Debt reclassification, accrual and cash flow persistence,
                              and capital market consequences


...
Debt reclassification, accrual and cash flow persistence,
                                and capital market consequences
...
Debt reclassification, accrual and cash flow persistence,
                                and capital market consequences
...
exclude short-term obligations from current liabilities if the firm 1) intends to replace the

maturing short-term debt is...
Lys and Vincent 2001). Rather, we explore the association between reclassification and the

persistence of performance mea...
whether and how to incorporate reclassified debt into their forecasts and recommendations. Our

debt-rating and market-val...
likely to reclassify. Conversely, credit-scoring models and debt covenants typically specify

maximum levels for total lev...
We use earnings persistence as a broad test of earnings quality. While only one measure

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

persistence of cash flows from...
(Millon and Thakor 1985). In this section, we argue that debt-rating agencies may privately learn

firms’ rationale for re...
record of the firm’s management as well as to management’s overall philosophy. To the extent

that reclassification is ass...
HYPOTHESIS 4: The market value of firms’ equity is negatively (positively) related to the
          reclassification (decl...
firms with firms that exhibit no reclassification activity at any point during the 1989-2000

period. The results we find ...
The association between reclassification and persistence of earnings, cash flows and accruals

       The following model ...
that credit analysts deem relevant. Thus, the model includes a parsimonious set of control

variables suggested by theory ...
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’...
total assets; BVL is the book value of total liabilities including the reclassified amount. To

calculate abnormal earning...
Sample and descriptive statistics

       Table 1 presents descriptive statistics for our sample. Panel A explains the sam...
Findings regarding firms’ reclassification decisions

       Table 3 reports results for our logistic regressions (equatio...
measures the incremental earnings persistence for firms that reclassify, and it has a negative

estimated coefficient (β3 ...
directions of changes in reclassification behavior (i.e., initial reclassification, ongoing

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

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

well as by the market val...
implies that although managers may strategically classify short-term debt as long-term, credit

analysts do not reward thi...
Endnotes




           23
References

Abarbanell, J., Bernard, V. 2000. Is the US stock market myopic? Journal of Accounting
   Research 38, 221-247...
Fields, T., Lys, T., Vincent, L. 2001. Empirical research on accounting choice. Journal of
    Accounting and Economics 31...
Maddala, G. 1983. Limited-dependent and qualitative variables in econometrics. Cambridge
  University Press, New York, NY....
Exhibit 1
Excerpt from the Debt Footnote of Pacificorp’s 1998 Form 10-K

The Company’s long-term debt was as follows:
(in ...
Table 1
Sample selection

Panel A: Criteria for sample of firm-years that reclassified at least once between 1989 and 2000...
(This table is continued on the next page.)




29
Notes:
The table reports sample selection results. We applied the following search term to the AR group file within the
NA...
Table 2
 Descriptive statistics for all firm-years between 1989 and 2000

 Panel A: Comparison of firm-years with and with...
Table 2 (Continued)

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

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

Equation 1: RECLASSi,t = β0 + β1 ROAi,...
Table 4
Association between reclassified amounts and the persistence of earnings, cash flows, and accruals

Equation 3:   ...
Table 5
Association between reclassified amounts and debt rating changes

Equation 5: RATINGΔi,t0+1 = β 0 + β1 + β2 ROAΔi,...
Table 5 (Continued)

Notes:
The table reports parameter estimates, χ2 statistics, and explanatory power of a logistical re...
Table 6
Association between reclassified amounts and market value of equity

 Equation 7: MVEi,t = β0 + β 1 BVAi,t + β2 BV...
Table 6 (Continued)

Notes:
The table reports parameter estimates and t-statistics for an OLS regression using data for ea...
1
 “A short-term obligation ... shall be excluded from current liabilities only if 1) the enterprise intends to refinance ...
16
  Abarbanell and Bernard (2000) report consistent results for abnormal earnings calculated with discount rates ranging ...
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Debt reclassification, accrual and cash flow persistence,

