levels accruals

(i.e., either an increase or a decrease). This latter result suggests that some firms jointly manage

their balance sheets using debt reclassification and their income statements using accruals.

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 5, positive

(negative) coefficients imply that an upgrade is more (less) likely.19 Consistent with prior studies

(Reiter and Ziebart 1992; 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 5 and 6. 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 RECLAMT is negative in equation 5 (p<0.01). Distinguishing between the

20

directions of changes in reclassification behavior (i.e., initial reclassification, ongoing

reclassification and declassification) in equation 6, significantly improves the power of the

model. Specifically, the chi-square model likelihood increases from 82.45 for equation 5 to

112.85 for equation 6. The coefficient on initial reclassified amounts (START RECLAMT) is

negative and highly significant (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. 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.

Results of Tests for an Association between Reclassification and Market Value of Equity

Table 6 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 7). 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 a value-relevant signal. However, we

find significant results when we distinguish among reclassification behaviors: equation 8

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 (6 = -1.243, p = 0.0259). Thus, on

average, for every additional dollar of debt reclassified during the year, equity market value

decreases by $1.24.

21

Table 6 also shows that initial debt reclassification is not statistically associated with a

change in the market value of equity (i.e., the coefficient on START is not significant). Moreover,

the magnitude of the initial reclassification does not impact equity value (i.e., the coefficient on

START RECLAMT is not significant). However, 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, consistent with

our finding that earnings are more persistent in the year after declassification than during years

when firms reclassify.

Comparing the results of equations 7 and 8, 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 value-relevant signals, we have identified a

leading indicator that is publicly available to investors and other market participants.

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 6. We trimmed (i.e., winsorized) observations in the lowest and highest percentile for the

continuous variables included in the logistic and ordinary least-squares regression models to

remove the potentially powerful effects of extreme observations. In our debt-rating models

22

reported in Tables 5 and 6, we deflated the continuous financial variables by the book value as

well as by the 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.

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 the result of a

comprehensive financial reporting strategy with economic implications and consequences. We

present a series of tests that, when taken together, amount to an indictment of reclassification

behavior. In particular, 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).

We find that reclassification precedes deteriorating persistence of earnings, cash flows

and accruals. Thus, we identify a leading indicator of managerial intervention into the financial

reporting process. While other studies document instances of such intervention, our study is the

first to our knowledge that finds a publicly-available signal of future intervention in the income

statement. This supports the argument in Fields et al. (2001) that firms likely use a variety of

techniques to reach a number of financial reporting targets.

We also find that firms’ debt ratings are negatively affected by reclassification. In

particular, initial reclassification increases the likelihood of a rating downgrade. This evidence

23

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.

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).

Lastly, we 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 (the elimination of long-term loan commitment fees), the

magnitude of this cost savings cannot explain the magnitude of the price changes we document.

We fully acknowledge that firms’ reclassification decisions likely do not cause changes

in debt ratings and/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

it can be argued that our models suffer from correlated omitted variables problems, we contend

that reclassification and declassification proxy for these unspecified omitted 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 about the

persistence of future earnings and about debt and equity prices. Moreover, this study contributes

to the general understanding of the determinants and consequences of accounting choice. Much of

the extant research on accounting choice focuses on earnings management (see Holthausen and

Leftwich 1983; and Fields et al. 2001). Comparably little has been written about balance sheet

management, perhaps because managerial motivation is less obvious for balance sheet accounts

than for earnings (although see Imhoff and Thomas 1988; Mohr 1988; and Hopkins 1996). Thus,

we provide initial evidence of simultaneous balance sheet and income statement management.

24

Endnotes

25

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28

Exhibit 1Excerpt 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 bonds 5.6%-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 2025 through 2035 175.8 175.8Commercial paper (b) (d) 116.8 120.6Other 21.9 25.1

Total 3,783.1 3,583.1Less 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 1,195.3Less 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.

