Pulling Back the Curtain on the Drivers of Signed Earnings Announcement Returns
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Pulling Back the Curtain on the Drivers of Signed Earnings Announcement Returns John R. M. Hand Henry Laurion UNC–Chapel Hill University of Colorado Boulder [email protected] [email protected] Alastair Lawrence Nicholas Martin London Business School UNC–Chapel Hill [email protected] [email protected] ABSTRACT Since 1990, the number of one-quarter-ahead financial statement items forecasted by analysts and firm managers that are captured in I/B/E/S and FactSet online data feeds has soared from 1 to 29 and 0 to 21, respectively. We propose that this shift in data capture enables us as researchers to pull back the curtain on the drivers of signed returns at earnings announcements more powerfully than ever before. We offer support for our view by annually estimating regressions of earnings announcement returns on the increasingly rich and diverse set of analyst and guidance forecast errors in I/B/E/S and FactSet, and show that the adjusted R2s of regressions that include all available financial item forecast surprises are up to six times larger than those that contain Street earnings surprise alone. Most of the increase in explanatory power comes from analyst and guidance surprises about firms’ top lines, P&L subtotals, and cash flows—revenues, EBITDA, EBIT, pre-tax income, and operating and free cash flows—rather than from balance sheet items or expenses such as SG&A, depreciation, R&D or income taxes. We also document marked time-series trends in the coefficients on analyst and guidance forecast errors and adjusted R2 and conclude that they reflect both the increase in our ability as researchers to better see what the market is seeing when it sets stock prices at earnings announcements, and changes in the economic relations among returns and accounting information. April 7, 2019 Keywords: Analyst and guidance forecast errors, FactSet, I/B/E/S, information content JEL Classifications: G12, G17, M41 Data Availability: Data are available from the sources cited in the text. We appreciate the helpful comments of two anonymous reviewers, Mark Bradshaw, Andrew Alford, Ray Ball, Robert Bushman, Emmanuel De George, Travis Dyer, Petri Ferreira, Mustafa Gultekin, Peter Joos, Jim Ryans, Richard Sloan, Ahmad Tahoun, Eli Talmor, Irem Tuna, and workshop participants at the University of Connecticut, IESE, INSEAD, London Business School, Ohio State University, and UNC– Chapel Hill. I. INTRODUCTION Ever since the seminal studies of Ball and Brown (1968) and Beaver (1968), empirical financial accounting researchers have sought to document, measure and understand the magnitude and drivers of the information content of earnings and earnings announcements. However, while it is clear that earnings surprises are causal drivers of earnings announcement returns, the measured explanatory power of the relation has been small, on the order of 2% to 5% depending on the samples and regression specifications used. This has led some researchers to throw substantial shade on the usefulness of accounting information to capital market participants (e.g., Lev, 1989). In this paper we put forward a less pessimistic view of the explanatory power of accounting numbers at earnings announcements by introducing a new data set to the literature: the union of I/B/E/S’ Summary History and FactSet’s Standard DataFeed Estimates online data feeds, henceforth the IUF data feed.1 As of mid-2016 the IUF data feed contained 29 different quarterly financial statement items forecasted by analysts, and 21 financial statement items managers provide quarterly guidance about. The goal of our paper is to exploit the richness of the IUF data feed and assess whether, how and why our understanding of the magnitude and drivers of the information content of accounting data released at earnings announcements warrants being seen in a new light. Our current analysis ignores 131 key performance indicators (KPIs) that are available through FactSet and I/B/E/S. We begin our analysis with the assumption that analysts, managers and investors have always had strong incentives to forecast more than bottom line net income and EPS, and have indeed done so. Not only are detailed forecasts of the line items in all three major financial statements required for proper DCF valuation, but the SEC requires public companies to disclose granular actuals financial statements in their 10-K and 10-Q filings, making highly dimensioned line item forecasts and forecast surprises a reality for market participants. For example, since the 1970s Value Line analysts have published their forecasts of 22-23 quarterly and annual financial items, spread across all three statements, every 13 weeks for approximately 1,700 stocks that Value Line deems to be of interest to institutions. In contrast, researchers have rarely gone beyond including GAAP or Street earnings when explaining variation in earnings announcement returns. As such, we propose that prior literature has understated the information content of accounting data at earnings announcements because market 1 FactSet is a multinational financial data and software company that was founded in 1977 and went public in 1996. Revenues in its most recent fiscal year ended 8/31/17 were $1.22 billion. I/B/E/S (Institutional Brokers’ Estimates System) was founded by Lynch, Jones & Ryan and Technimetrics and began collecting earnings estimates for US companies in 1976. Barra bought I/B/E/S in 1993, then sold it to Primark in 1995. Thomson Financial (now Thomson Reuters) purchased Primark in 2000. We focus on dissemination through FactSet and I/B/E/S because they are the largest online providers of analyst forecast data feeds to US capital markets. 