Why Consensus Anchors Deep Estimates as Well as High-Level Ones

Analysts construct financial forecasts based on their own views of the future but inevitably look to “the consensus” as a benchmark for how their views of the future differ from that of the broader analyst community. In many instances, that consensus coalesces around company financial guidance, when such guidance is available – which it often is for high-level or “headline” metrics, such as revenue or EPS.

However, analysts’ forecasts typically converge around a consensus even when those average analyst estimates aren’t anchored by financial guidance, which is often unavailable for deeper financial metrics. (Bloomberg’s MODL function shows the mean of analyst estimates, for which there are generally more broker contributors for higher-level line items, like revenue, than deeper performance drivers, such as segment sales growth).

When we undertook the brief study described in this article, we expected to observe analysts’ estimates coalescing around a headline financial forecast, such as revenue, but varying more widely for detailed drivers and key performance indicators (KPIs) that lie deeper in company financial statements and disclosures. While consensus estimates do sometimes vary more widely for deep estimates and drivers, there are other instances in which analysts’ estimates around deeper metrics exhibit smaller variations than is the case for high-level financial outputs.

For companies 2, 3 and 5 in the accompanying exhibit, the coefficient of variation (CV) – defined as standard deviation divided by mean – for estimates of deeper KPIs exceeded that for top-line revenue. In the instance of company 2, Street estimates for slot revenue had a CV of 5.8%, which was more than 2% for its overall revenue. For company 3, the CV of 8.8% pertaining to estimates for incentive fees exceeded that of 3.5% for total revenue. Likewise, company 5’s CV of 1.8% for replacement sales was greater than the 1.3% CV for total sales.

The data for companies 1 and 4 tell a different story. The CV associated with consensus estimates for company 1’s revenue yield (a deep metric) was 1.7% – less than the 2.8% coefficient for total revenue (a headline metric). Moreover, the CV for company 4’s revenue per user, at 4.8%, was less than the 5.2% coefficient of variation for total revenue. In these two instances, the dispersion of estimates around consensus was smaller for deeper revenue building blocks than it was for high-level revenue forecasts.

Analysts tend to embrace consensus for deep revenue metrics in the same way they do for top-line or bottom-line metrics – even in the absence of detailed financial guidance. While it’s difficult to generalize about the thought processes underlying their estimates, it seems that analysts, either consciously or subconsciously, gravitate towards average views, even when such views emerge in the absence of published company projections. It’s also possible that analysts – who employ similar analytical methods and often rely on related industry data sets and management commentary – turn to similar heuristics* in constructing model forecasts.

* The Decision Lab, an applied research firm that focuses on behavioral science, defines heuristics as “mental shortcuts that allow us to make quick judgement calls based on generalization or rules of thumb.”

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