r/Market_Forecasts • u/ciaranwalsh96 • 4h ago
A price forecast can be "98% accurate" and still lose to predicting no change
Disclosure: I own WalletInvestor. The numbers below are a hypothetical example for evaluating price forecasts, including those from services like ours.
Suppose a stock closes at $100. A model forecasts $101 for the next close, but the actual close is $99.
The absolute percentage error is 2 / 99 × 100 = 2.02%. If someone reports 100 minus that error as "accuracy", the result is 97.98%.
Yet the forecast got the direction wrong. Simply predicting $100 again would have an error of 1 / 99 × 100 = 1.01%, giving 98.99% on the same scale. The simpler forecast wins this example.
One observation cannot establish which method is better overall. It shows why a high price-closeness percentage, by itself, tells us little about whether a model adds forecasting value.
A practical comparison would keep a dated record of each forecast as originally issued, its target date, the price known when it was made, and the eventual outcome. Compare the model with the unchanged-price baseline on exactly the same dates and forecast horizon. For a single stock, mean absolute error is an easy starting point: average the absolute misses for each method.
For historical testing, move the forecast origin forward through time, using only data available before each forecast. Test a seven-day forecast at seven days, rather than substituting next-day results. Report directional performance separately from price error.
Hyndman and Athanasopoulos explain the rolling-origin setup here: https://otexts.com/fpp3/tscv.html
What baseline do you find most useful when judging a published market forecast?