On June 5th, the model made 20 pattern picks across equities. Directional accuracy came in at 9 of 20, or 45%. The portfolio averaged a -0.09% move across the day. The best individual call was FLR, predicted up 1.41% and delivered 4.12% — a beat of 2.71 percentage points. The worst was WM, predicted up 0.64% but fell 1.93%, missing by 2.57 points in the opposite direction. The hero pick of the day was CARR, flagged as bearish with a predicted move of -4.75%. The stock instead gained 0.28%, creating an absolute error of 5.03 percentage points. This was not a directional miss in a modest way; the forecast and outcome sat on opposite sides of the zero line by a significant margin. CARR exemplifies a scenario where pattern recognition identified sell pressure that did not materialize when the market opened. This result reflects the core challenge in single-day equity prediction: pattern consistency fails roughly half the time even when directional calls appear justified. The 45% hit rate, paired with a near-flat average return and the wide dispersion between best and worst picks, suggests the patterns detected are real enough to occasionally outperform but lack the stability to generate reliable edge. The CARR miss illustrates that even confident bearish setups can reverse. No recalibration is warranted based on one day, but the data continues to show that mean reversion and gap-fill logic—common pattern assumptions—do not hold predictably at the one-day horizon.

2026-06-05: $CARR predicted -4.75%, did +0.28%