Brier Labs reads the market before the opening bell, then grades every one of its own calls against real closing prices. Confidence is easy to generate. We publish the record instead.
The pipeline runs unattended every weekday. Nothing is hand-picked, and nothing is quietly discarded when it turns out wrong.
Independent news feeds are pulled for every position, deduplicated, and consolidated into a single evidence set per company.
A language model weighs the full set at once and returns a structured call with a sentiment score and a written rationale.
After the close, real prices come back in. Each past call is marked correct or missed, and the record updates itself.
The Brier score is the standard way to measure how accurate a probabilistic forecast turned out to be. We took the name because grading the forecast is the harder half of the work, and the half most tools skip.
Every call keeps the exact headlines behind it. When a call fails, the reason it failed is still on file.
In a rising market, always-buy scores well. We report it next to our own number so the comparison is unavoidable.
Same-day accuracy and one-month accuracy answer different questions. We don't blend them into one flattering figure.
Nothing resets. The history grows and stays queryable, which is what makes the system able to learn from its own misses.
Market sentiment moves more than share prices. It moves input costs, freight, and the pricing decisions that follow.
See the macro pressure building on your inputs before it reaches a quarterly review, with the reasoning attached.
Track the sectors your suppliers sit in, and get told when the tone around them turns — not a week after it did.
A running record of what the market was saying, when, and whether it turned out to be right.
We're accumulating the record now. When there's enough of it to mean something, it gets published — good or bad — and early users see it first.