Analyst Information Precision and Small Earnings Surprises

Analyst Information Precision and Small Earnings Surprises
Author: Sanjay Bissessur
Publisher:
Total Pages: 46
Release: 2017
Genre:
ISBN:

This study proposes and tests an alternative to the extant earnings management explanation for zero and small positive earnings surprises (i.e., analyst forecast errors). We argue that analysts' ability to strategically induce slight pessimism in earnings forecasts varies with the precision of their information. Accordingly, we predict that the probability that a firm reports a small positive instead of a small negative earnings surprise is negatively related to earnings forecast uncertainty and present evidence consistent with this prediction. Our findings have important implications for the earnings management interpretation of the asymmetry around zero in the frequency distribution of earnings surprises. We demonstrate how empirically controlling for earnings forecast uncertainty can materially change inferences in studies that employ the incidence of zero and small positive earnings surprises to categorize firms as “suspect” of managing earnings.

A Temporal Analysis of Earnings Surprises

A Temporal Analysis of Earnings Surprises
Author: Lawrence D. Brown
Publisher:
Total Pages: 22
Release: 2014
Genre:
ISBN:

I show that the median earnings surprise has shifted rightward from small negative (miss analyst estimates by a small amount) to zero (meet analyst estimates exactly) to small positive (beat analyst estimates by a small amount) during the 16 years, 1984 to 1999. I show that a rightward temporal shift in median surprise from negative to positive describes earnings, but neither profits nor losses. Median profit surprise shifts within the positive quadrant, from zero to one cent per share. Median loss surprise shifts within the negative quadrant from extreme negative (about -33 cents per share) to zero. I show that the median surprise for profits exceeds that for losses in every year. I document significant positive temporal trends in both meet and beat analyst estimates for both profits and losses, but I find a greater frequency of profits that either meet or beat analyst estimates in every year. I find a significant positive temporal trend in positive profits that are 'a little bit of good news,' and a significant negative temporal trend in managers who report losses that are an 'extreme amount of bad news.' My results are robust to the four internal validity threats I consider - namely temporal changes in: (1) analyst forecast accuracy, (2) the mix of earnings of one sign preceded by earnings of another sign four quarters ago, (3) the timeliness of the most recent analyst forecast, and (4) the I/B/E/S definition of actual earnings. I find that managers of growth firms are relatively more likely than managers of value firms to report good news profits. I show that when they do report positive profit surprises, managers of growth firms are more likely to report 'a little bit of good news' in every year.

The Value of Analyst Forecast Revisions

The Value of Analyst Forecast Revisions
Author: Kanyuan Huang
Publisher:
Total Pages: 60
Release: 2022
Genre:
ISBN:

This paper examines the information contained in analyst forecast revisions following earnings announcements. I find that sorting firms on aggregated forecast revisions generates a much stronger post-earnings-announcement drift than sorting on measures of earnings surprises. The strong association between aggregated forecast revisions and post-earnings-announcement returns is driven by the subsample of firms with large-magnitude earnings surprises. This result is consistent with analysts' roles in interpreting corporate earnings. Further, the mispricing is the strongest when forecast revisions contradict earnings surprises, suggesting investors have difficulties in processing contradictory signals. Lastly, I document aggregated forecast revisions are more informative when the information environment around earnings announcements is more opaque, when firms have high accruals and when investors do not pay attention to the firm. They are less informative when analysts disagree with each other. Overall, these results point to the value of analyst forecast revisions following earnings announcements.

Does the Stock Market See a Zero or Small Positive Earnings Surprise as a Red Flag?

