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Negative accounting earnings and gross domestic product

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Abstract

Konchitchki and Patatoukas Journal of Accounting and Economics 57 (1-2), 76–88, (2014a) show that aggregate accounting earnings growth predicts future nominal gross domestic product (GDP) growth and that professional macro forecasters do not fully incorporate the information contained in aggregate accounting earnings. Based on results from prior literature, which find that accounting earnings reflect bad economic news in a timelier manner than good news, we condition Konchitchki and Patatoukas’s GDP growth forecast model on the sign of earnings changes. We show that negative changes in aggregate earnings predict future GDP growth while positive changes in earnings do not. Furthermore, we show that professional macro forecasters underreact to the information contained in negative changes in aggregate earnings about future GDP growth. Additional tests suggest our findings are a result of conservative accruals in earnings.

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Notes

  1. See Fischer and Merton (1984) and Konchitchki and Patatoukas (2014a).

  2. GDP is also used internationally by governments, international investors, and various other bodies, such as Organisation of Economic Co-operation and Development,, the International Monetary Fund, and the World Bank.

  3. Abdalla and Carabias (2017) employ the level of aggregate special items, whereas we employ the change in aggregate special items in our test. Further, they do not control for common leading indicators of economic growth or test whether the association between aggregate special items and GDP growth forecast error is conditional on the sign of aggregate special item changes.

  4. The QFR is a survey of public and private corporations conducted quarterly by the Census Bureau and covers mining; manufacturing; wholesale trade; retail trade; information; and professional, scientific, and technical services. Aggregated shareholder reports sourced from Compustat drive the indicators for the following industries: construction; administrative and waste management services; educational services; health care and social assistance; arts, entertainment, and recreation; and other services.

  5. See Basu (1997); Kwon et al. (2001); Watts (2003a, 2003b); Ball and Shivakumar (2005); Kwon (2005); Lafond and Watts (2008); Roychowdhury and Watts (2007); Zhang (2008); Nikolaev (2010); Lawrence et al. (2013); Tan (2013); and Ahmed and Duellman (2013); among others.

  6. The notion that systematic news affects firms at the individual level is accepted in the finance literature and underpins the capital asset pricing model (Sharpe 1964; Lintner 1965; Fama and French 2004). The accounting literature links systematic news to firm-level accounting earnings. For instance, Beaver et al. (1970) develop a measure of accounting beta (i.e., the correlation between firm-level accounting earnings and market-level accounting earnings) and show that this measure is relevant to market-determined measures of firm risk. Konchitchki and Patatoukas’s finding that aggregate accounting earnings are helpful in forecasting future GDP growth implies that firm-level earnings change in a systematic way as a function of macroeconomic news (i.e., firm-level earnings changes are not entirely idiosyncratic).

  7. For instance, the value of a factory must be written down, regardless of whether its fair value drops below book value, because of lower aggregate demand (i.e., systematic news) or because of an expiring patent (i.e., idiosyncratic news).

  8. Accounting conservatism is not the only driver of asymmetric timeliness in earnings. For a discussion of how other possible drivers of asymmetric timeliness could affect our results, see Section 6.1.

  9. Following Konchitchki and Patatoukas (2014a), we omit the BEA’s estimate of corporate profits because it is included in the calculation of current period GDP growth, which is already included in the model. Therefore a significant coefficient on aggregate earnings should be interpreted as an incremental effect to that of the BEA’s estimate of corporate profits. Konchitchki and Patatoukas (2015) examine the basic model after controlling for the BEA’s estimate of corporate profits and find consistent results with those of Konchitchki and Patatoukas (2014a). After including BEA corporate profits and allowing it to vary asymmetrically we find similar results. See the web appendix.

  10. Throughout the paper, we follow Konchitchki and Patatoukas (2014a) and use OLS coefficient estimates with standard errors adjusted for both heteroskadasticity and autocorrelation, following Newey and West (1987). The lag length is set equal to three (N0.25, where N, the number of observations used in our regressions, equals 93); see Greene (2011) for details. However, our results are robust to using lag lengths varying from zero to four.

