Everyone loves a hit rate. “78% accurate” sounds authoritative until you ask: accurate on what horizon? Against which benchmark? With what universe of forecasts included or excluded? Without definitions, track records become marketing. With definitions, they become a useful—but still imperfect—tool for comparing transparency and consistency.
Before accepting any score, translate it into a sentence a careful friend would agree with. For example: “When this source published a directional call on a large-cap U.S. equity, the price was higher 90 trading days later in 60% of cases, measured from the close of publication day.” If you cannot write that sentence, you do not yet understand the metric.
The same forecast can look brilliant at 30 days and wrong at 180. Good track records specify horizons (or a small, justified set of horizons) and stick to them. Switching horizons after the fact is one of the easiest ways to accidentally fool yourself—or others.
A source can be “often right” while still being economically useless if the calls are timid or late. Conversely, bold calls can look mediocre by hit rate but valuable by tail outcomes. This is why serious evaluation often pairs hit rates with magnitude metrics and drawdown awareness—not because retail investors need PhD statistics, but because honesty requires more than one number.
InsightMeter is oriented around making methodology visible: what was measured, when, and under what constraints. The platform can summarize outcomes, but the ethical core is that summaries should be traceable. If a user cannot trace a headline number back to a definition, the product is asking for mistrust—and reviewers notice that instantly.
A forecast track record is only as honest as its definitions. Ask for the sentence-length claim, lock horizons, disclose exclusions, and treat metrics as a starting point for questions—not a finale.