  1. 1. Debt reclassification, accrual and cash flow persistence, and capital market consequences Jeffrey D. Gramlich University of Southern Maine William Mayew University of Texas at Austin Mary Lea McAnally* Texas A&M University August 2003 * Corresponding author: Accounting Department Wehner 401Q Mays Business School Texas A&M University College Station, TX, 77843 Phone: (979) 845-5017 Fax: (979) 845-0028 Email: 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.
  2. 2. 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
  3. 3. 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 1
  4. 4. 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 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, 2
  5. 5. 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. 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 3
  6. 6. 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 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 4
  7. 7. 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 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). 5
  8. 8. 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 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 6
  9. 9. 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 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 7
  10. 10. (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 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 8
  11. 11. 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- 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: 9
  12. 12. 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. 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 10
  13. 13. 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+υi,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 υ is the unexplained residual. Both CRATIO and LEV are calculated after removing the effects of 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.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 ) (2) + β3(CRATIOi,t - MedianCRATIOi,t ) + β4(CFOi,t - MedianCFOi,t ) + β5RECLASSi,t-1 + υi,t We compute industry medians using data from the entire Compustat database for each year. Other variables are as previously defined. 11
  14. 14. 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 = βO + β1 RECLASSi,t-1+ β2 ROAi,t-1 + β3 (ROAi,t-1× RECLASSi,t-1) + υi,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 = β0 + β1 RECLASSi,t-1 + β4 CFOi,t-1+ β5 ACCi,t-1+ β6 (CFOi,t-1× RECLASSi,t-1) + (4) β7 (ACCi,t-1× RECLASSi,t-1) + υi,t 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 Poor’s “Corporate Ratings Criteria” (Standard and Poor’s 2001) to determine additional factors 12
  15. 15. 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 (5) 19 + β6 SIZEΔi,t + β7 RECLAMTΔi,t + ∑β j =8 j INDi + υi,t 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, RATING∆i,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: RATINGΔi,t0+1 = β 0 + β1 + β2 ROAΔi,t + β3 LEVΔi,t + β4 CRATIOΔi,t + β5 CFOΔi,t (6) + β6 SIZEΔi,t + β7 RECLAMTΔi,t + β8 STARTi,t + β9 STOPi,t + β10 (STARTi,t × RECLAMTi,t) 23 + β11 (STOPi,t × RECLAMTi,t-1) + ∑ β j INDi + υi,t j =12 13
  16. 16. 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 = β0 + β 1 BVAi,t + β2 BVLi,t + β3 AEi,t + β4 RECLAMTi,t + υi,t (7) 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 14
  17. 17. 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 = β0 + β 1 BVAi,t + β2 BVLi,t + β3AEi,t + β4 STARTi,t + β5 STOPi,t (8) + β6 (RECLAMTi,t - RECLAMTi,t-1 )+ β7 (STARTi,t × RECLAMTi,t) + β8 (STOPi,t × RECLAMTi,t-1) + υi,t We predict that initial reclassification will decrease equity values (i.e. β4 < 0 and β7 < 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. β5 > 0 and β8 > 0). 4. Results 15
  18. 18. 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, 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 . 16
  19. 19. 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) 17