Does the Stock Market See a Zero or Small Positive Earnings Surprise as a Red Flag?
Author: Zhi-Xing Lin
Publisher:
Total Pages: 58
Release: 2007
Genre:
ISBN:

Manipulation of earnings or analyst earnings expectations is costly to firms. Manipulators of earnings and/or analyst earnings expectations therefore are likely to report earnings that precisely meet or narrowly beat analyst earnings forecasts, resulting in a zero or small positive earnings surprise. We predict that investors see such an earnings surprise as a red flag in their attempt to identify manipulators of earnings and/or analyst earnings expectations. Consistent with our prediction, we find the coefficient in the regression of abnormal stock returns on the earnings surprise (known in the literature as the earnings response coefficients or ERC) is significantly lower for zero and small positive earnings surprises than for earnings surprises in adjacent ranges. We find evidence that analysts see a zero or small positive earnings surprise as a red flag as well. The coefficient in the regression of analyst revisions of next-quarter earnings forecasts on the earnings surprise (the analyst ERC) is significantly lower for zero and small positive earnings surprises than for earnings surprises in adjacent ranges.

Trading on Corporate Earnings News

Trading on Corporate Earnings News
Author: John Shon
Publisher: FT Press
Total Pages: 225
Release: 2011-03-09
Genre: Business & Economics
ISBN: 0132615851

Profit from earnings announcements, by taking targeted, short-term option positions explicitly timed to exploit them! Based on rigorous research and huge data sets, this book identifies the specific earnings-announcement trades most likely to yield profits, and teaches how to make these trades—in plain English, with real examples! Trading on Corporate Earnings News is the first practical, hands-on guide to profiting from earnings announcements. Writing for investors and traders at all experience levels, the authors show how to take targeted, short-term option positions that are explicitly timed to exploit the information in companies’ quarterly earnings announcements. They first present powerful findings of cutting-edge studies that have examined market reactions to quarterly earnings announcements, regularities of earnings surprises, and option trading around corporate events. Drawing on enormous data sets, they identify the types of earnings-announcement trades most likely to yield profits, based on the predictable impacts of variables such as firm size, visibility, past performance, analyst coverage, forecast dispersion, volatility, and the impact of restructurings and acquisitions. Next, they provide real examples of individual stocks–and, in some cases, conduct large sample tests–to guide investors in taking advantage of these documented regularities. Finally, they discuss crucial nuances and pitfalls that can powerfully impact performance.

Earnings Surprises that Motivate Analysts to Reduce Average Forecast Error

Earnings Surprises that Motivate Analysts to Reduce Average Forecast Error
Author: Orie E. Barron
Publisher:
Total Pages: 0
Release: 2008
Genre:
ISBN:

Large earnings surprises and negative earnings surprises represent more egregious errors in analysts' earnings forecasts. We find evidence consistent with our expectation that egregious forecast errors motivate analysts to work harder to develop or acquire relatively more private information in an effort to avoid future egregious forecasting failures. Specifically, we find that after large or negative earnings surprises there is a greater reduction in the error in individual analysts' forecasts of future earnings, and these individual forecasts are based more heavily on individual analysts' private information. This increased reliance on private information reduces the mean forecast error for upcoming earnings (even after controlling for the effect of reduced error in individual forecasts). As reliance on private information increases, more of each individual forecast error is idiosyncratic, and thus averaged out in the computation of the mean forecast.

Changes in Analysts' Information Around Earnings Announcements

Changes in Analysts' Information Around Earnings Announcements
Author: Orie E. Barron
Publisher:
Total Pages: 0
Release: 2002
Genre:
ISBN:

In this study we examine changes in the precision and the commonality of information contained in individual analysts' earnings forecasts, focusing on changes around earnings announcements. Using the empirical proxies suggested by the Barron et al. (1998) model that are based on the across-analyst correlation in forecast errors, we find that the commonality of information among active analysts significantly decreases around earnings announcements. We also find that the idiosyncratic information contained in these individual analysts' forecasts increases significantly immediately after earnings announcements, and this increase is more significant as more analysts revise their forecasts. These results are consistent with theories positing that an important role of accounting releases is to trigger the generation of idiosyncratic information by elite information processors such as financial analysts (Kim and Verrecchia 1994, 1997).