The Speed of Earnings Anticipation: Evidence from Daily Analyst Forecasts

Marshall, Nathan T.

doi:10.2139/ssrn.2795160

Cited by 2 publications

(7 citation statements)

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“…When we turn to examining EIFT variation, we find that the sign of earnings news affects the flow of earnings information arrival in the market. Consistent with Marshall (2018) and managerial disclosure incentives (Donelson et al, 2012), we find bad earnings news is reflected by analysts' forecasts on a timelier basis than good news. However, in subsequent tests we show that this effect is concentrated in instances where analysts forecast street earnings rather than GAAP earnings, which suggest that bad news is more readily available when it relates to earnings from firms' underlying operations.…”

Section: Discussionsupporting

confidence: 66%

“…To examine the effect of the sign and magnitude of earnings news on EIFT, we utilise a regression approach. The use of a regression approach is appropriate given that analyst forecast revisions are typically a relatively smooth trajectory (Marshall, 2018). We estimate the following equation: We identify the effect of earnings news sign by using an indicator variable, GOOD_NEWS, which equals one if the difference between actual earnings per share and the consensus estimate at the beginning of the fiscal period is positive, and zero otherwise.…”

Section: Methodsmentioning

confidence: 99%

“…Hence, we include GOOD_NEWS * LN(ABSFE) and BAD_NEWS * LN(ABSFE) in the regression model. In addition to our examination of the effect on EIFT of the sign and magnitude of the earnings news, we also include several control variables (Marshall, 2018). Analysts can diverge in their earnings estimates due to differences in skills and experience, a problem that is exaggerated when they are covering firms with extreme balance sheet fundamentals (Clement, 1999;Joos et al, 2016).…”

Section: Methodsmentioning

confidence: 99%

“…7 We also remove 14 invalid firm-years with forecasts made after the earnings announcement date. To reduce any analyst selection effects (McNichols and O'Brien, 1997), we follow Marshall (2018) and hold constant the analyst pool for each firm-year. This means an analyst must hold an active forecast since the start of the fiscal period until the earnings announcement date.…”

Section: Sample Selectionmentioning

confidence: 99%

“…However, these measures capture only the production of information, rather than its usage by market participants. To examine the efficiency of the market for earnings information among Australian firms, we employ a modified version of the measure suggested by Marshall (2018), which we label as earnings information flow timeliness (EIFT). Put simply, this measure captures the speed with which analysts' earnings forecasts adjust to more accurately reflect firms' earnings outcomes.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

How efficient is the market for Australian firms’ earnings information?

Taylor

Tong

2019

Accounting & Finance

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We construct a measure of the speed with which forecasts issued by sell‐side analysts accurately forecast future annual earnings. Following Marshall, we label this measure earnings information flow timeliness (EIFT). This measure avoids the aggregation problem inherent in price‐based measures of information efficiency. We document large variation in EIFT across firm‐years, and show that EIFT is positively associated with the extent of analyst following, consistent with increased analyst coverage improving the speed with which earnings‐related information is recognised. We also find that EIFT is higher for firm‐years classified as ‘bad news’ (i.e., where analysts’ forecasts at the start of the financial period exceed the reported outcome). However, when we separately consider instances where analysts appear to forecast non‐GAAP (or ‘street’) earnings rather than GAAP earnings, we find that the greater timeliness of bad news is concentrated among observations where analysts forecast non‐GAAP earnings, where unusual items are typically excluded. We conclude that the market for accounting information is more efficient for negative operating outcomes than for negative outcomes reflecting unusual items.

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Section: Discussionsupporting

confidence: 66%

Section: Methodsmentioning

confidence: 99%