29

Table 1Sample 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 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 criteria

Industry group† 2-digit SICcategories

Reclassifying firm-years

Non-reclassifying

firm years

Total sample

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

Totals 752 932 1,684

Panel C: Sample distribution across years

Year Reclassifying firm-years

Non-reclassifying firm-years

Total 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 752 932 1,684

30

(This table is continued on the next page.)

31

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).

32

Table 2Descriptive statistics for all firm-years between 1989 and 2000

Panel A: Comparison of firm-years with and without reclassification

Firm-years with reclassification(N=932)

Firm-years without reclassification(N=752)

Attributes Mean Median75th

Percentile25th

Percentile Mean Median75th

Percentile25th

Percentile

Assets, $ millions 9,332** 5,022** 11,466 2,158 6,179 3,449 6,495 1,607

Market value of equity, $ millions 8,217* 3,761** 9,357 1,837 6,489 2,297 5,302 1,087

Return on assets 4.4% 4.2% 7.2% 2.0% 4.1% 4.1% 7.6% 1.9%

Cash from operations to assets 10.1% 9.8% 13.5% 6.5% 9.3% 9.4% 13.3% 5.5%

Asset growth 21.9%** 5.6%** 15.1% -1.2% 8.6% 4.1% 12.0% -2.8%

Reclassified amount, $ millions 564** 252** 590 100 0 0 0 0

Long-term debt to assets, as reported 0.275** 0.268** 0.344 0.184 0.234 0.221 0.311 0.135

Long-term debt to assets, without reclassification 0.204 0.195 0.269 0.111 0.234** 0.221** 0.311 0.135

Current ratio, as reported† 1.330 1.202 1.546 1.018 1.480** 1.266* 1.744 0.963

Current ratio, without reclassification† 1.012 0.950 1.190 0.740 1.480** 1.266** 1.744 0.963

Number (percent) of debt-rating downgrades

177(19.0%)

128(17.0%)

Number (percent) of debt-rating upgrades

99(10.6%)

90(12.0%)

Number (percent) of debt-rating downgrades

78(8.4%)**

38(5.1%)

(This table is continued on the next page.)

33

Table 2 (Continued)

Panel B: Before and after comparisons for a sample of firms that start and stop reclassification

Attributes

Firm-years where reclassification starts (n =172)

Firm-years where reclassification stops (n = 123)

Prior yearMean (Median)

First year with reclass

Mean (Median)Prior year

Mean (Median)

First year without reclassMean (Median)

Assets, $ millions 5,204(6,799)

6,334**

(8,539)**6,266

(3,633)6,486

(3,875)**

Market value of equity, $ millions

4,458(2,866)

4,984**

(3,148)**4,698

(2,403)4,738

(2,549)**

Return on assets 7.1%**

(6.9%)**5.8%

(5.9%)5.1%

(5.7%)5.5%

(5.7%)Cash from operations to assets

10.7%*

(10.8%)**9.9%

(10.2%)9.0%

(7.7%)9.6%

(9.6%)Current ratio, as reported‡ 1.443

(1.250)1.416

(1.277)1.434

(1.300)1.457

(1.254)Current ratio, without reclassification†

1.443**

(1.250)**1.120

(1.023)1.187

(1.085)1.457**

(1.254)**

Notes:* (**) indicates that either the ‘reclassification firm-years’ or the ‘non-reclassification firm-years’ 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.† 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. ‡ Compustat does not report values of both current assets and current liabilities for all firms in the sample. Of 172 (123) firm-years that begin (end) reclassification, 163 (118) report data sufficient to compute the current ratio.

