nach oben

Tipp

Tilting the evidence: the role of firm-level earnings attributes in the relation between aggregated earnings and gross domestic product

verfasst von: Ryan T. Ball, Lindsey Gallo, Eric Ghysels

Erschienen in: Review of Accounting Studies | Ausgabe 2/2019

Einloggen

Abstract

We examine whether the contribution of firm-level accounting earnings to the informativeness of the aggregate is tilted towards earnings with specific financial reporting characteristics. Specifically, we investigate whether considering the smoothness of firm-level earnings increases the informativeness of aggregate earnings for future real GDP, and if so, whether macroeconomic forecasters use this information efficiently. Using recently-developed mixed data sampling methods, we find that the aggregate is tilted towards firms with smoother earnings and that this composition of aggregate earnings outperforms traditional weighting schemes. Further, this tilted aggregate has a stronger positive association with forecast revisions; in fact, analysts who utilize earnings the most in their forecasts appear to fully impound the informativeness of earnings smoothness. Our results synthesize and span parallel yet distinct streams of research on the role of accounting earnings in firm-level and macroeconomic outcomes and suggest an important role for financial reporting characteristics in the aggregate.

Vorheriger Artikel Long-term economic consequences of hedge fund activist interventions

Nächster Artikel Does a change in dividend tax rates in the U.S. affect equity prices of non-U.S. stocks?

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

Smoothness is often considered a measure of a firm’s earnings quality in other contexts. In this study, we do not take a position on whether smoother earnings reflects higher or lower earnings quality. Instead, our interest lies in simply understanding whether and to what extent smoothness influences the differential informativeness of firm-level earnings for aggregate outcomes.

Although we focus on smoothness, the goal of this study is to measure, test, and report whether financial reporting characteristics can survive aggregation to enhance the aggregate informativeness of earnings, rather than attempting to identify the best financial reporting characteristic from a menu of options. We leave examinations of other firm-level reporting attributes to future research for which our empirical framework provides guidance.

Mixed data sampling models, developed by Ghysels et al. (2006), have been applied almost exclusively to temporal differences in the frequency of observation among variables, such as quarterly versus daily (e.g., Ball and Gallo 2018; Ball and Ghysels 2018). In our setting, mixed data sampling refers to cross-sectional differences in the frequency of observations because thousands of firm-level quarterly earnings are observed in the cross section for every one quarterly observation of real GDP. Along with Anderson et al. (2009) and Ball et al. (2018), our study is among the first to apply mixed data sampling methods in the cross-section.

We incorporate both equal and value weighting into our aggregation benchmark, as these are the two commonly used weighting schemes in the aggregate earnings literature.

For example, an equal-weighted aggregate treats each dollar of (scaled) earnings the same (i.e., applies the same coefficient). In this study, we allow each firm’s contribution to the aggregate to tilt away from the benchmark aggregate earnings as a function of firm-specific characteristics, which allows the coefficient to vary across firms in accordance with each firm’s earnings smoothness parameter.

Finding a significant tilt towards firms with smoother earnings in the association between aggregate earnings and future GDP does suggest that smoothness, on average, reflects either an attribute of the fundamental earnings process or management’s communication of relevant private information, as opposed to nefarious earnings manipulation (which would not be expected to have a stronger relationship with real economic outcomes).

We do not assert whether forecasters explicitly use earnings smoothness in constructing their forecasts. Smoothness may be associated with some other characteristic that makes these firms more relevant for their forecasts. Nevertheless, our findings do suggest that discriminating by earnings smoothing is a potentially fruitful approach.

Specifically, corporate profits are measured primarily using tax data, which are collected with a significant lag and then extrapolated to estimate corporate profits. In addition to tax returns, the BEA uses surveys as well as some financial statement information to measure corporate profits. This number is then revised over time.

See Dechow et al. (2010) for an overview of the earnings quality literature.

We choose market value of assets as our scaling variable in an effort to abstract away from the firm’s financing choice. Scaling by sales or market value of equity—two common approaches in the aggregate earnings literature—yields qualitatively similar results.

For example, firms with a fiscal quarter ending in January, February, or March of 2014 would all be included in our aggregate of 2014 first quarter earnings, so long as the earnings are announced before the end of April 2014.

Konchitchki and Patatoukas (2014a, pg. 80) state that “according to Thomas Stark, Assistant Director and Manager of the Real-Time Data Research Center, ‘almost no one uses the advance estimate’ when measuring GDP growth forecast errors.”

The Philadelphia Fed sends the questionnaire to its panel of forecasters at the end of the first month of the calendar quarter, after the advance release of GDP. The deadline for responses is set for the second or third week of the middle month of the calendar quarter. The results are always released before the second report is released by the BEA at the end of the middle month of the quarter. For example, in the first quarter, the questionnaire is sent to panelists at the end of January and is due back in the middle of February, with results released in mid- to late-February. In our sample, the average number of forecasters is 36.

In our setting, we observe firm-level quarterly earnings growth for hundreds of firms every calendar quarter for every one quarterly observation of real GDP growth.

Using a predetermined representative agent utility function, Brandt et al. (2009) formulate approximated portfolio allocations, using a similar weighting scheme. Their analysis does not involve the estimation of regression models with the purpose of aggregating cross-sectional data, as we do in our analysis.

For example, the benchmark component is equivalent to applying an equal weight to all observations if the estimated value of 𝜃 = 0. Conversely, the benchmark component is equivalent to applying a value weight to all observations if the estimated value of 𝜃 = 1. We allow 𝜃 to take on a range of values, which increases the flexibility of the benchmark component.

Technically speaking, the estimated smoothness-weighted aggregate earnings presumably reduces the measurement error that results from imposing an equal weight when constructing aggregate earnings, which ultimately biases the coefficient β_EARN.

