Abstract
This paper examines the effect of two Securities and Exchange Commission regulatory interventions related to disclosure of non-GAAP financial measures. There are three main results. First, the probability of disclosure of non-GAAP earnings declines in 2003, but the probability of disclosure of other non-GAAP financial measures has an accelerated decline after the first intervention. Second, all else equal, after Regulation G, investors have a positive market reaction to the disclosure of non-GAAP earnings. Finally, investors react to the adjustments made by I/B/E/S financial analysts as they do to the GAAP surprise, but they do not react to the additional adjustments made by firms.
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Notes
Throughout this paper I will use the term non-GAAP financial measures instead of pro forma, as I am aware that historically pro forma earnings represent earnings under the assumption that two merging (or divesting) companies have been merged (or divested) in prior years, and that article 11 of Regulation S-X states “pro forma financial information should provide investors with information about the continuing impact of a particular transaction by showing how it might have affected historical financial statements if the transaction had been consummated at an earlier date.”
This last result is consistent with the findings of Gu and Chen (2004), where the difference between the items excluded by First Call analysts and the items that are included is studied.
In 1973, the SEC issued Accounting Series Release No. 142, warning of possible investor confusion from the use of financial measures outside of GAAP.
As an additional regulatory action, on June 13, 2003, staff members in the division of Corporation Finance of the SEC published responses to frequently asked questions regarding the use of non-GAAP financial measures.
I include the ninth calendar quarter in regime 3, as my results indicate that Regulation G starts having effect as soon as it is published, and not just after it becomes effective. I realize that, in reality, the division into regimes is not based on “bright lines,” and that the increase in regulation is somewhat evolutionary. However, on average, successive regimes featured stronger regulation than previous regimes.
Bradshaw and Sloan (2002) and Brown and Sivakumar (2003) use the numbers disclosed by Thomson Financial I/B/E/S as a proxy for the non-GAAP earnings, while Bhattacharya et al. (2003) use the actual non-GAAP numbers disclosed in a sample of press releases gathered from Lexis/Nexis. However, as Bradshaw (2003) points out, Bhattacharya et al. (2003) use a “Compustat-defined measure of GAAP operating earnings, so what is referred to as GAAP is actually another pro forma earnings number.” Brown and Sivakumar (2003) use three procedures to study the value relevance of non-GAAP financial measures: (1) ability to predict future earnings [predictive ability], (2) association of earnings levels with stock price levels [valuation], and (3) correlation of earnings surprises (measured by forecast error) with abnormal stock returns [information content]. This last procedure was used by the authors with both a long window (as in Bradshaw & Sloan, 2002) and a short window (as in Lougee & Marquardt, 2004 and Bhattacharya et al., 2003).
The authors use the term street earnings when referring to non-GAAP earnings.
This is the first paper of which I am aware that allows for this type of comparison. Although there are now several papers other than Bhattacharya et al. (2005a) based on hand-collected non-GAAP financial measures their samples are always built by the authors through searches performed on databases (such as Lexis/Nexis and Dow Jones) for specific words and/or phrases. This results in a sample that only includes pro forma firms. The paper that has a sample closest to mine is Entwistle, Feltham, and Soliman (2005), which analyzes year-end earnings press releases from the S&P500 and the TSX S&P300 (from Canada) for a year.
Wall Street Journal, August 21, 2001.
I use GICS instead of the traditional standard industrial classification codes (SIC) because Bhojraj et al. (2003) show that they are significantly better in various settings of capital market research.
Firms were excluded by reason of merger only if they made it clear in the press release that the business arrangement altered their operations significantly.
Schrand and Walther (2000) mention that PR Newswire and Business Wire edit press releases only for grammar (such as commas, decimal points and AP style) and verify any changes with the firm.
I include in this category EBIT_DA (earnings before interest, taxes, depreciation and amortization), adjusted EBITDA, EBIT (earnings before interest and taxes) and adjusted EBIT.
The value of intangibles is obtained by adding Compustat quarterly items 234 and 235. Whenever an observation is missing this data, I assume that the value of intangibles is zero. The rules for goodwill amortization changed over the time period I analyze, and that may also affect the results. In my sample, in 2001 there are 76 observations where one of the adjustments made was goodwill amortization, in 2002 this number declines to 56 observations, and in 2003 there are only 17.
As in Brown and Sivakumar (2003) I use the last mean consensus estimate in the I/B/E/S summary file prior to the quarterly earnings announcement as a proxy for expected EPS.
Outliers were identified using the cut-off point of 2p/n for the hat matrix values (following Belsley, Kuh, and Welsch, 2004). This resulted in the elimination of 216 observations (5% of initial sample) in the NGE estimation and 84 observations (2% of initial sample) in the ONG estimation. The initial sample for this analysis consisted of 4,095 observations, as 139 observations did not have all the data necessary for this model.
INC_B_ADJ equals non-GAAP EPS if such a measure is disclosed in the press release (either on a per share basis or in the aggregated form). If no non-GAAP earnings are disclosed, INC_B_ADJ is equal to NI_PS.
I do not include QRT_4 in this equation, as this analysis is made by regimes, not calendar quarters.
Following Belsley et al. (2004) I removed the observations where either Rstudent or Dfitts have an absolute value in excess of 2. This resulted in the elimination of 208 observations (6% of initial sample) in the short window estimation and 198 observations (5% of initial sample) in the long window estimation. The initial sample for this analysis consisted of 3,637 observations, as 597 observations did not have all the data necessary for this model.
The values calculated in this example have not yet been deflated (to keep calculations simple).
Following Belsley et al. (2004) I removed the observations where either Rstudent or Dfitts have an absolute value in excess of 2. This resulted in the elimination of 208 observations (6% of initial sample) in the short window estimation and 199 observations (5% of initial sample) in the long window estimation. The initial sample for this analysis consisted of 3,637 observations, as 597 observations did not have all the data necessary for this model.
These tests are made in pairs. Thus, for every set of coefficients I perform 3 tests.
However, tests on the estimated coefficients show that an equality only exists in regime 3.
Although the way the market reacts to the adjustments made by I/B/E/S is consistent through time, this does not guarantee investors are assessing the permanence of these items correctly. In a sample from 1988 to 1999, Doyle et al. (2003) find significant mispricing of this type of exclusions (especially when the excluded items are not special items). Furthermore, the same paper concludes that the market rewards positive earnings surprises, but that “the reward is diminished if the surprise is achieved by the use of exclusions in the definition of pro forma earnings.” My results contradict this last part of their findings. I believe this is due to the difference in the time periods our samples cover; with more information about the excluded items and more discussion about non-GAAP measures in the press, investors can now understand these numbers better. Another possible cause is differences in the sample (since I restrict my study to S&P 500 firms).
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Acknowledgments
This paper is based on my dissertation completed at the University of Texas at Austin. I would like to thank the members of my committee for their helpful comments and guidance: Ross Jennings (chair), Keith Brown, Robert Freeman, Thomas Sager and Senyo Tse. This paper has also benefited from the comments of one anonymous reviewer, Russell Lundholm (the editor), Romana Autrey, Jennifer Brown, Ted Christensen, Steve Kachelmeier, William Mayew and the workshop participants at the University of Texas at Austin. I gratefully acknowledge the financial support of the Foundation for Science and Technology (Portugal) and the European Social Fund.
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Marques, A. SEC interventions and the frequency and usefulness of non-GAAP financial measures. Rev Acc Stud 11, 549–574 (2006). https://doi.org/10.1007/s11142-006-9016-x
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DOI: https://doi.org/10.1007/s11142-006-9016-x