34

Table 3Factors explaining the decision to reclassify short-term debt as long-term

Equation 1: RECLASSi,t = β0 + β1 ROAi,t + β2 LEVi,t + β3 CRATIOi,t + β4 CFOi,t + β5 RECLASSi,t-1 + i,t

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 )+ β5RECLASSi,t-1 + i,t

Variable (predicted sign)

Sample 1 (n =1,574) † Sample 2 (n =724) ‡

Equation 1 Estimate (2)

Equation 2Estimate (2)

Equation 1 Estimate (2)

Equation 2Estimate (2)

Intercept (?) 0.870***

(11.23)-1.044***

(125.26)1.123**

(5.49)-1.445***

(109.11)ROAi,t () 0.105

(0.01)0.986

(0.63)-3.335(1.98)

-3.226(1.88)

LEVi,t () -2.703***

(27.15)-2.612***

(23.01)-3.329***

(8.03)-2.980**

(6.12)CRATIOi,t () -1.299***

(90.53)-1.534***

(94.21)-1.081***

(14.82)-1.190***

(14.90)CFOi,t () 0.253

(0.03)-0.230(0.027)

-3.321**

(6.21)-3.417**

(6.37)RECLASSi,t-1 (+) 3.012***

(449.07)3.006***

(445.42)17.527(0.01)

17.463(0.01)

Model likelihoodChi-square

834.94 840.84 651.41 651.36

Psuedo-R2 41% 41% 59% 59%% correctly predicted 88% 88% 95% 95%

Notes:The table reports parameter estimates, 2 statistics, and explanatory power of a logistical regression using data for each firm i and year t.

RECLASS is one if the firm reclassified short-term debt to long-term, and zero otherwise.ROA is earnings before extraordinary items divided by total assets.LEV is long-term liabilities measured without the effect of any debt reclassification, scaled by total 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 scaled by total 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.** Significant at the 5% level.* Significant at the 10% level.† 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 do not indicate reclassification during the period from 1989 to 2000.

35

Table 4Association between reclassified amounts and the persistence of earnings, cash flows, and accruals

Equation 3: ROAi,t = β0 + β1 RECLASSi,t-1 + β2 ROAi,t-1+ β3 (ROAi,t-1 RECLASSi,t-1) + υi,t

Equation 4: ROAi,t = β0 + β4 CFOi,t-1+ β5 ACCi,t-1 + β6 RECLASSi,t-1+ β7 (CFOi,t-1 RECLASSi,t-1) + β8 (ACCi,t-1 RECLASSi,t-1) + υi,t

Equation 3 Equation 4Variable (predicted sign) Parameter

estimatet-statistic Parameter

estimatet-statistic

Intercept 0.017 8.21*** -0.004 -1.31RECLASSi,t-1 (?) 0.008 2.53*** 0.021 4.42***

ROA i,t-1 (+) 0.586 23.48***

ROA i,t-1 RECLASSi,t-1 () -0.163 -3.67***

CFO i,t-1 (+) 0.730 24.17***

ACC i,t-1 (+) 0.433 14.02***

CFO i,t-1 RECLASSi,t-1 () -0.255 -5.19***

ACC i,t-1 RECLASSi,t-1 () -0.072 -1.43*

Adjusted R2 28.8% 31.9%

Notes:The table reports parameter estimates, t-statistics, and explanatory power for an OLS regression using data for each firm i and year t.

The sample includes 1,684 observations: 932 reclassifying firm years and 752 non-reclassifying firm years.

ROA is earnings before extraordinary items scaled by total assets. CFO is cash flows from operations scaled by total assets. ACC is ROA minus CFO.RECLASS is one if the firm reclassified short-term debt to long-term, and zero otherwise.

