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Selling-price estimates in revenue recognition and the usefulness of financial statements

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Abstract

Revenue is one of the largest and most value-relevant items in firms’ financial statements. Based on the “realizable” and the “earned” criteria of SFAC No. 5 (FASB in Concepts statement no. 5. Recognition and measurement in financial statements of business enterprises, 1984), revenues should reflect both selling price and timing of delivery. Of those two aspects, selling-price estimates are required for revenue recognition when standalone selling prices for products and services are not available. In this study, I examine the effects of selling-price estimates in revenue recognition on the contracting and informational roles of financial statements. Particularly, I examine the setting of SOP 97-2 (AICPA in Software revenue recognition. Statement of Position (SOP) 97-2, AICPA, New York, 1997) that removed software firms’ flexibility to recognize revenues using selling-price estimates. I find that SOP 97-2 implementation did not improve the contracting role of earnings. However its implementation partly shifted the informational role of financial statements from income-statement to balance-sheet components.

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Notes

  1. According to studies on accounting frauds and earnings restatements, revenue is among the most frequently misreported items in firms’ financial statements (Dechow et al. 1996; COSO 1999; Palmrose et al. 2004).

  2. Kothari et al. (Kothari et al. 2010, p 282) call for research that would be “useful to standard-setters as they grapple with questions of how to trade-off the different informational and contracting demands of various contracting parties.” This study might interest standard setters, as described later.

  3. The vendor specific objective evidence (VSOE) condition of SOP 97-2 requires firms to establish the selling prices of each component based solely on the firm’s own history of standalone transactions for that component (Sondhi 2006). Recently, EITF 08-1[FASB 2009a]) and ASU 2009-13 [FASB 2009b]) have partially relaxed the objective-price constraint.

  4. For example, firms could shift to real activities manipulation, defined as “a purposeful action to alter reported earnings in a particular direction, which is achieved by changing the timing or structuring of an operation, investment, or financing transaction, and which often has suboptimal business consequences” (Zang 2012, p 676). Examining this aspect, however, is beyond the scope of this study.

  5. Watts and Zimmerman (1990), Healy and Wahlen (1999), Fields et al. (2001), Altamuro et al. (2005), Dechow et al. (2010), Badertscher et al. (2012).

  6. I conduct two “placebo tests” in settings unaffected by SOP 97-2 to ascertain whether my findings are due to patterns unrelated to SOP 97-2 implementation: on software firms in the year before the implementation year and on control firms in the SOP 97-2 implementation year. In neither setting are abnormal deferrals priced as revenues.

  7. For example, Microsoft’s deferred-revenue account reached $17 billion at the end of 2011.

  8. This interpretation is consistent with anecdotal evidence. While discussing 2008 fourth quarter financial performance, Apple, Inc.’s CFO indicated that Apple’s pro forma sales and net income would be higher by 48 and 115 % than the reported amounts, respectively, “by backing out the September quarter’s amortization of deferred revenue from iPhone and Apple TV sales and adding back all amounts generally due at the time of sale.”

  9. This project was started at the behest of the SEC to address the revenue-recognition issues of multiple-element firms (Turner 2001). This project has been identified as among FASB’s highest priority projects by its advisory council in its previous four surveys (FASAC 2004, 2005, 2006, 2009).

  10. Among the firms that restated their accounts, technology firms were disproportionately represented.

  11. SOP 97-2 builds upon the concepts of earnings process completion and revenue realizability (SFAC No. 5). It requires compliance with four necessary conditions before revenue can be recognized: (1) a formal arrangement, (2) completion of delivery, (3) determinable fees, and (4) revenue realizability.

  12. The criteria used in practice to establish VSOE are stringent. For example, a firm should provide evidence from at least 30 prior randomly selected transactions, and 85 % of such transactions should have been priced within 15 % of the median price (Sondhi 2006).

  13. Typically software firms match variable expenses, such as the cost of goods sold and warranty/maintenance costs, with revenues, but immediately expense R&D, product development, and marketing costs.

  14. Francis and Schipper (1999) interpret earnings informativeness, measured by long-window association tests, as earnings’ ability to capture or summarize information, regardless of source, that affects share values. This interpretation does not require that financial statements be the earliest source of information (Hanlon et al. 2008).

  15. RECCHY and OANCFY are year-to-date variables. Thus, for each of those variables, I subtract the variable by its value in the previous quarter in fiscal quarters two through four. Moreover, I use the negative of RECCHY because a positive (negative) value in the statement of cash flows represents a decrease (increase) in accounts receivable.