  11. We do not have a theoretical reason to expect a different intercept term when aggregate accounting earnings decline and thus make no prediction for the coefficient on the negative change indicator variable.

  12. Anillowski-Cain and McVay (2016) find that asset write-downs and restructuring comprise most Compustat special items. Specifically, the authors find that special items is comprised from 31%–73% write-downs and from 28%–46% restructuring charges, depending on the subsample examined.

  13. In the web appendix, we report an alternate specification employing I(ΔNIq < 0), rather than decomposing into I(ΔCIq < 0) and I(ΔSPIq < 0), and find similar results. In untabulated results, we find that the correlation between I(ΔNIq < 0) and I(ΔSPIq < 0) is 0.68.

  14. The index incorporates nonfarm payroll employment, the unemployment rate, average hours worked in manufacturing, wages and salaries, housing permits, unemployment insurance claims, manufacturing delivery times, and the interest rate spread. See https://fred.stlouisfed.org/series/USSLIND for details.

  15. RECPROB is provided by Federal Reserve Bank of St. Louis and is obtained from a dynamic-factor Markov-switching model. The model was originally developed by Chauvet (1998). Further detail can be found at the website: https://fred.stlouisfed.org/series/RECPROUSM156N.

  16. We find similar results when using an equal-weighted measure of aggregate earnings growth (see the web appendix), suggesting our results are not driven by the weighting system established by Konchitchki and Patatoukas (2014a).

  17. For details, see https://www.bea.gov/national/pdf/NIPAhandbookch1-4.pdf.

  18. 3.08 = 0.7408 / 0.2398.

  19. The incremental positive coefficient on negative aggregate earnings changes remains significant in explaining GDP growth up to two quarters ahead.

  20. 1.91 = 0.2590 / 0.1359.

  21. Several researchers question the Basu (1997) measure of conservatism, which is widely used in the literature. They question the measure on both theoretical (Schipper 2005; Guay and Verrecchia 2006) and econometric (Dietrich et al. 2007; Patatoukas and Thomas 2011; Lawrence et al. 2013; Patatoukas and Thomas 2016) grounds. Our empirical analysis does not use the Basu specification and is therefore not subject to criticisms of the measure.

  22. Poor contemporaneous operating performance could indicate that a loss has occurred and trigger an asset impairment. In this case, the full capitalized loss will impact accounting earnings, whereas the impact on operating performance will occur over several years.

  23. We use GDI, one of the three theoretical measures of GDP, because it directly incorporates corporate profits. As such, it provides the best sectioning of GDP to examine the direct and indirect explanations.

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Acknowledgments

We thank Kris Allee, John Campbell, Dan Bens, Yaniv Konchitchki, Stacie Laplante, Clive Lennox, Tom Linsmeier, Dan Lynch, Panos Patatoukas, Michael Minnis (2016 FARS discussant), Siew Hong Teoh, Terry Warfied, conference participants at the 2016 FARS mid-year meeting, and workshop participants at the University of Nebraska-Lincoln, Nanyang Technological University, and the University of Wisconsin-Madison for providing helpful comments.

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Authors and Affiliations

  1. University of Wisconsin-Madison, Madison, WI, USA

    Fabio B. Gaertner

  2. American University, Washington, DC, 20016, USA

    Asad Kausar

  3. Oregon State University, Corvallis, OR, 97331, USA

    Logan B. Steele

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  1. Fabio B. Gaertner
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  2. Asad Kausar
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Correspondence to Fabio B. Gaertner.

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Appendix

Appendix

Variable Definitions.

NI q

Aggregate quarterly net income [NIQ] deflated by sales [SALEQ]. Firm-level sales-deflated quarterly net income is value-weighted by beginning of quarter market value of equity obtained from the CRSP monthly stock file [prc x shrout].

