Trader Positioning and Agricultural Price Risk

Traders and researchers often use the weekly Commitments of Traders (COT) reports to look for signals about future movements in agricultural futures prices. But these signals are only useful for forecasting if the trader-position data were actually available when the forecast was made and if the relationship between positions and prices remains stable over time. This study examines both issues and shows what can happen when either condition is ignored. COT positions are normally released three days after they are measured. However, following the federal funding lapse in 2025, one report was scheduled for release 50 days after its measurement date. Using a simple forecasting model that allows for structural change, the analysis shows that when trader positions are not persistent, a coefficient estimated from an earlier period outperforms the historical mean only when the new coefficient keeps the same sign and remains more than half as large. A Monte Carlo experiment covering six settings with 800 replications each supports this result. The simulations also show how easily forecasting performance can be overstated. A model that uses trader positions before they were publicly available passes a one-sided predictive-accuracy test in 99 percent of samples, even when no usable public signal exists. For a weak but genuine public signal, the Clark and West test detects predictive improvement in 79 percent of samples, compared with 37 percent for the Diebold and Mariano test. Selecting the best performer from 50 irrelevant position measures also produces an apparent validation gain that turns into a loss when evaluated on new data. After a sign reversal, a 52-week rolling window reduces mean squared error from 2.80 to 1.54. However, when the underlying signal is weak but stable, the same rolling-window approach performs worse than the fixed-window historical mean.