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The market’s reaction to changes in relative performance rankings

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Abstract

The media commonly gauges a firm’s performance by comparing its performance to others within the same industry. We provide evidence that investors and analysts positively value improvements to the firm’s relative performance ranking (RPR) within its industry. Consistently, RPR is positively associated with the firm’s earnings persistence, which suggests that RPR provides information about the firm’s ability to capture profits within the industry. We also find that managers use non-GAAP exclusions from earnings to improve the appearance of the firm’s RPR and that not all the information found in the firm’s performance ranking is priced by investors at the time of the earnings announcement. This evidence suggests that investors and analysts use the entire distribution of earnings to evaluate a firm’s performance, allowing us to identify an alternative benchmark not previously explored.

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Notes

  1. For example, The New York Times (Stewart 2015) compared the operating incomes of Apple and Microsoft, implicitly ranking each firm’s performance. The Wall Street Journal (Orlik 2012) noted that Lenovo increased personal computer shipments from 2010 to 2012, improving its ranking from fourth to second in the industry. This type of comparison is common. The Wall Street Journal (Calia 2014) compared the operating profit margins for several firms in the automotive industry when evaluating the operating performance of Chrysler. Many other similar examples dot the financial press landscape. Also, research suggests that financial statement users compare the firm’s financial information with its peers’ information (e.g., Lee et al. 2015).

  2. In describing the usefulness of the income statement, Kieso et al. (2018) state: “Examining revenues and expenses indicates how the company performed and allows comparison of its performance to its competitors. For example, analysts use the income data provided by Hyundai (KOR) to compare its performance to that of Toyota (JPN).”

  3. In untabulated results, we do not find evidence of an implementable trading strategy. Identifying an implementable trading strategy is not relevant to our analysis, as this is not the main thrust of the paper.

  4. Degeorge et al. (1999), Matsumoto (2002), Skinner and Sloan (2002), and Fischer et al. (2014) suggest that investors evaluate the firm’s performance relative to analyst forecasts. Burgstahler and Dichev (1997), Degeorge et al. (1999), Ertimur et al. (2003), and Jegadeesh and Livnat (2006) suggest that investors evaluate the firm’s performance relative to past performance. Burgstahler and Dichev (1997) suggest that investors evaluate the firm relative to zero earnings.

  5. In a robustness test, we examine the change in the firm’s RPR around other dates (i.e., not just the earnings announcement) in Section 8.2.

  6. Market share is another measure that could be used to measure the firm’s ranking within the industry. Unlike market share, the firm’s performance (using earnings) takes into consideration both the firm’s ability to generate sales and its ability to control costs. Given the importance of earnings in equity valuation (e.g., Ohlson 1995), we believe that investors consider both the firm’s ability to generate sales and its ability to manage costs when evaluating the firm, relative to its peers. Therefore we believe that evaluating the firm’s performance ranking based on ROE is a more comprehensive measure for the firm’s ability to capture profits in the industry. Nevertheless, we examine the market reactions to the changes in the market-share rankings and expect these rankings to be more important for growing firms. In untabulated tests, we use market-to-book ratio to identify growing firms and find some evidence that the investor and analyst reactions to market share ranking changes are more pronounced for them.

  7. We calculate the daily median EPS consensus analyst forecast using the IBES unadjusted detail file. We specifically calculate the median EPS consensus based on individual analyst forecasts, which are required to be reported within the 90-day window immediately preceding the consensus forecast date to ensure that our analyst consensus is not based on stale forecasts. We exclude individual analyst forecasts if IBES excludes the forecasts from calculating IBES-reported median EPS consensus. If the daily median EPS consensus analyst forecast is missing, we supplement our data by using IBES-reported median EPS consensus forecasts (i.e., IBES item MEDEST).

  8. IBES eliminates nonrecurring items from earnings when defining IBES earnings (Easton 2003). IBES employees adjust the earnings reported by the firm to exclude any nonrecurring or nonoperating items that are excluded by the majority of analysts following the firm (Thomson Reuters 2009, p. 6). Research provides support for the idea that analysts have the expertise to identify and exclude transitory revenues and expenses from non-GAAP earnings (e.g., Gu and Chen 2004).

  9. We deflate earnings to reduced heterogeneity of the firms with respect to firm size (e.g., Barth and Kallapur 1996; Barth and Clinch 2009). Without proper treatments, the scale effects may cause the violation of homoscedasticity assumption, which could lead to incorrect inferences. To reduce the likelihood that our results are sensitive to the choice of deflator, we replace ROE with the return on assets (ROA), profit margin (PM), and earnings-price ratio (E/P) as performance proxies to check the robustness of our results. The results using these alternative performance proxies are qualitatively similar to our main findings, presented in Tables 4–6.

