Core Earnings: New Data and Evidence

Ethan Rouen is an Assistant Professor of Business Administration at Harvard Business School; Eric C. So is the Sloan Distinguished Professor of Management at the MIT Sloan School of Management; and Charles C.Y. Wang is the Glenn and Mary Jane Creamer Associate Professor of Business Administration at Harvard Business School. This post is based on their recent article, forthcoming in the Journal of Financial Economics.

Financial statements contain a wealth of information about a firm’s net income, an estimate of the net value flow during a period. Investors commonly seek to distinguish the component of earnings that stems from a firm’s central business activities (“core earnings”) from those components that result from ancillary business activities or transitory shocks. This exercise is essential for interpreting and forecasting firm performance.

The behavior of sell-side analysts and managers attests to the importance of distinguishing core and non-core earnings. Analysts regularly report and forecast firms’ earnings on a non-GAAP basis (“street earnings”) by excluding from GAAP earnings items deemed transitory or not reflective of the central business activities. Similarly, managers commonly report non-GAAP “pro forma” earnings that exclude items they consider unimportant for understanding firm performance. A concern with these metrics is that managers and analysts choose in a biased fashion which items to include or exclude. For example, pro forma earnings often exclude stock-based compensation expenses, which result from central business activities and are recurring. Excluding these measures help to paint a rosier picture of firm performance.

In Core Earnings: New Data and Evidence, forthcoming in the December issue of the Journal of Financial Economics, we study the significance and properties of core and non-core earnings disclosures for the cross-section of public firms. Comprehensively identifying revenues, expenses, gains, or losses stemming from transitory shocks or ancillary business activities is practically challenging. Doing so for one firm requires identifying and categorizing items disclosed in the footnotes, management discussion and analysis (MD&A), and cash flow statement sections of firms’ 10-Ks, which have grown in length (often exceeding 200 pages) and complexity. Moreover, correctly categorizing items as core versus non-core is highly context-specific (i.e., it depends on a firm’s primary business) and requires judgment. These activities are therefore challenging to scale in the cross-section and over time.

We address these challenges by leveraging data compiled by New Constructs (NC), a financial research firm that identifies and classifies all income-statement-related quantitative disclosures appearing in the 10-K. The data are available for a large sample, covering more than 60,000 firm-year observations from 1998 to 2017. Moreover, NC collects detailed attributes of each quantitative disclosure, allowing us to observe the frequencies and amounts of non-core earnings items based on their (i) location of disclosure within the 10-K (e.g., on the face of the income statement; or in the footnotes, MD&A, or cash flow statement and then aggregated into an income-statement line item); (ii) direction of impact on core earnings (e.g., income-increasing or income-decreasing); and (iii) economic category (e.g., acquisition- or restructuring-related).

To measure core earnings, we exclude from GAAP earnings the items NC deems to have resulted from transitory shocks or ancillary business activities. Specifically, our measure (Core Earnings) adds back to GAAP earnings (Net Income) net expenses stemming from (1) acquisitions, (2) currency fluctuations, (3) discontinued operations, (4) legal or regulatory events, (5) pension plans, (6) restructuring, (7) gains and losses that companies label as “other” as a separate line item on the income statement, and (8) other gains and losses that NC analysts classify as transitory or ancillary to the central activities. Because an independent research firm produces the underlying data, our measure’s key appeal is that the classification of earnings components is less likely to exhibit systematic biases found in street earnings or pro-forma earnings.

Using this dataset, we first document the frequency, magnitude, disclosure location, and time trend of the adjustments that reconcile Net Income and Core Earnings. We find that all firms disclose non-core earnings items at some point, and they are significant in terms of frequency and magnitude. There are many such disclosures in a 10-K, and increasingly so over the last 20 years: from 1998 to 2017, the average number of non-core earnings items identified in a 10-K rose more than 30%, from six to eight. The average total non-core earnings amounts to 19% of average Net Income. Moreover, disclosures of non-core earnings items are dispersed throughout the 10-K, both on and off of the face of the income statement (e.g., in footnotes, MD&A, or cash flow statement). Roughly half of these items, by frequency or magnitude, are disclosed off of the income statement. These findings suggest that individuals seeking to understand the composition of GAAP earnings need to process a large amount of information disclosed in various parts of the 10-K.

Next, we examine the forecasting properties of Core Earnings. Consistent with its definition, we show that Core Earnings satisfies two key properties: (i) it exhibits a high level of year-over-year persistence, in particular higher than Net Income (due to the removal of transitory components of GAAP earnings); and (ii) its adjustments of Net Income (Total Adjustments) exhibit a relatively low degree of year-over-year persistence. Core Earnings also is effective at distinguishing these components of earnings over a five-year horizon. Furthermore, Core Earnings predicts firms’ future Net Income as well as other measures of operating performance over a one- to five-year horizon.

Finally, we study whether and to what extent market participants understand the implications of the non-core components of Net Income. Our evidence suggests that market participants are slow to consider the implications of non-core earnings: Total Adjustments positively forecasts revisions in analysts’ earnings forecasts in the 12 months following a firm’s 10-K filing. Similarly, Total Adjustments significantly forecasts firms’ stock returns in the 12 months following their 10-K filing. In both cases, the economic and statistical significance of these findings are largest for adjustments found in the footnotes, where information tends to be less salient to core operations and less structured. These findings are consistent with analysts and market participants relying on a subset of adjustments that require lower processing costs in making initial earnings forecasts, but gradually updating forecasts over time.

Together, our findings highlight the importance of financial statement analysis and of adjusting GAAP earnings to account for non-core earnings items for forecasting and valuation. In particular, our findings underscore the usefulness of the information disclosed off of the income statement (in footnotes, MD&A, or cash flow statement sections of 10-Ks) for understanding the nuances in firm performance.

The complete paper is available here.

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