In an age of real-time tweets and decentralized commentary, it's easy to dismiss analyst ratings as old-fashioned. But they remain one of the most systematically predictive signals in public equities — especially when analyzed at scale.

At Finvera, we offer structured analyst ratings data across thousands of tickers, capturing changes in recommendations (buy, hold, sell), price targets, and estimate revisions. But we don’t stop at raw data. We normalize it, timestamp it precisely, and track consensus evolution over time — allowing our users to model momentum, divergence, and institutional sentiment.

What makes analyst ratings valuable isn’t just the action (“upgraded to buy”) — it’s the context. Was it a high-conviction change? How often has this analyst been right? Did others follow within a short window? Finvera helps surface these signals with clean attribution, historical snapshots, and API-first delivery.

We also handle edge cases, like conditional ratings ("speculative buy") or hybrid models (multiple PTs per coverage). You shouldn’t have to build a logic layer just to use ratings — we’ve already done that work.

If you're building models that rely on institutional sentiment or signals from the sell side, clean ratings data is essential. And with Finvera, it's finally easy to work with.