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Accrual Accounting and Risk: Abnormal Sales Growth, Accruals Quality, and Returns

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Abstract

This study proposes a new business risk proxy, unexpected sales growth (AG), which is measured as the difference between a firm’s sales growth rate in year t and its benchmark, the weighted average of sales growth rate over the past 3 years. I find that the AG is statistically, economically, significantly associated with other risk measures of risk (e.g., beta, volatility of cash flows, operating cycle, and negative earnings occurrences). I find that the more the sales growth of a firm in the current year deviates from its benchmark – the higher business risk the firm – the lower the accrual quality, and the higher future abnormal returns the firm, which support Penman’s (2016) theoretical conclusion that accrual accounting is for risk.

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Notes

  1. 1.

    A firm with slightly negative (or close to zero) AG has relatively stable sales growth, almost no fluctuation in sales growth.

  2. 2.

    Accounting textbooks show that the amount of accruals is associated with the amount of sales. The more sales a firm has in a given year t, the larger amount of receivable and payable associated with the sales the firm has. One would expect that the fluctuation in sales would cause fluctuation in accruals amount, therefore, impact on accruals quality as well. Not all firms with a change in sales growth have comparable accounting system that can capture changes in accruals related to change in sales growth immediately. Then, these firms may have unexplained accruals called discretionary accruals (Jones 1991), or accruals quality (Francis et al. 2005).

  3. 3.

    The Jensen-alpha approach is an alternative to the buy-and-hold abnormal returns calculating the calendar-time portfolio returns for firms in each decile of abnormal sales growth in preceding year, and calibrating whether portfolio returns are abnormal in a multifactor regression (Kothari and Warner 2007, 24-27). I use four-factor model in this study.

  4. 4.

    Thanks to Ken French for providing these factors on his website: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french.

  5. 5.

    In year t, a firm enters into a portfolio based on decile of ranked AG in the next month after its financial reports for prior year released if it had an AG falling into the range of that given decile of ranked AG at the end of year t-1, and leaves the portfolio 12 months after entering into it if its AG rank changes in the next year.

  6. 6.

    The based year in my sample is year 1964; therefore, the 1st year to have a firm-year observation of AQ is 1973. Because the AQ calculation needs 1-year lead cash flows data, the AQ in year 2010 needs cash flows data from year 2011.

  7. 7.

    The earliest year available in my sample is 1973 with 275 firms. However, it is not representative in my sample. Since 1973, there are about 1,700–3,800 firms per year. The sample reaches its largest number of distinct firms per year in 2002 (3,862), then declines over year toward the end of the sample period (2,621 distinct firms in year 2010).

  8. 8.

    The number of firms each year in Francis et al. (2005) sample ranges from about 1,500 per year in the early 1970s to about 3,500 firms per year toward year 2001.

  9. 9.

    Here, the current model is the full model, while the prior model without the newly added independent variable is the nested model. When an independent variable is added to the nested model, the R-square changes. If the R-square of the current model is significant, shown by F-test statistics, larger than that of the prior model, I can infer the newly added independent variable is a good explanatory variable for the dependent variable (Greene 2008, 81–92).

  10. 10.

    This study uses firms in Compustat in later years than Francis et al. (2005). The average accounting quality of Compustat sample firms in recent years are generally lower that of sample firms in earlier years because Compustat has expanded its database and included more small firms, which usually have a lower accounting quality than large firms.

  11. 11.

    Here, FM.T-stat is the Fama Macbeth t-statistics computed as \( FM.T- stat=\frac{\overline{t}\sqrt{N-1}}{stdev(t)} \). Z1 and Z2 statistics test whether the time-series mean t-statistics from yearly regressions is statistically different from zero, computed as \( {Z}_1=\frac{1}{\sqrt{N}}\sum \limits_{i=1}^N\frac{t_i}{\sqrt{k_i\left({k}_i-2\right)}} \), \( {Z}_2=\frac{\overline{t}}{stdev(t)\sqrt{N-1}} \), where t is t-statistic and k is the degrees of freedom for year i, and N is the number of years.

  12. 12.

    All VIFs are less than 10.

  13. 13.

    The magnitude of incremental R-square by adding an independent variable is related to the order of adding that independent variable. And also the regression method used in this subsection is annual regression different from OLS regression used in previous analyses.

  14. 14.

    Here, FM.T-stat is the Fama Macbeth t-statistics computed as \( FM.T- stat=\frac{\overline{t}\sqrt{N-1}}{stdev(t)} \). Z1 and Z2 statistics test whether the time-series mean t-statistics from yearly regressions is statistically different from zero, computed as \( {Z}_1=\frac{1}{\sqrt{N}}\sum \limits_{i=1}^N\frac{t_i}{\sqrt{k_i\left({k}_i-2\right)}} \), \( {Z}_2=\frac{\overline{t}}{stdev(t)\sqrt{N-1}} \) where, t is t-statistic and k is the degrees of freedom for year i, and N is the number of years.

  15. 15.

    Results of the value-weighted portfolio tests show that Jensen-alpha is 1.54% per month, then, will be 18.48% (1.54% x12) annually.

  16. 16.

    Results of regression Eq. (8) using firm-fixed effect regression method are not tabulated here, and available upon request.

  17. 17.

    The standardized coefficients on the AG and STDSales are 0.135 and 0.148, respectively, and statistically significant at 0.001 levels for the high-growth firms (AG >0). Results are available upon request.

  18. 18.

    Record, the larger (smaller) amount of AQ is the poorer (better) accruals quality.