  10. For example, Hui et al. (2016) examine the persistence and pricing of industry-wide and firm-specific earnings. Their measure of firm-specific earnings is simply the difference between firm earnings and the industry mean.

  11. We examine the change in ranking from quarter t-4 to t because it is possible that seasonality in earnings within certain industries induces a predicable change in performance ranking from quarter t-1 to t. By keeping the same seasonal quarter the same, we reduce the likelihood that we are identifying predictable ranking changes. Nevertheless, we re-estimate Table 1 comparing quarter t-1 to quarter t. We find qualitatively similar results to those reported in Table 1.

  12. We include only firms that have calendar/quarter fiscal period-ends to ensure that the earnings windows are the same for each firm in the industry. For example, we do not want to compare earnings generated from September to November for one firm to those generated from October to December of another, because there could be an industry or market shock in December that make the earnings of the two firms less comparable.

  13. Nearly monotonic relations in average returns are observed as we move from the first to fifth initial ranking quintile within each realized ranking quintile. Our theory suggests that a firm that starts in the highest initial ranking quintile and ends in the lowest realized ranking quintile should experience more negative returns, due to a decrease in the firm’s performance ranking, than firms that start in the lowest initial ranking quintile and end in the lowest realized ranking quintile. However, we believe that examining how the average return changes as we move from the first to fifth realized ranking quintile within each initial ranking quintile is more intuitive, because the initial ranking is first observed by investors and the realized ranking is subsequently realized.

  14. Our results are unaffected if we replace the changes in ROE and sales growth control variables with the industry-adjusted changes in ROE and industry-adjusted sales growth.

  15. The coefficient on CHG RANK EAi,t decreases when SUE RANKi,t is included in the regression. This suggests that CHG RANK EAi,t is identifying information that is also identified with the ranking of the firm’s earnings surprise within the industry (SUE RANKi,t). This is evident given the correlation between the two variables. The Pearson (Spearman) correlation between CHG RANK EAi,t and SUE RANKi,t is 0.643 (0.838). Both variables incorporate peer firms’ earnings information into its construction and thus represent the relative position of the firm in the industry. For this reason, a firm with a larger earnings surprise, relative to its peers, is more likely to experience a greater increase in its RPR. Therefore CHG RANK EAi,t and SUE RANKi,t indeed contain similar information. However, since we are trying to capture the change in the firm’s performance ranking within the industry and not the ranking of the surprise, SUE RANKi,t appears to be an important control variable. Note that SUE RANKi,t may control for what we want to capture with CHG RANK EAi,t (i.e., changes in the relative position of the firm in the industry), and thus controlling for SUE RANKi,t in the same regression leads to a downward bias in the CHG RANK EAi,t. SUE RANKi,t may also be controlling for more extreme earnings surprise observations (SUEi,t) as the Pearson (Spearman) correlation between SUEi,t and SUE RANKi,t is 0.532 (0.948). We perform several additional tests for nonlinearities in the earnings surprise described later in this section. Note that the Pearson (Spearman) correlation between CHG RANK EAi,t and SUEi,t is 0.533 (0.866).

  16. The results in Columns 5 and 6 of Tables 4 and 5 are robust to including industry fixed effects instead of firm fixed effects.

  17. The adjusted R2s in each regression in Panel B of Table 5 are either extremely small or negative. The overall model F-statistic for each regression estimated in Panel B of Table 5 is significant at the 1% level.

  18. In additional robustness tests, we include either Initial RANK EAi,t or Realized RANK EAi,t as an additional control variable. The results are qualitatively similar to those found in Tables 4 and 5.

  19. Lower ranked firms are likely looking to significantly improve their earnings stream (e.g., Lawrence et al. 2017), which would likely result in less persistent earnings, as lower ranked firms attempt to shed losses and capture profits. Therefore investors may desire lower ranked firms to have less persistent earnings when earnings are negative or low. Investors likely do not want lower ranked firms to continue producing persistent losses when the RPR is low.

  20. As mentioned earlier in the paper, we use IBES-reported actual earnings to reduce the impact of transitory expense and revenues. IBES attempts to eliminate nonrecurring expenses and revenues that are not included in the majority of analyst forecasts, which allows us to examine the persistence of the earnings that are “core” to the firm. Research provides support for the idea that analysts have the expertise to identify and exclude transitory revenues and expenses from non-GAAP earnings (e.g., Gu and Chen 2004).