1 participants have in reality been repricing firms’ equity at earnings announcements using a far richer and more detailed set of analyst and management guidance surprises than researchers have employed. We test our proposition by estimating annual cross-sectional regressions of signed earnings announcement stock returns on analyst and guidance forecast errors for all the non-KPI financial statement items contained in the IUF data feed over the period 1990-2016. Because I/B/E/S and FactSet have added items to the IUF data feed in a temporally staggered manner, the time-series of adjusted R2 in our regressions allows us as researchers to gradually “pull back the curtain” on the drivers of earnings announcement returns, and to do so in a more powerful way than previously. In our results we find that the adjusted R2s of regressions that include the set of IUF analyst and management guidance forecast surprises for financial statement items are up to six times larger than those of regressions that contain Street earnings surprise alone. Most of the increase in explanatory power comes from analyst and guidance surprises about firms’ top lines, P&L subtotals, and cash flows—revenues, EBITDA, EBIT, pre-tax income, and operating and free cash flows—rather than from balance sheet items or expenses such as SGA, depreciation, R&D and income tax. We also show that the multiples on guidance surprises for Street earnings and sales revenue are on average at least double those on analyst forecast surprises, indicating that investors place much more weight on new accounting-based information from managers about their firm’s expected future performance than on the resolution of uncertainty about actual firm performance in the most recently completed quarter. We then document and evaluate time-series trends in the estimated coefficients on IUF analyst and guidance surprises and the regression adjusted R2s. We propose that if there is no change in the set of analyst and management guidance surprises that are available to market participants at earnings announcements, and no change in the economic relations among announcement returns and analyst and management guidance surprises, then two testable predictions arise. First, when Street earnings surprise is the sole explanatory variable in the annual regressions, the coefficient on Street earnings surprise will show no upward or downward trend over time. This is because with an unchanging correlation structure between earnings announcement returns and surprises, while it will be the case that the coefficient on Street earnings surprise will be biased because it carries on it the correlations with the many surprises that are omitted, the bias will be constant over time. The second prediction is that when analyst and guidance surprises are included as they become available over time in the IUF data feed, to the extent that such surprises have explanatory power, the estimated coefficient on Street earnings surprise will decline as the omitted correlated variable bias on Street earnings surprise falls. We find results that are inconsistent with the first prediction, but consistent with the second. Specifically, we observe a reliable downward trend in the estimated coefficient on Street earnings 2 surprise over time as IUF analyst and guidance surprises are included in the annual regressions, and a reliable upward trend in the estimated coefficient on Street earnings surprise when it is the only independent variable. This leads us to infer that while the IUF data feed enables researchers to better see what the market sees at earnings announcements, the IUF data feed also makes visible otherwise hidden changes in the underlying economic relations between accounting-based drivers of firms’ stock returns and firms’ economic characteristics. Our study contributes to the accounting literature in several ways. First, we introduce the IUF data feed to empirical accounting research and highlight the rich and diverse data that it contains. Second, through the results we present, we offer a less pessimistic perspective on the information content of earnings than has historically been assessed. Third, we add to the insights of other recent work that has shed new light on the information content of earnings. Our study complements Beaver et al.