  20. 20. measures the incremental earnings persistence for firms that reclassify, and it has a negative estimated coefficient (β3 = -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 18
  21. 21. 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 (β10 = -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 (β6 = -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. 19
  22. 22. 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 20
  23. 23. 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 21
  24. 24. 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. 22
  25. 25. Endnotes 23
  26. 26. References Abarbanell, J., Bernard, V. 2000. Is the US stock market myopic? Journal of Accounting Research 38, 221-247. Altman, E. 2000. Predicting financial distress of companies: revisiting the Z-score and ZETA models. Working paper, New York University. Barron, M., Clare, A., Thomas, S. 1997. The effect of bond rating changes and new ratings on UK stock returns. Journal of Business Finance & Accounting 24(3/4): 497-509. Barth, M., Beaver, W., Hand, J., Landsman, W. 1999. Accruals, cash flows, and equity values. Review of Accounting Studies 4 205-229. Barth, M., Cram, D., Nelson, K. 2001. Accruals and the prediction of future cash flows. The Accounting Review 76: 27-58. Barth, M., Hutton, A. 2001. Financial analysts and the pricing of accruals. Stanford University and Harvard University, Working paper. Beatty, A., Ramesh, K., Weber, J. 2002. The importance of accounting changes in debt contracts: the cost of flexibility in covenant calculations. Journal of Accounting and Economics 33 205-227. Belsley, D., Kuh, E., Welsch, R. 1980. Regression diagnostics. John Wiley & Sons, Inc., New York, NY. Butler, A. and K. Rodgers 2002. Relationship rating: How do bond-rating agencies process information. Rice University and Pennsylvania State University, Working paper, October. Cantwell, J. 1998. Managing credit ratings and rating agency relationships. TMA Journal 18, 14-22. Credit 1991. Rating agencies answer questions. Anonymous author. 17: 13-16. Dechow, P. 1994. Accounting earnings and cash flows as measures of firm performance: the role of accounting accruals. Journal of Accounting and Economics 18, 3-42. Dechow, P., Kothari, S., Watts, R. 1998. The relation between earnings and cash flows. Journal of Accounting and Economics 25, 133-168. Dichev, I., Piotroski, J. 2001. The long-run stock returns following bond ratings changes. Journal of Finance 66, 173-203. Ederington, L. 1985. Classification models and bond ratings. The Financial Review 20, 237-261. 24
  27. 27. Fields, T., Lys, T., Vincent, L. 2001. Empirical research on accounting choice. Journal of Accounting and Economics 31, 255-307. Financial Accounting Standards Board 1975. Statement of financial accounting standards no. 6: classification of short-term obligations expected to be refinanced. FASB, Stamford, CT. Financial Accounting Standards Board 1980, Statement of financial accounting concepts no. 2. FASB, Stamford, CT. Gramlich, J., McAnally, M., Thomas, J. 2001. Balance sheet management: the case of short-term obligations that are reclassified as long-term debt. Journal of Accounting Research 39, 283-296. Hand, J., Holthausen, R., Leftwich, R. 1992. The effects of bond rating agency announcements on bond and stock prices. Journal of Finance 47, 733-752. Hand, J. Landsman, W. 2000. The pricing of dividends in equity valuation. University of North Carolina, Chapel Hill, Working paper. Hanlon, M. 2002. The persistence of earnings, accruals and cash flows when firms have large book-tax differences. University of Washington, Working paper. Holthausen, R., Leftwich, R. 1983. The economic consequences of accounting choice: Implications of costly contracting and monitoring. Journal of Accounting and Economics 5, 77-117. Hopkins, P. 1996. The effect of financial statement classification of hybrid financial instruments on financial analysts' stock price judgments. Journal of Accounting Research 34 (Suppl.), 33-50. Imhoff, E., Thomas, J. 1988. Economic consequences of accounting standards: the lease disclosure rule change. Journal of Accounting and Economics 10, 277-310. Jonas, G., Blanchet, J. 2000. Assessing Quality of Financial Reporting. Accounting Horizons 14 (3): 353-363. Kliger, D., Sarig, O. 2000. The information value of bond ratings. Journal of Finance 55, 2879-2902. Kormendi, R., Lipe, R. 1987. Earnings innovations, earnings persistence, and stock returns. Journal of Business 60, 323-345. Lev, B. Industry Averages as Targets for Financial Ratios. Journal of Accounting Research. (Autumn 1969): 290-299. 25
