Key facts for IBES Smartestimates & Analytics
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Overview of IBES Smartestimates & Analytics
Reveal predictive estimate signals early
• Leverage SmartEstimates and predictive surprise analytics to anticipate earnings surprises sooner.
• Use analyst ranking to track the top performing analysts and their forecasts.
• Track revision clusters and recommendation summary analytics.
Improve conviction with smarter estimates
• Weight analyst forecasts by accuracy and timeliness instead of treating every estimate equally.
• Identify estimate momentum through coordinated revision clusters.
• Support quantitative screening using predicted surprise indicators.
Integrate analytic into investment workflow
• Access SmartEstimate, Analyst Rankings through documented models.
• Leverage SmartEstimate and Predicted Surprise signals to identify potential earnings surprises.
• Monitor SmartEstimates changes and trends through APIs, data feeds and desktop.
Description of the dataset
- For investors seeking more predictive estimate signals, LSEG provides Estimate Analytics powered by StarMine SmartEstimates and related I/B/E/S summary measures. We enhance traditional mean estimates by weighting analysts based on timeliness and historical accuracy rather than treating every estimate equally.
- LSEG identifies recent revision clusters to help customers detect coordinated changes in analyst expectations over short periods. We provide cluster dates, detection dates, average revisions, cluster mean estimates and estimate units so customers can evaluate momentum in forecast revisions.
- Our analytics content includes mean, median, high, low and standard deviation measures across estimate periods and windows. We also support KPI-level analytics through mean KPI, high KPI, low KPI, KPI standard deviation, normalization indicators, currencies and unit scales.
- LSEG organises Estimate Analytics across tables including SmartEstimateLineItem, EstimateCluster, MeanEstimateSummary, SmartEstimateSummary, RecommendationSummary and CalculatedLongTermGrowth. We document 306 data dictionary rows to support integration of SmartEstimates, consensus summaries, surprise windows and recommendation analytics.
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