  21. We re-estimate Table 6 with the decile ranked CHG RANK EAi,t variable, rather than the decile ranked RANKi,t variable, and continue to find a positive and significant coefficient on the interaction between ROEi,t and Decile CHG RANK EAi,t when ROEi,t+4 and ROEi,t+12 are the dependent variables. We no longer find a positive and significant coefficient on the interaction when ROEi,t+8 is the dependent variable. The results using the Decile CHG RANK EAi,t variable suggest that firms that experience unexpected increases to the RPR (revealed at the earnings annoucnemnt) have higher earnings persistence, corroborating our findings in Tables 4 and 5. We use the decile ranked RANKi,t variable in our main analysis (Table 6), because our theory is that firms with higher performance ranking (regardless of expected or unexpected) have greater earnings persistence.

  22. Collins and Kothari (1989) state: “Because dividends are assumed a positive fraction of earnings, greater persistence will lead to larger revisions in dividends expectations and the ERC will be larger.”

  23. We may not have fully controlled for management’s focus on increasing reported earnings or beating analyst forecasts. If so, the positive coefficient on the exclusion variables could still be attributed to management’s desire to increase reported earnings or beat analyst expectations. While we have attempted to control for these alternative incentives that managers have to use exclusions, we cannot fully rule out these possibilities with our research design.

  24. Specifically, we calculate the industry-adjusted abnormal return by subtracting the three-day equal-weighted buy-and-hold industry (defined as GICS codes) stock return from firm i’s three-day buy-and-hold stock return surrounding the earnings announcement. We exclude firm i from the equal-weighted buy-and-hold industry stock return.

  25. We find qualitatively similar results for the analyst forecast revisions and changes in stock recommendations regressions with the following two exceptions. First, when SUE RANKi,t is an additional independent variable in the AF Revisioni,t regression, CHG RANK EAi,t is no longer significant in the fifth earnings announcement quintile. Second, when SUE RANKi,t is an additional independent variable in the ∆RECi,t regression, we do not find any meaningful pattern, and CHG RANK EAi,t is only significant in the fifth earnings announcement quintile.

  26. Similar to our primary test, we use peer firms’ realized earnings to compute CHG RANK PRE EAi,t if the peer firms announce earnings 10 days prior to firm i’s actual earnings announcement date.

  27. We perform two additional tests to reduce concerns that the findings in Table 12 are due to information leakage before the actual earnings announcement date. First, we supplement Equation 5 to include SUEi,t and CHG RANK EAi,t, which are both calculated around the earnings announcement. Second, we supplement Equation 5 with SUE RANKi,t and CHG RANK EAi,t. We find qualitatively similar results for CHG RANK PRE EAi,t in both regressions. These results suggest that information leakage of the earnings announcement is not driving our results.

  28. In other words, if the realized ranking quintile less the initial ranking quintile is greater than one or less than negative one, then the observation is deleted.

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Authors and Affiliations

  1. Olin Business School, Washington University in St. Louis, St. Louis, MO, USA

    Jared Jennings

  2. NUS Business School, National University of Singapore, Lower Kent Ridge, Singapore

    Hojun Seo

  3. Marshall School of Business, University of Southern California, Los Angeles, CA, USA

    Mark T. Soliman

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  1. Jared Jennings
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Correspondence to Jared Jennings.

Additional information

We thank Stephen Penman (editor) and two anonymous reviewers for helpful comments. We also thank David Burgstahler, Dane Christensen, John Donovan, Alex Edwards, Richard Frankel, Paul Healy, Bob Holthausen, Raffi Indjejikian, Miguel Minutti-Meza, V.G. Narayanan, Pat O’Brien, Krishna Palepu, Tatiana Sandino, Haresh Sapra, Robert Simons, Suraj Srinivasan, Dan Taylor, Regina Wittenberg-Moerman, Charles Wang, Franco Wong, and Gwen Yu for helpful comments. We also thank workshop participants at the 8th annual Rotman Accounting Research Conference at the University of Toronto, Harvard University, University of Pennsylvania, Washington University in St. Louis, and 2015 AAA annual and Midwest regional meeting, Boston College, 2016 Colorado Summer Accounting Research Conference, National University of Singapore, and University of North Carolina at Chapel Hill. We are grateful for financial support from the Olin Business School, NUS Business School, and the Leventhal School of Accounting.

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Jennings, J., Seo, H. & Soliman, M.T. The market’s reaction to changes in relative performance rankings. Rev Account Stud 25, 672–725 (2020). https://doi.org/10.1007/s11142-020-09532-1

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