  28. 28. Maddala, G. 1983. Limited-dependent and qualitative variables in econometrics. Cambridge University Press, New York, NY. Mester, L. 1997. What’s the point in credit scoring? Federal Bank of Philadelphia Business Review, September/October 3-16. Millon, M., Thakor, A. 1985. Moral hazard and information sharing: a model of financial information gathering agencies. Journal of Finance 40, 1403-1422. Mohr, R. 1988. Unconsolidated finance subsidiaries: characteristics and debt/equity effects. Accounting Horizons 2 (March), 27-34. Ohlson, J. 1995. Earnings, book values and dividends in security valuation. Contemporary Accounting Research 11, 661-687. Picker, I. 1991. The ratings game. Institutional Investor 25 (September): 73-77. Reiter, S. 1990. The use of bond market data in accounting research. Journal of Accounting Literature 9, 183-228. Schipper, K., Vincent, L. 2002. Earnings quality. Northwestern University, Working paper. Sloan, R. 1996. Do stock prices fully reflect information in accruals and cash flows about future earnings? Accounting Review 71, 289-315. Standard and Poor’s 2001. Corporate rating criteria. McGraw-Hill Companies, New York, NY. Ziebart, D., Reiter, S. 1992. Bond ratings, bond yields, and financial information. Contemporary Accounting Research 9, 252-282. 26
  29. 29. 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 2025 through 2035 175.8 175.8 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. 27
  30. 30. 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 3,080 Less: 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 SIC Reclassifying Non- Total sample categories firm-years reclassifying firm years Clothing 22-23 24 5 29 Financial 60-69 28 35 63 Food 1-7,20-21 62 52 114 Media 27,48 231 361 652 Metallurgy 34 23 40 63 Miscellaneous manufacturing 39 7 0 7 Oil 13,46 17 72 89 Retail sales 50-59 94 104 198 Services and other 70-79,83,99 55 62 117 Transport 40-45,47 21 47 68 Utilities 49 74 56 130 Wood products 24-26 56 86 142 Totals 752 932 1,684 Panel C: Sample distribution across years Year Reclassifying Non- Total sample firm-years reclassifying firm-years 1989 49 35 84 1990 53 31 84 1991 47 37 84 1992 48 36 84 1993 62 73 136 1994 63 103 166 1995 87 86 173 1996 104 82 186 1997 111 71 182 1998 113 67 180 1999 103 67 170 2000 92 64 156 Totals 752 932 1,684 28
  31. 31. (This table is continued on the next page.) 29
  32. 32. 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). 30
  33. 33. Table 2 Descriptive statistics for all firm-years between 1989 and 2000 Panel A: Comparison of firm-years with and without reclassification Firm-years with reclassification Firm-years without reclassification (N=932) (N=752) 75th 25th 75th 25th Attributes Mean Median Percentile Percentile Mean Median Percentile 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 177 128 debt-rating downgrades (19.0%) (17.0%) Number (percent) of 99 90 debt-rating upgrades (10.6%) (12.0%) Number (percent) of 78 38 debt-rating downgrades (8.4%)** (5.1%) (This table is continued on the next page.) 31
  34. 34. Table 2 (Continued) Panel B: Before and after comparisons for a sample of firms that start and stop reclassification Firm-years where reclassification Firm-years where reclassification starts (n =172) stops (n = 123) First year with First year Prior year reclass Prior year without reclass Attributes Mean (Median) Mean (Median) Mean (Median) Mean (Median) Assets, $ millions 5,204 6,334** 6,266 6,486 (6,799) (8,539)** (3,633) (3,875)** Market value of equity, 4,458 4,984** 4,698 4,738 $ millions (2,866) (3,148)** (2,403) (2,549)** Return on assets 7.1%** 5.8% 5.1% 5.5% (6.9%)** (5.9%) (5.7%) (5.7%) Cash from operations to 10.7%* 9.9% 9.0% 9.6% assets (10.8%)** (10.2%) (7.7%) (9.6%) Current ratio, as reported‡ 1.443 1.416 1.434 1.457 (1.250) (1.277) (1.300) (1.254) Current ratio, without 1.443** 1.120 1.187 1.457** reclassification† (1.250)** (1.023) (1.085) (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. 32
