Extracting Sustainable Earnings from Profit Margins

Abstract

Revenues and expenses are fundamentally proportional to one another, but are likely to be disproportionally affected by transitory items or economic shocks. We build on this observation and propose a new measure of sustainable earnings based on deviations from normal profit margins. While some other sustainable earnings metrics attempt to identify transitory components on a line-by-line basis, our measure, referred to as the intensity of core earnings (ICE), uses ratio analysis to extract the transitory portion of earnings from all line items. We find that the ICE, as measured here, is positively associated with earnings persistence, better earnings predictability, and stronger market reaction to unexpected earnings. We also find that our measure is positively associated with post-earnings announcement excess stock returns. Comparing our measure with an accrual-based measure of earnings quality, we find that, in general, the two metrics provide distinct incremental information relative to one another and in some instances our measure is better than an accrual-based measure in assessing earnings quality.

Acknowledgements

We thank Joshua Livnat, Doron Nissim, Terrance Skantz, Florin Vasvari, Amir Ziv, and seminar participants in the 2008 Tel Aviv International Accounting Conference, the 2009 American Accounting Association Annual Meetings, INSEAD, the University of New South Wales (Sydney), the University of Melbourne, the University of Queensland (Brisbane), and the Stockholm School of Economics for many useful comments. Eli Amir is grateful to London Business School for research funding while he was a faculty member there. Eti Einhorn and Itay Kama are grateful to the Henry Crown Institute of Business Research in Israel at Tel Aviv University for financial support.

Notes

¹ Dechow et al. (2010) define earnings quality as follows: ‘Higher quality earnings provide more information about the features of a firm's financial performance that are relevant to a specific decision made by a specific decision-maker’.

² See, for example, Lipe (1986), Wilson (1987), Barth et al. (1992), Ohlson and Penman (1992), Sloan (1996), Ramakrishnan and Thomas (1998), Fairfield and Yohn (2001), Ertimur et al. (2003), Jegadeesh and Livnat (2006), and Kama (2009). Another measure of earnings quality from the perspective of earnings management is the magnitude of discretionary accruals (for example, Jones, 1991; Dechow et al., 1995; Kothari et al., 2005).

³ Fairfield et al. (2009) argue that while industry analysis yields only marginal incremental information over firm-specific figures in forecasting return on net operating assets (RNOA), return on common equity (ROCE), and growth in net operating assets (NOA), it is useful in predicting future sales growth.

⁴ We repeated the analyses using the industry classification suggested by Kenneth French. Results (not tabulated) are very similar. See http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.

⁵ Consistent with Gu and Wu (2003) and Weiss (2010) we require in this analysis that the stock price be at least $3 to avoid the small deflator problem. We replicate our analysis using all firms with a stock price over $1, obtaining virtually the same results (not tabulated).

⁶ The results (not tabulated) are not sensitive to adding accruals as an additional control variable or omitted the coefficient of variation from the model.

⁷ Here, we do not examine the effect of core intensity based on gross profit [INT(GP)], because, as mentioned in Sections 3 and 4.1, the INT(GP) is quite stable (and relatively high) over time and within industry.

⁸ We also examined the industry composition of each ICE quintile using the industry classification suggested by Kenneth French. We find that the proportion of computer, software, and electronic equipment (high R&D) firms decreases as we proceed up the intensity quintiles. In contrast, the proportion of consumer nondurable, wholesale, retail, and service (low R&D) firms increases monotonically as we proceed up the intensity quintiles. These findings are consistent with Amir et al. (2003), as earnings forecasts are less accurate (more dispersed) in high R&D industries.

⁹ We repeated the analysis in Table 5 using actual earnings as a deflator instead of the beginning-of-quarter stock price obtaining similar results (not tabulated). Also, in measuring analysts' dispersion we limit our sample to firm/quarter observations with a minimum of three different forecasts. Limiting the dispersion analysis to firm/quarter observations with a minimum of two different analysts' earnings forecasts does not change the results qualitatively nor does limiting the analysis of analysts' accuracy and analysts' bias to a minimum of two or three analysts' earnings forecasts. In addition, we repeated the analysis of analysts' accuracy, dispersion, and bias using a sub-sample of firms that report positive earnings. Results (not tabulated, for brevity) are qualitatively the same.

¹⁰ We replicate the analysis of contemporaneous market reaction using standardized unexpected earnings (SUE), and standardized unexpected revenues (SURG) instead of analysts' forecasts error. Results (not tabulated) regarding the effect of the ICE on market reaction to unexpected earnings are qualitatively the same. We also repeated the industry-based analysis using IINT(NI) in quarter t − 4 obtaining similar results.

¹¹ When the intensity of core net income is based on firm-specific profit margins (specifications 3 and 5), the coefficients are positive at the 0.05 level for the PREFILE window (specification 3) and at the 0.10 level for the POSTFILE window (specification 5). This evidence suggests that firms with above-median firm-specific intensity of core net income have stronger drifts, regardless of the magnitude of unexpected earnings.

¹² The Spearman correlations between cash-based intensity measures and income-based intensity measures range from 0.12 to 0.37. The Spearman correlations between and range from 0.09 to 0.29.