  16. I obtain similar results after controlling for firms’ incentives to meet or beat earnings benchmarks (results not tabulated). I control for firm size (Log of Assets [Compustat AT]), Debt (Compustat DLTISY), secondary equity offerings (Compustat SSTKY), and cash balances (Compustat CHEQ).

  17. Altamuro et al. (2005) estimate a regression of changes in stock prices during a short time interval (that is, during a few days surrounding earnings-announcement dates) on seasonally adjusted changes in earnings. I find partial support for H2a using this model (results not tabulated). However, I do not use this model for my main tests for the following reasons. First, a change specification assumes that the investors’ earnings expectations at the beginning of the return window equal the prior-period performance. This is an unrealistic assumption in my setting because, as I show later, software firms in the late 1990s not only grew rapidly, they also varied widely in their growth rates. Second, Ball et al. (2012) caution against drawing inferences exclusively from market reactions around “announcement periods,” because audited financial reporting indirectly affects information released at other times and through other channels. Moreover, the shorter the window used to measure announcement period returns, the higher the “errors” because of the “prices lead earnings” phenomenon (Kothari 2001, p 129). Such errors are especially important in my setting, because in the late 1990s, software firms’ share prices routinely responded to events such as new product launches and strategic partnerships, long before the firms reported their earnings effects.

  18. I obtain similar results after controlling for fixed and interaction effects of the following ERC determinants: persistence, cost of capital (risk-free rate and beta), and firm size (Kormendi and Lipe 1987; Collins and Kothari 1989; Easton and Zmijewski 1989; Freeman 1987; Collins et al. 1987).

  19. I use reported revenues to estimate abnormal revenue deferrals, as there is no better alternative. Prior studies on revenue manipulation (Stubben 2010) also use reported revenues to estimate the magnitude of manipulated revenues. Using this measure also enables me to isolate the component of deferred revenues that is unrelated to reported revenues.

  20. Zhang (2005) and Altamuro et al. (2005) examine the implementation effects of SOP 91-1 and SAB 101 by capitalizing on reported cumulative effect adjustments. In contrast to SOP 91-1 and SAB 101, SOP 97-2 was applicable on a prospective basis and specifically prohibited retrospective application. Therefore, firms did not report cumulative effect adjustments upon SOP 97-2 implementation.

  21. However, firms that defer reporting the first type of revenue record no corresponding revenues, assets, or liabilities because no transaction has yet occurred. Consequently, post-implementation financial statements would not reveal this effect of new rules. This effect can, however, be measured from decreases in an asset account called “revenues in excess of billings.” I attempted to hand-collect data on this asset account. I found that this account is economically insignificant, because very few firms report this account.

  22. This SOP was effective for fiscal years beginning after December 15, 1997. Thus, for firms with fiscal years ending in December, the transactions in the year 1998 were recorded using SOP 97-2. Consequently, the SOP 97-2 implementation year for December year-end firms corresponds to Compustat fiscal year 1998. However, for all other firms, the first fiscal year with transactions recorded using SOP 97-2 ends in calendar year 1999. Of those firms, for firms with year-ends June through November, this year corresponds to Compustat fiscal year 1999. Nevertheless, for firms with year-ends January through May, this year corresponds to Compustat fiscal year 1998. I hand-collect data on actual implementation years.

  23. SOP 97-2 could affect all firms whose products contain a significant software component. Accordingly, I define unaffected as not being in industries beginning with three-digit SIC codes 365 (household audio, video equipment, audio receiving), 366 (communication equipment), 367 (electronic components, semiconductors), 368 (computer hardware), 481 (telephone communications), or 737 (computer programming, software, data processing).

  24. I winsorize at 5 and 95 % levels. These levels are consistent with Zhang (2005), who excludes all regression observations with standard errors greater than two-sigma limits, because software firms display large variations in their characteristics. I obtain qualitatively similar results by winsorizing at 1 and 99 % levels.

  25. As noted in Sect. 5, the average implementation-year stock return for software firms was 44 %. Efendi et al. (2007) show that abnormal increases in equity valuation in the late 1990s increased managers’ personal incentives (for example, to protect the value of their stock-option holdings) as well as firm incentives (for example, to conduct secondary equity offerings at favorable prices) to misreport earnings.

  26. I use panel data with eight observations per firm. This model allows error variances to be correlated across firms and quarters.

