Sell-side equity analysts — earnings forecast accuracy

Source class: Analyst firms
Metric: Systematic optimism + analyst-disagreement-vs-error correlation (proper-scoring-rule analogue for point forecasts)
Reported value: public — survey of decades of empirical work
Measured: 2011-06-30

Context

A widely cited literature review of decades of empirical work on sell-side analyst earnings forecasts. Findings include: forecasts are systematically optimistic, optimism declines with horizon, recommendations have informational content for investors only when conditioned on forecast revision history, and consensus-disagreement among analysts is a useful proxy for forecast uncertainty (a calibration-adjacent property).

Citation

Bradshaw, M. T. (2011). Analysts' Forecasts: What Do We Know After Decades of Work? Working paper, Boston College Carroll School of Management.

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1880339

What Phase 1 launch will add

Calibration Ledger has not independently recomputed the value above. Phase 1 launch (target Q3 2027, gated on prerequisites) will add for this source class:

Independent recomputation from the original outcome data, under data-licensing agreement
Time-windowed breakdown (rolling 3-month, 12-month, lifetime)
Cross-domain calibration (does this source calibrate uniformly across topical verticals?)
Append-only timestamp anchoring of every score so retroactive revisions are visible
Per-source citation page with full Murphy decomposition (Reliability − Resolution + Uncertainty)

Other findings in the same source class

Federal Reserve Survey of Professional Forecasters — GDP / inflation accuracy — Real-time forecast error vs. final-revised outcome (RMSE per horizon; coverage of probability ranges)

All other findings

Good Judgment Project Superforecasters (Human forecasters)
Metaculus community-prediction aggregate (Forecaster aggregator platform)
Manifold Markets — platform calibration (Prediction market)
GPT-4 (OpenAI) — pre-RLHF vs post-RLHF calibration (AI models)
Open Science Collaboration — psychological science replication rate (Scientific papers)
Anthropic — Claude / language model self-knowledge (AI models)
Camerer et al. — social science experiment replication (Nature/Science 2010-2015) (Scientific papers)
Hausfather et al. — climate model projections vs. observed warming (Scientific papers)

All beta findings — at-a-glance + JSON + BibTeX exports
Methodology v1.1 — full Brier + Murphy + append-only framework
Operator track record — methodology applied to Paulo de Vries’s own dated forecasts
Source classes — what each of the 6 source classes will score at Phase 1
Roadmap — milestone status + Q3 2027 launch gate + kill criterion

Last verified: 2026-04-28. Cited; Calibration Ledger has not independently recomputed this finding. Independent recomputation in Phase 1 (Q3 2027). Operator: Paulo de Vries. Contact: contact@calibrationledger.com.