  35. 35. Table 3 Factors 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 + υi,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 + υi,t Sample 1 (n =1,574) † Sample 2 (n =724) ‡ Equation 1 Equation 2 Equation 1 Equation 2 Variable (predicted sign) Estimate (χ2) Estimate (χ2) Estimate (χ2) Estimate (χ2) Intercept (?) 0.870*** -1.044*** 1.123** -1.445*** (11.23) (125.26) (5.49) (109.11) ROAi,t (−) 0.105 0.986 -3.335 -3.226 (0.01) (0.63) (1.98) (1.88) LEVi,t (−) -2.703*** -2.612*** -3.329*** -2.980** (27.15) (23.01) (8.03) (6.12) CRATIOi,t (−) -1.299*** -1.534*** -1.081*** -1.190*** (90.53) (94.21) (14.82) (14.90) CFOi,t (−) 0.253 -0.230 -3.321** -3.417** (0.03) (0.027) (6.21) (6.37) RECLASSi,t-1 (+) 3.012*** 3.006*** 17.527 17.463 (449.07) (445.42) (0.01) (0.01) Model likelihood 834.94 840.84 651.41 651.36 Chi-square Psuedo-R2 41% 41% 59% 59% % correctly predicted 88% 88% 95% 95% Notes: The table reports parameter estimates, χ2 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. 33
  36. 36. Table 4 Association 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 4 Variable (predicted sign) Parameter t-statistic Parameter t-statistic estimate estimate Intercept 0.017 8.21*** -0.004 -1.31 *** RECLASSi,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. 34
  37. 37. Table 5 Association 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 19 + β7 RECLAMTΔi,t + ∑ β j INDi + υi,t j =8 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) 23 + β11 (STOPi,t × RECLAMTΔi,t) + ∑ β j INDi + υi,t j =12 Variable (predicted sign) Equation 5 Equation 6 Estimate (χ2) Estimate (χ2) Intercept for upgrade (?) -2.14*** -2.144*** (77.78) (75.72) Intercept for downgrade (?) 1.171*** 1.200*** (24.71) (25.04) ROAΔi,t (+) 4.693*** 4.656*** (20.30) (19.77) LEVΔi,t (−) -6.220*** -6.648*** (44.62) (48.18) CRATIOΔi,t (+) -0.031 0.009 (0.31) (0.02) CFOΔi,t (+) 1.000 1.083 (0.71) (0.82) SIZEΔi,t (?) -0.207 -0.144 (0.39) (0.18) RECLAMTΔi,t (−) -4.348** -4.805** (17.23) (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 likelihood 100.76 121.21 Chi-square Psuedo-R2 7.5% 8.9% % correctly predicted 65.0.% 65.5% (This table is continued on the next page.) 35
  38. 38. Table 5 (Continued) Notes: The table reports parameter estimates, χ2 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. 36
  39. 39. Table 6 Association between reclassified amounts and market value of equity Equation 7: MVEi,t = β0 + β 1 BVAi,t + β2 BVLi,t + β3 AEi,t + β4 RECLAMTi,t + υi,t Equation 8: MVEi,t = β0 + β 1 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) Equation 7 Equation 8 Estimate (t-statistic) Estimate (t-statistic) Intercept (?) 911.453*** 1,102.450*** (3.10) (3.47) BVAi,t (+) 2.945*** 2.964*** (27.45) (28.10) BVLi,t (−) -3.099*** -3.132*** (-23.12) (-23.46) AEi,t (+) 12.870*** 13.170*** (24.05) (24.39) RECLAMTi,t (−) 0.139 (0.41) STARTi,t (−) -905.634 (-1.08) STOPi,t (+) -1,434.492 (-1.48) RECLAMTi,t − (−) -1.243** RECLAMTi,t-1 (-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.) 37
  40. 40. 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); 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. 38
  41. 41. 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). 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. 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). 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. 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. 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.
  42. 42. 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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