*** Significant at the 1% level.** Significant at the 5% level.* Significant at the 10% level.

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Table 5Association between reclassified amounts and debt rating changes

Equation 5: RATINGΔi,t0+1 = β 0 + β1 + β2 ROAΔi,t + β3 LEVΔi,t + β4 CRATIOΔi,t + β5 CFOΔi,t + β6 SIZEΔi,t

+ β7 RECLAMTΔi,t + + υi,t

Equation 6: RATINGΔi,t0+1 = β 0 + 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)

+ β11 (STOPi,t RECLAMTΔi,t) + + υi,t

Variable (predicted sign) Equation 5Estimate (2)

Equation 6 Estimate (2)

Intercept for upgrade (?) -2.14***

(77.78)-2.144***

(75.72)Intercept for downgrade (?) 1.171***

(24.71)1.200***

(25.04)ROAΔi,t (+) 4.693***

(20.30)4.656***

(19.77)LEVΔi,t () -6.220***

(44.62)-6.648***

(48.18)CRATIOΔi,t (+) -0.031

(0.31)0.009

(0.02)CFOΔi,t (+) 1.000

(0.71)1.083

(0.82)SIZEΔi,t (?) -0.207

(0.39)-0.144(0.18)

RECLAMTΔi,t () -4.348**

(17.23)-4.805**

(14.93)STARTi,t () 0.762***

(7.11)STOPi,t (+) -0.023

(0.89)STARTi,t RECLAMTΔi,t () -12.098***

(11.00)STOPi,t RECLAMTΔi,t (+) -3.412

(2.08)Model likelihoodChi-square

100.76 121.21

Psuedo-R2 7.5% 8.9%% correctly predicted 65.0.% 65.5%

(This table is continued on the next page.)

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Table 5 (Continued)

Notes:The table reports parameter estimates, 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 (RATINGΔi,t0+1 = +1), 285 downgrades (RATINGΔi,t0+1 = -1) and 836 observations with no rating change (RATINGΔi,t0+1 = 0)

RATING∆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 constant. ROA∆t is the change from year t-1 to year t in the ratio of earnings before extraordinary items scaled by total assets.LEV∆ 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, scaled by total assets.CRATIO∆ 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.CFO∆ 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, scaled by total assets.SIZE∆ t is the change from year t-1 to year t in the natural log of total assets.START is 1 if short-term obligations are reclassified in the current year but not in the prior year, and zero otherwise. STOP is 1 if short-term obligations are reclassified in the prior year but not in the current year, and zero otherwise. RECLAMT∆ 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. INDj 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.

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Table 6Association between reclassified amounts and market value of equity

Equation 7: MVEi,t = 0 + 1 BVAi,t + 2 BVLi,t + 3 AEi,t + 4 RECLAMTi,t + i,t

Equation 8: MVEi,t = 0 + 1 BVAi,t + 2 BVLi,t + 3 AEi,t + 4 STARTi,t + 5 STOPi,t + (RECLAMTi,t – RECLAMTi,t-1)+ 7 (STARTi,t RECLAMTi,t) + 8 (STOPi,t RECLAMTi,t-1) + i,t

Variable (predicted sign) Equation 7 Estimate (t-statistic)

Equation 8 Estimate (t-statistic)

Intercept (?) 911.453***

(3.10)1,102.450***

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

(27.45)2.964***

(28.10)BVLi,t () -3.099***

(-23.12)-3.132***

(-23.46)AEi,t (+) 12.870***

(24.05)13.170***

(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%

(This table is continued on the next page.)

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Table 6 (Continued)

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 total assets in $ millions; BVL is 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);

6 For example, Barth and Hutton (2001) report that equity analysts’ earnings forecast revisions are consistent with analysts detecting changes in earnings persistence.7 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. 8 Because certain variables require lagged data, we also gathered data for 1988.9 We also 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.10 Lev does not attempt to 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.11 See Ederington 1985, for a review of this empirical approach.14 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).15 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.16 Abarbanell and Bernard (2000) report consistent results for abnormal earnings calculated with discount rates ranging from nine 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.17 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.18 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.19 We also estimate the models in Table 5 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, Holthausen, and Leftwich 1992; Barron, Clare, and Thomas 1997).

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ROA is earnings before extraordinary items scaled by total assets; RECLAMT∆ 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 1 if short-term obligations are reclassified in the current year but not in the prior year, and zero otherwise. STOP is 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.** Significant at the 5% level.* Significant at the 10% level.

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