MAPE — Mean Absolute Percentage Error — measures how far off the model's point forecast has historically been from what actually happened, on this ticker's own out-of-sample backtest.
MAPE is computed by rewinding the model to an earlier date, forecasting forward, and comparing the mean forecast to what the price actually did. Lower MAPE means a more accurate point forecast — roughly, under 10% is strong, 10–25% is workable, and above 25% signals a genuinely hard-to-forecast name.
Live example: AAPL's 3-month MAPE from the last backtest run is 17.8% — the average absolute error of the model's point forecast versus the realized price on this ticker's own history. See the full AAPL forecast.
MAPE is measured per ticker and per horizon, because forecastability genuinely differs across names — a stable, low-beta consumer staple typically backtests to a lower MAPE than a high-volatility growth name or a stock mid-way through a structural business change. We publish MAPE per ticker rather than one blended average so this dispersion is visible.
Roughly: under 10% is strong, 10-25% is workable, and above 25% signals a genuinely hard-to-forecast name. MAPE varies a lot by ticker.
No. MAPE grades point-forecast accuracy; CI90 coverage grades whether the band's stated 90% actually holds. A model can be accurate on average yet overconfident about its own uncertainty.
By rewinding the model to an earlier date, generating a forecast, and comparing it to what actually happened — repeated across the historical backtest window.
AAPL analysis shows this metric in context, or browse all S&P 500 tickers.
Educational research only — not investment advice.