  27. The sensitivity of dependent variable to a unit change in independent variable (XK) in a logistic regression is calculated by the following formula (LeClere 1992, Equation 6, p 771): \( \frac{\partial P}{{\partial X_{K} }} = \frac{{e^{ - (\alpha + \beta \times X)} }}{{(1 + e^{{ - \left( {\alpha + \beta \times X} \right)}} )^{2} }} \times \beta_{K} \)

    I calculate change in the probability of meet-beat due to changes in revenue accruals from its 25th percentile value to its 75th percentile value and change in categorical variable (After) from zero to one, with all other independent variables held constant at their median values.

  28. The Control group for this test is different from the control groups for H1 and H2 tests, because this test requires data on deferred -revenue accounts. Most firms, however, do not report this account. Thus, I identify the control group for H3 tests by first identifying firms that reported deferred revenue accounts in year 2002 when Compustat started reporting details on this data item. I then hand-collect deferred amounts for those firms from their 1997 and 1998 10-K filings. For this test, I relax matching criteria for selecting control firms described in Sect. 5.

  29. For this test, I hand-collect data on quarterly deferred-revenue accounts from software firms’ 10-Q filings. Then, I estimate rules-created deferrals on a quarterly basis by using a similar procedure as in Eq. (5), using similar sized unaffected firms’ same quarter’s average revenue growth as the instrumental variable for RevenueGrowth, and deflating dollar-denominated values by market value of equity.

  30. I find that, by 2009, the number of firms reporting deferred-revenue accounts increased to 2,270, which constitutes 25.9 % of the listed firm population. This finding suggest that the number of firms using multi-element revenue arrangements has increased, consistent with the notion that more and more firms now provide a solution to their customers, as opposed to just selling a standalone product or service (Turner 1999a, b).

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Acknowledgments

This paper is based on my dissertation, completed at Texas A&M University. I thank Lakshmanan Shivakumar (the editor) and an anonymous referee for constructive feedback that has considerably improved the paper. I thank Anwer Ahmed, Eli Bartov, Craig Chapman, Kevin Clarke, Michael Clement, Ian Gow, Ross Jennings, Steven Kachelmeier, Ranjani Krishnan, Bill Kinney, S. P. Kothari, Wayne Landsman, Christian Leuz, Tom Lys, Bob Magee, Mary Lea McAnally, Lillian Mills, Jim Ohlson, Tom Omer, Kathy Petroni, Ray Pfeiffer, Jana Raedy, Joshua Ronen, Katherine Schipper, Kumar Sivakumar, Abbie Smith, Shyam Sunder, Ed Swanson (chair), Senyo Tse, Beverly Walther, Wan Wongsunwai, Linda Vincent, Jeff Wilks, Paul Zarowin, and workshop participants at University of Chicago, Duke University, Michigan State University, New York University, University of North Carolina, Northwestern University, Texas A&M University, University of Texas (Austin), the 2008 American Accounting Association annual meeting, and the 2009 Financial Accounting Standards Research Initiative’s online session on revenue recognition for helpful comments. I greatly acknowledge the Deloitte Foundations’ doctoral fellowship for supporting my dissertation. I thank Zhenu Hou, Tiantian Ren, and Nick He for their assistance in hand-collecting data on deferred revenues.

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Appendix: Definitions of variables

Appendix: Definitions of variables

Software Firms: Firms in pre-packaged software (SIC code 7372) and integrated software and services (SIC code 7373) sub-industries, which comprise more than 90 % of all firms in the software industry and which routinely use multiple-element contracts

Unaffected Firms: Firms from industries other than those beginning with three-digit SIC codes 365 (household audio, video equipment, audio receiving), 366 (communication equipment), 367 (electronic components, semiconductors), 368 (computer hardware), 481 (telephone communications), or 737 (computer programming, software, data processing)

Control Firms: I first estimate earnings response coefficient, earnings management intensity (BeatZero, defined below), and accruals-future cash flow relation for each software and unaffected firm using four quarterly observations in the before year. Then I estimate the decile ranks for each firm separately for each of the following four dimensions: aforesaid three variables plus the firm size (the market value of equity). Then, I find the closest one-to-one unaffected firm match for each software firm that has the smallest cumulative difference of decile ranks on the above four dimensions, after imposing a constraint of no more than a two decile rank difference in any particular dimension

Quarterly Data (H1 and H2)

Implementation year = Fiscal year in which firm starts applying the SOP 97-2 revenue-recognition rules. I obtain this information from firm’s 10-K filings by studying the “revenue recognition” section in the “significant accounting policies” footnote. For most firms, it corresponds to fiscal year 1998

Before year = The year before the implementation year (1997 for control firms)

