Handling the Lag in Analyst Earnings Estimates for Valuation
Summary
The document discusses how delayed sell-side earnings revisions can distort valuation signals. After a sharp stock decline, stale forecasts may make a company look unusually cheap; after a market crash, stale aggregate estimates may likewise suggest the market is inexpensive before recession risks appear. The responses caution against treating analyst estimates as timely measures of fundamentals or assuming that their revisions can be forecast reliably.
One answer points to research on analyst forecast quality and describes a study whose forecasts were reportedly close to slightly adjusted naïve estimates, while acknowledging that the reference was not recalled. It suggests forecasting earnings from one’s own models is difficult, even for the broad market, and that structural changes can undermine historical inputs. If analyst revisions demonstrably affect prices, the relevant signal may be analyst behavior itself. Another answer proposes normalizing valuation measures across the business cycle, such as comparing price with peak earnings, because normalized measures may mean-revert. The discussion offers no tested forecasting procedure or detailed empirical results, so these suggestions are guidance rather than a validated trading strategy.
Key ideas
- Analyst earnings estimates can lag market expectations and make valuation ratios misleading after large price moves.
- The responses question whether analyst forecasts add reliable information beyond simple earnings extrapolation.
- Forecasting company earnings remains difficult, and structural shifts can break historically useful models.
- If analyst revisions move prices, a signal may need to model analyst behavior rather than fundamentals alone.
- Business-cycle normalization, including price relative to peak earnings, is suggested as a way to reduce cyclical distortion.
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# How do earnings estimates respond to changes in underlying fundamentals and economic conditions? # How do earnings estimates respond to changes in underlying fundamentals and economic conditions? Sell-side analysts' earnings estimates for individual companies, typically reported by I/B/E/S, are a key ingredient to many quantitative models. However, revisions to analyst estimates tend to lag changes in market expectations. Basing a model's recommendation on these estimates, particularly for valuation, can often lead to certain stocks seeming very cheap only because the stock price has declined dramatically and analyst estimates have not yet had a chance to catch up. Likewise, for market timing, the entire market can seem cheap immediately after a crash ahead of a predicted recession. How should one deal with the lag when constructing valuation indicators? Should you attempt to correct for the lag by attempting to predict the coming changes in earnings estimates? If so, how should you do this prediction? Is there any available research on analyst earnings revisions? ## Answer by bill_080 (score 5, accepted) https://quant.stackexchange.com/a/2145 I wouldn't put too much faith in IBES forecasts. You may remember this situation: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=889322 (In case the above link doesn't work, Google "Rewriting History Alexander Ljungqvist"). You'll find lots of excuses for worthless forecasts: http://www.princeton.edu/~hhong/rje-analyst.pdf Below is a graph that I saved from some study. As I recall, their point was that analyst forecasts are typically nothing more than slightly modified naive forecasts (i.e. next year's earnings will be about the same as this year's earnings....and this was for analyst forecasts of S&P500 earnings). I'll post a reference if I can find it again. The bottom line is, you're better off if you ignore analysts. From your own models you'll find that forecasting earnings for individual companies is a crap shoot. And, by far, the easiest earnings sequence to forecast is for the S&P500. However, that's by no means easy. Structural shifts happen without warning and model inputs that were useful for decades can easily drift into bizarre territory. As far as your questions go, if you have evidence that some analyst's earnings forecasts have influence on a stock/index, your model would probably be more about the analyst than actual earnings. Edit 1 (10/11/2011) =================================================== Additional information on IBES earnings data: http://www.olin.wustl.edu/docs/Faculty/Larocque%20paper.pdf Analyst earnings expectations: http://rady.ucsd.edu/faculty/directory/timmermann/docs/three_states_24May_2009_final.pdf http://business.rice.edu/uploadedFiles/Faculty_and_Research/Academic_Areas/Accounting/papers/yeung_january_2011.pdf http://www.stanford.edu/group/knowledgebase/cgi-bin/2011/08/15/when-they-are-wrong-analysts-may-dig-in-their-heels/ http://www.business.uconn.edu/Realestate/publications/pdf%20documents/407%20Do%20Investors%20See%20Through%20Mistakes%20in%20Reported%20Earnings.pdf ## Answer by Ram Ahluwalia (score 1) https://quant.stackexchange.com/a/2155 I would normalize valuation variables over the business cycle. These normalized variables exhibit mean-reversion. For example, use price-to-peak earnings rather than P/E. Here is a good illustration of the idea.
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