ΔNIq:

The year-over-year change in value-weighted sales-scaled quarterly earnings.

I(ΔNIq < 0)

An indicator variable that takes on a value of 1 when ΔNIq is negative and 0 otherwise.

CI q

Aggregate quarterly core income, which equals net income [NIQ] before special items [SPIQ] deflated by sales [SALEQ]. Firm-level sales-deflated core income is value-weighted by beginning of quarter market value of equity obtained from the CRSP monthly stock file [prc x shrout].

ΔCI q

The year-over-year change in value-weighted sales-scaled quarterly core income.

SPI q

Aggregate quarterly special items [SPIQ] deflated by sales [SALEQ]. Firm-level sales-deflated quarterly special items are value-weighted by beginning of quarter market value of equity obtained from the CRSP monthly stock file [prc x shrout].

ΔSPI q

The year-over-year change in value-weighted sales-scaled quarterly special items.

G q

The advance estimate of nominal GDP growth for quarter q, obtained from the real-time data set for macroeconomists available from the Federal Reserve Bank of Philadelphia.

G q + 1

The final estimate of nominal GDP growth for quarter q + 1, obtained from the real-time data set for macroeconomists available from the Federal Reserve Bank of Philadelphia.

E q (G q + 1 )

The mean consensus Society of Professional Forecasters’ forecast of nominal GDP growth for quarter q + 1, which is available from the Federal Reserve Bank of Philadelphia.

YIELD q

The yield on the one-year constant-maturity Treasury bill (T-bill) measured one month after quarter q ends. This item is obtained from the Federal Reserve Board’s H15 Report.

SPREAD q

The yield on the 10-year constant-maturity Treasury bond minus the yield on the one-year constant-maturity T-bill measured one month after quarter q ends. This item is obtained from the Federal Reserve Board’s H15 Report.

RETURN q

The quarterly buy-and-hold stock market return are from the CRSP Monthly Index File.

ΔHSTART q

The quarterly change in housing starts. Housing starts data is downloaded from Real-Time Data Research Center, Federal Reserve Bank of Philadelphia.

ΔCPI q

The quarterly change in consumer price index (CPI). CPI is downloaded from Federal Reserve Bank of St. Louis’s website.

ΔCCI q

The quarterly change in consumer confidence index. The consumer confidence index (CCI) for the United States is based on households’ plans for major purchases and their economic situation, both currently and their expectations for the immediate future (https://data.oecd.org/leadind/consumer-confidence-index-cci.htm).

ΔUNRATE q

The quarterly change in unemployment rate. Unemployment data is obtained from Federal Reserve Bank of St. Louis.

IPG q

The quarterly growth in industrial production is obtained from Real-Time Data Research Center, Federal Reserve Bank of Philadelphia.

ΔLI q

The quarterly change in leading index. Leading index for the United States is downloaded from Federal Reserve Bank of St. Louis’s website.

FEDFUNDS q

The federal funds rate is obtained from the Federal Reserve Board’s H15 Report.

RECPROB q

The probability of a U.S. recession is downloaded from the Federal Reserve Bank of St. Louis’s website.

GDIq

The quarterly change in the wages and salaries (GDI) component of Gross Domestic Income.

WSDq

The quarterly change in the wages and salaries disbursements (WSD) component of Nominal Personal Income.

EMPLOYq

The quarterly change in the total number of employees of nonagricultural payrolls (EMPLOY).

HOURSq

The quarterly change in the Bureau of Labor Statistics’s index of aggregate weekly hours (HOURS).

  1. All variables in brackets are obtained from Compustat unless otherwise noted

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Gaertner, F.B., Kausar, A. & Steele, L.B. Negative accounting earnings and gross domestic product. Rev Account Stud 25, 1382–1409 (2020). https://doi.org/10.1007/s11142-020-09536-x

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