NASDAQ100 analyst rating changes and price target revisions

What is AnaChart?

AnaChart compiles a structured record of every analyst rating change and price target revision on NASDAQ100 constituents, captured point in time. Each observation includes the analyst, brokerage firm, rating before and after the change, price target before and after the revision, and the timestamp. The result is a longitudinal dataset built for empirical finance research rather than for traders watching a Bloomberg terminal.

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What academic researchers should know about AnaChart analyst ratings data

Coverage starts in 2015 and spans more than 80,000 observations across NASDAQ100 names, refreshed monthly on an incremental basis. Records are standardized, deduplicated, and structured to stay consistent across brokers, analysts, and issuers. The point in time format preserves the rating and target values that were active before each update, which is what makes the data usable for event studies, herding tests, and any analysis where what was known when matters. Distribution of observations varies by firm, analyst, and issuer, since the data reflects publicly available research disclosures.

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Why academic researchers choose AnaChart on Dewey

Standard analyst ratings feeds tell you the current consensus but not what an analyst said yesterday or two years ago, which is exactly the question most empirical finance research turns on. AnaChart fills that gap by structuring rating transitions and target revisions with their prior values, in a format clean enough to drop into an event study without wrangling. The data is built for rigorous backtesting and sentiment work, not casual stock picking, which is why finance and financial engineering programs at Brown, NYU, the University of Virginia, and Northeastern have already built it into their research pipelines. It slots cleanly into any research design that needs analyst signals alongside returns, volatility, or earnings data.

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AnaChart academic research ideas and use cases

Analyst herding and credibility. Are analysts moving with the crowd or ahead of it, and which ones are worth listening to? An NYU capstone project, "Decoding Analyst Signals: Credibility, Herding, and Strategy," used AnaChart's rating and price target history to separate consensus chasers from leading voices and to test whether early movers carry more information value. Similar designs extend to any subset of analysts or sectors within the NASDAQ100.

Event studies on analyst actions. Rating upgrades and price target revisions are textbook event study triggers, but most data feeds make it hard to recover the rating that was active just before the change. AnaChart's structured before-and-after format removes that friction. A 2025 NYU capstone used the dataset to measure short window market reactions to analyst actions across NASDAQ100 names, and the same approach extends naturally to volatility events, earnings windows, and sector rotations.

Behavioral finance and sentiment dynamics. Analyst language and target revisions are a cleaner read on professional sentiment than headline surveys, and they pair well with returns to test for overreaction, underreaction, and anchoring. The structured rating transitions are well-suited to modeling how sentiment propagates through the analyst community before showing up in price.

Asset pricing and factor construction. Rating and target changes can be aggregated into firm-level signals that feed into cross-sectional or time series asset pricing tests. Northeastern has used AnaChart to build analyst signal factors and benchmark them against established quality, momentum, and earnings revision factors, which is a template other quantitative finance programs can run with on the NASDAQ100 panel.

Quantitative finance curricula and reproducible research. The structured, point-in-time format makes the dataset a strong fit for graduate finance and financial engineering programs that want students to run real event studies and backtests on real disclosure data. The longitudinal panel since 2015 is large enough for serious empirical work and small enough to teach within a semester.

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Dive deeper with Dewey documentation

Detailed information on onboarding with Dewey, data partner details, and technical documentation on data access.

View documentation

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