INSIGHTS: The Corporate & Securities Law Advisor (Volume 35, Number 7, July 2021) is published monthly by Wolters Kluwer. © 2021 CCH Incorporated and its affiliates. All rights reserved.
Recent commentary by Diana Tani, an Assistant Regional Director with the Securities and Exchange Commission (SEC), at the “Securities Enforcement West” conference emphasized the agency’s focus on data-driven investigations. Two areas targeted for recent enforcement are (1) manipulations to achieve earnings per share (EPS) estimates, and (2) undisclosed executive compensation, especially as related to executives’ usage of corporate aircraft.
Recent enforcement actions suggest that the SEC is targeting quarterly public filings. Quarterly reports are not subject to the same accounting oversight as annual reports. Recent cases highlighted by the agency as examples of data-based enforcement actions suggest that the SEC’s focus with data-based investigations is on quarterly reports and other areas where manipulation may be more likely to occur.
The SEC’s use of advanced analytics tools is not new for the agency. It is simply the latest iteration of a longstanding push to stay up to date with advancing technology. Before the focus on EPS and corporate perquisites, the SEC used data analytics tools to identify abusive trading and insider trading based on real-time analysis of trading data.
These efforts trace back to 2011, when the SEC created the Analysis & Detection Center under the Market Abuse Unit in the Enforcement Division. In a 2016 address, then-Chair Mary Jo White emphasized the need for the SEC to use in-house data analysis to originate cases, lessening the agency’s reliance on outside tips and whistleblowers for information.
The SEC’s expanded use of data analytics in the realm of company filings, while more recent, still has deep roots. In 2018, the Wall Street Journal reported on findings from an academic paper which revealed an abnormally low rate of reported EPS figures with a “4” in the tenths place. The article suggested that companies would have a strong incentive to increase reported EPS figures, because a small change would be amplified due to the effects of rounding. The Journal reported that the paper was widely read within the SEC’s Enforcement Division. The fact that the SEC is now announcing enforcement priorities related to EPS estimates suggests the continued relevancy of this paper to the SEC and indicates the agency’s continued investment in data analysis in new enforcement areas.
Reading between the lines of the press releases surrounding recent enforcement actions and other SEC commentary, it appears that the targets for these new enforcement initiatives are relatively small manipulations to figures that can have an alleged outsized effect by causing a company to meet analysts’ EPS expectations or attain other quarterly results that, while not necessarily material in and of themselves, can have a significant impact on analyst and investor expectations or outlooks.
Enforcement Action Focuses on EPS Manipulation
A recent enforcement action highlights the focus on EPS manipulation at the SEC and the use of data analytics. In an action against Fulton Financial Corporation, the SEC discovered that Fulton had altered its valuation methodology for certain mortgage servicing rights to lower its EPS to meet analyst predictions in several quarters, followed by a reversal of this methodology which also resulted in a nearly exact match with EPS targets. The SEC found that “Fulton’s disclosures created the misleading appearance of consistent earnings across multiple reporting periods.” The SEC credited its “internal data analysis tools” with uncovering the false reporting.
The takeaway from the SEC’s action is that a company’s recurring pattern of meeting or barely exceeding EPS estimates may cause the SEC to inquire further into a company’s financials, especially if the performance appears to be driven by a single category that may register as an outlier against a company’s previous filings. This, in turn, suggests that the SEC’s new data analytics tools are targeting previously hard-to-detect violations—the discovery of which, until the emergence of these tools—would have required the allocation of significant resources.
The SEC’s increased use of advanced data analysis likely will spill over into other enforcement areas. Companies should ensure that any accounting or disclosure changes that significantly affect performance are documented, justified, and comply with all relevant standards. The SEC’s recent comments, enforcement actions, and the research underlying the new efforts all tend to suggest that small but intentional manipulations are prone to enhanced scrutiny.
Inadequate Disclosure of Executive Compensation
In her remarks, Ms. Tani also highlighted cases where the SEC brought actions against companies for failing to adequately disclose executive compensation and perquisites. Another panel participant noted that, in the three recent cases mentioned by Ms. Tani, all involved the use of corporate aircraft and failures by the companies to adequately account for the use of the aircraft as an element of compensation.
In one case, the SEC’s order noted that the calculation of the value of corporate aircraft use for tax purposes is not the same as the calculation of the value of corporate aircraft use for executive compensation purposes. While Ms. Tani’s remarks do not specify exactly what data the SEC is mining to evaluate potential investigations, one recent enforcement action suggests that the SEC may be focusing on similar patterns that drive its EPS initiatives.
In the case of Hilton Worldwide Holdings, Inc., the SEC charged Hilton with disclosure violations related to corporate perquisites that the company understated in its proxy statements, which then were incorporated into its annual filings. The SEC found that Hilton had improperly categorized certain perquisites as business expenses that did not require disclosure.
In the press release accompanying the settled charges, the SEC noted that ”[t]he action was generated by the Division of Enforcement’s use of risk-based data analytics to uncover potential violations related to corporate perquisites.” Comparing the order with the press release, it appears likely that SEC data analysis revealed abnormally low corporate perquisite compensation figures for senior executives.
These triggered a request for further information. It is unclear whether the SEC was comparing Hilton’s reported figures to either previous filings, other similar companies, or both. But the implication is that Hilton’s figures were “outliers” compared to other data.
The recent EPS orders and corporate perquisite orders together suggest the SEC is using, at a minimum, data analysis to predict potential outliers across prior public filings. In the case of Fulton, the abnormally consistent attainment of EPS estimates, coupled with accounting adjustments, suggested a potential pattern of earnings manipulation. In the perquisite cases, SEC filings that altered or disclosed previously-undisclosed calculations for executive travel may have triggered closer scrutiny.
In all cases, however, it is clear that the SEC’s software focused on data that had the outward appearance of manipulation when examined in comparison with the huge volumes of other filings received daily by the SEC. At the same time, the SEC’s demonstrated track record of investing in and developing new data analytics tools suggests that the agency’s focus will not be limited to EPS estimates and corporate perquisites for long.
Consequently, companies should be cautious when making accounting adjustments or compensation calculations, especially where those changes can cause the company’s reported figures to appear as an “outlier” compared to past data, even in areas not currently announced as data-driven enforcement priorities. Ultimately, however, the SEC’s data analysis tools are focused on triggering investigations by enforcement personnel. While outlier data may cause a company to receive some mildly uncomfortable scrutiny, properly supported and justified figures can insulate a company from liability for any wrongdoing, whether the reported data is an “outlier” or not.