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Acknowledgments

Min (Shirley) Liu cordially thanks James A. Ohlson, Jeff Madura, Mark Kohlbeck, and workshop participants at Florida Atlantic University for their helpful comments on the early draft of this manuscript. The author also gratefully thanks the organizers of the 2017 Journal of Accounting, Auditing & Finance Conference, Cheng-Few Lee, and the participants of the research session of the 2021 AAA Spark and 2021 AAA Annual Meeting Webinar for their insightful suggestions on this manuscript. All remaining errors are my own.

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Authors and Affiliations

  1. Department of Accounting, University of Illinois, Chicago, IL, USA

    Liu Min Shirley

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  1. Liu Min Shirley
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Correspondence to Liu Min Shirley .

Editor information

Editors and Affiliations

  1. Rutgers School of Business, Rutgers, The State University of New Jer, Piscataway, NJ, USA

    Cheng-Few Lee

  2. Phoenix, AZ, USA

    Alice C. Lee

Appendix

Appendix

Accruals Quality

The calculation of total accruals quality (AQ), innate and discretionary component of accruals quality, follows the empirical procedures in Francis et al. (2004 and 2005), adopting the form that larger value of the AQ is a worse accruals quality. The accruals quality is measured on a firm- and year-specific basis, using the accounting data for rolling 10-year windows, t-9 … t. This procedure requires firms with time-series data. This requirement biases the sample toward surviving firms (larger and more successful firms). However, the advantage of using the firm as its own benchmark over long window outweighs the sample selection bias (Eason et al. 1992).

I measure accrual quality using modified Dechow and Dichev’s (2002) model.

TCAj,t=φ0,j+φ1, jCFOj,t − 1+φ2,jCFOj,t+φ3, jCFOj,t + 1+ϕ4, jΔRevj,t+φ5, jPPEj,t+υj,t (4) Where, TCAj,t = firm j’s total current accruals in year t, calculated as TCAj, t=ΔCAj,t – ΔCLj,t-ΔCashj,t + ΔSTDEBTj,t; CFOj,t= NIBEj,t- TAj,t, firm j’s cash flow from operations in year t; NIBEj,t= firm j’s net income before extraordinary items (Data #18) in year t; TAj, t= ΔCAj,t – ΔCLj,t-ΔCashj,t+ ΔSTDEBTj,t- DEPNj,t, firm j’s total accruals in year t; ΔCAj,t= firm j’s change in current assets (Data #4) between year t-1 and year t; ΔCLj, t= firm j’s change in current liabilities (Data #5) between year t-1 and year t; ΔCashj, t= firm j’s change in cash (Data #1) between year t-1 and year t; ΔSTDEBTj, t= firm j’s change in debt in current liabilities (Data #34) between year t-1 and year t; DEPNj, t = firm j’s depreciation and amortization expense (Data #14) in year t; ΔRevj, t firm j’s change in revenues (Data #12) between year t-1 and year t; PPEj, t = firm j’s gross value of PPE (Data #7) in year t. For each firm-year, I estimate equation (4) by industry (classified as Fama and French 1997) and year. These estimations yield firm- and year-specific residuals,υj, t, which form the basis for the accrual quality metric, AQj, t=σ(υj, t), equal to the standard deviation of firm j’s estimated residuals over year t-4 through t. The larger (small) the standard deviation of residuals is the poorer (better) the earnings quality.

Other Variables Used in Analyses

Sales = gross sales, scaled in millions.

AT = total assets, unit is million dollars.

Mcap = market value of the firm in million dollars.

IB = income before extraordinary items.

ROE = return on equity for year t.

SaleGrowth = sales growth in year t, computed as the difference of sales over time t-1 and time t, divided by sales in year t-1 (ΔSalest-1,t /Salest-1).

AG = abnormal sales growth year t, computed as below.

$$ {AG}_t={SaleGrowth}_t\hbox{--} {SalesGBechmark}_t $$
$$ {SalesG Bechmark}_t=0.3\times {SalesG}_{t-3}+0.3\times {SalesG}_{t-2}+0.4\times {SalesG}_{t-1} $$

InnateAQ = innate component of accruals quality.

DisAQ = discretionary component of accruals quality.

Size = log value of total assets.

STDCFO = cash flow variability over the past 10 years.

STDSale = sales variability over the past 10 years.

OperCycle = operating cycle.

NegEarn = incidence of negative earnings over the past 10 years.

Leverage = total interest bearing debt to total assets.

PB = ratio of closing stock price per share at the end of fiscal year t to book value per share at the end of year t.

ROA = return on total assets for year t.

XFIN = external financing activities, computed as: XFIN= DEquity + Ddebt

DEquity = net cash received from the sale (and/or purchase) of common and preferred stock less cash dividend paid deflated by total assets

Ddebt = net cash received from the issuance (and/or reduction) of debt deflated by total assets [(DATA#111-DATA#114+DATA#301)/DATA#6]

Ret_f = 12-months buy-and-hold returns during the fiscal year t-1 to t, computed as:

$$ \operatorname{Re}t\_{f}_{j,t}=\frac{P_{j,t}-{P}_{j,t-1}}{P_{j,t-1}}-1 $$

Where Pj,t is firm j’s daily closing stock price at the end of fiscal year t, and Pj,t-1 is firm j’s daily closing stock price at the end of fiscal year t-1.

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Min Shirley, L. (2021). Accrual Accounting and Risk: Abnormal Sales Growth, Accruals Quality, and Returns. In: Lee, CF., Lee, A.C. (eds) Encyclopedia of Finance. Springer, Cham. https://doi.org/10.1007/978-3-030-73443-5_106-1

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  • DOI: https://doi.org/10.1007/978-3-030-73443-5_106-1

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