After year = The implementation year (1998 for control firms)

After = Dummy variables set to zero for four quarters’ observations in the before year and one for four quarters’ observations in the after year

After for Control firms equals zero (one) for four quarters in year 1997 (1998)

Earnings = Income before extraordinary items (Compustat IBQ)

Operating cash flows = Compustat OANCFY. I subtract OANCFY by its previous quarter’s value in fiscal quarters two through four

RET = Buy-and-hold returns, measured as ([End-of-quarter share price {Compustat PRCCQ}/Adjustment factor {Compustat AJEXQ} + Dividend per share {Compustat DVPSPQ}/Adjustment factor—Beginning-of-quarter share price/Beginning-of-quarter adjustment factor]/[Beginning-of-quarter share price/Beginning-of-quarter adjustment factor])

Accruals = Earnings—operating cash flows

Revenue accruals = Changes in accounts receivables (–1 × Compustat RECCHY). I subtract this value by its previous quarter’s value in fiscal quarters two through four

Nonrevenue accruals = accruals—revenue accruals

Beginning-of-quarter market value of equity = Common shares outstanding (Compustat CSHOQ) × fiscal quarter end closing price (Compustat PRCCQ), both measured at the end of previous quarter

BeatZero = Defined as one if net income (NIQ) is positive but no more than 0.5 % of beginning-of-quarter market value of equity and zero otherwise

BeatPrior = Defined as one if seasonally adjusted change in net income (NIQ) is positive but no more than 0.25 % of beginning-of-quarter market value of equity and zero otherwise

D_Loss = Defined as one if RET is negative and zero otherwise

Annual Data (H3)

Assets = Compustat AT

Revenues = Compustat SALE

Earnings = Income before extraordinary items (Compustat IB)

Expenses = Revenues—earnings

Share price = Compustat PRCCF

Shares outstanding = Compustat CSHO

RET = Buy-and-hold returns, measured as ([End-of-year share price {Compustat PRCCF}/Adjustment factor {Compustat AJEX} + Dividend per share {Compustat DVPSP_F}/Adjustment factor—Beginning-of-year share price/Beginning-of-year adjustment factor]/[Beginning-of-year share price/Beginning-of-year adjustment factor])

Liabilities = Assets—Book value of equity (Compustat CEQ)

Deferred revenues = Deferred revenue account. I obtain information on this account from the liabilities section and footnotes in firms’ 10-K filings. I search for terms including “deferred revenues,” “customer advances,” “unearned revenues,” and “billings in excess of revenues.”

Deferred revenues (after 2002) = Compustat DRC + DRLT

Other liabilities = Liabilities—Deferred Revenues

Working-capital cycle = (defined as [Accounts Receivable {AR} + Inventory {INV} + Other Current Assets {OCA} − Accounts Payable {AP} − Tax Payable {TP} − Other Current Liabilities {OCL}] × 365/Revenues)

Abnormal deferrals = The unexplained portion (residual plus the intercept) from the following equation: \( ChangeInDeferredRevenue_{{i,t - 1{\kern 1pt} \,to\,t}} = \beta _{1} + \beta _{2} \times Revenue_{{i,t}} + \beta _{3} \times RevenueGrowth_{{i,t - 1\,to\,t}} + \beta _{4} \times WorkingCapitalCycle_{{i,t}} + \varepsilon _{{i,t}} \quad (5) \) Dollar-denominated values are deflated by common shares outstanding.

I use two instrumental variables for estimating RevenueGrowth:

 Instrument 1: Average revenue growth in the after year for unaffected firms belonging to the same firm-size decile (market value of equity) as the software firm

 Instrument 2: The same software firm’s revenue growth in the before year

Rules-created deferrals = Abnormal deferrals in the implementation year

Normal deferrals = Deferred revenues—Abnormal deferrals

Quarterly Data (Additional H2b Tests)

Rules-created deferralsv = Estimated on a quarterly basis. Represent the unexplained portion (residual plus the intercept) of Eq. (5) above estimated using deferred-revenues (hand-collected from firms’ 10-Q filings) and by deflating all dollar-denominated variables by beginning-of-quarter market value of equity. I use the following instrument for RevenueGrowth: the average revenue growth in the same quarter for unaffected firms belonging to the same firm-size decile (market value of equity)

  1. All regression variables are winsorized at 5  and 95 %

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Srivastava, A. Selling-price estimates in revenue recognition and the usefulness of financial statements. Rev Account Stud 19, 661–697 (2014). https://doi.org/10.1007/s11142-013-9263-6

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