Anderson, E., Ghysels, E., Juergens, J. (2009). The impact of risk and uncertainty on expected returns. Journal of Financial Economics, 94(2), 233–263. CrossRef

Ball, R., & Sadka, G. (2015). Aggregate earnings and why they matter. Journal of Accounting Literature, 34, 39–57. CrossRef

Ball, R.T., Bonham, J., Hemmer, T. (2018). Does it pay to “Be Like Mike”? Aspirational peer firms and relative performance evaluation. Working paper, University of Michigan, University of Chicago, and Rice University.

Ball, R.T., & Gallo, L. (2018). A mixed data sampling approach to accounting research, Working paper, University of Michigan.

Ball, R.T., & Ghysels, E. (2018). Automated earnings forecasts: beat analysts or combine and conquer? Management Science, 64(10), 4936–4952. CrossRef

Bonsall, S., Bozanic, Z., Fischer, P. (2013). What do management earnings forecasts convey about the macroeconomy? Journal of Accounting Research, 51(2), 225–266. CrossRef

Brandt, M., Santa-Clara, P., Valkanov, R. (2009). Parametric portfolio policies: exploiting characteristics in the cross-section of equity returns. Review of Financial Studies, 22(9), 3411–3447. CrossRef

Crawley, M. (2014). Macroeconomic consequences of accounting: the effect of accounting conservatism on macroeconomic indicators and the money supply. The Accounting Review, 90(3), 987–1011. CrossRef

Dechow, P. (1994). Accounting earnings and cash flows as measure of firm performance: the role of accounting accruals. Journal of Accounting and Economics, 18(1), 3–42. CrossRef

Dechow, P., Ge, W., Schrand, C. (2010). Understanding earnings quality: a review of the proxies, their determinants and their consequences. Journal of Accounting and Economics, 50(2–3), 344–401. CrossRef

DeFond, M., & Park, C. (1997). Smoothing income in anticipation of future earnings. Journal of Accounting and Economics, 23(2), 115–139. CrossRef

Demerjian, P., Lewis-Western, M., McVay, S. (2017). How does intentional earnings smoothing vary with managerial ability? Journal of Accounting, Auditing and Finance. https://doi.org/10.1177/0148558X17748405.

Dichev, I. (2018). Comparing GAAP to NIPA earnings. Working paper, Emory University.

Gallo, L., Hann, R., Li, C. (2016). Aggregate earnings surprises, monetary policy, and stock returns. Journal of Accounting and Economics, 62(1), 103–120. CrossRef

Ghysels, E., Santa-Clara, P., Valkanov, R. (2006). Predicting volatility: getting the most out of return data sampled at different frequencies. Journal of Econometrics, 131(1–2), 59–95. CrossRef

Konchitchki, Y., & Patatoukas, P. (2014a). Accounting earnings and gross domestic product. Journal of Accounting and Economics, 57(1), 76–88. CrossRef

Konchitchki, Y., & Patatoukas, P. (2014b). Taking the pulse of the real economy using financial statement analysis: implications for macro forecasting and stock valuation. The Accounting Review, 89(2), 669–694. CrossRef

Kothari, S.P. (2001). Capital markets research in accounting. Journal of Accounting and Economics, 31(1–3), 105–231. CrossRef

Kothari, S.P., Lewellen, J., Warner, J. (2006). Stock returns, aggregate earnings surprises, and behavioral finance. Journal of Financial Economics, 79(3), 537–568. CrossRef

Kothari, S.P., Lewellen, J., Warner, J. (2014). The behavior of aggregate corporate investment. Working paper, MIT, Dartmouth College, and University of Rochester.

Laurion, H., & Patatoukas, P. (2016). From micro to macro: does conditional conservatism aggregate up in the national income and product accounts? Journal of Financial Reporting, 1(2), 21–45. CrossRef

Leuz, C., Nanda, D., Wysocki, P. (2003). Earnings management and investor protection: an international comparison. Journal of Financial Economics, 69 (3), 505–527. CrossRef

Nallareddy, S., & Ogneva, M. (2016). Predicting restatements in macroeconomic indicators using accounting information. The Accounting Review, 92(2), 151–182. CrossRef

Newey, W., & West, K. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703–708. CrossRef

Shivakumar, L., & Urcan, O. (2017). Why does aggregate earnings growth reflect information about future inflation? The Accounting Review, 92(6), 247–276. CrossRef

Subramanyam, K. (1996). The pricing of discretionary accruals. Journal of Accounting and Economics, 22(1–3), 249–281. CrossRef

Tucker, J., & Zarowin, P. (2006). Does income smoothing improve earnings informativeness? The Accounting Review, 81(1), 251–270. CrossRef

Titel: Tilting the evidence: the role of firm-level earnings attributes in the relation between aggregated earnings and gross domestic product
verfasst von: Ryan T. Ball
Lindsey Gallo
Eric Ghysels
Publikationsdatum: 04.03.2019
Verlag: Springer US
Erschienen in: Review of Accounting Studies / Ausgabe 2/2019
Print ISSN: 1380-6653
Elektronische ISSN: 1573-7136
DOI: https://doi.org/10.1007/s11142-019-09493-0

Springer Professional

Tipp

Weitere Artikel dieser Ausgabe durch Wischen aufrufen

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 2/2019

A contextual analysis of the impact of managerial expectations on asymmetric cost behavior

Regulatory oversight and trade-offs in earnings management: evidence from pension accounting

Dividends from unrealized earnings and default risk

Using IRS data to identify income shifting to foreign affiliates

Long-term economic consequences of hedge fund activist interventions

The implication of unrecognized asset value on the relation between market valuation and debt valuation adjustment