C.H. Robinson Worldwide, Inc.
CHRW scores 62.8 on the Conservative profile, blending a fundamental score (80% weight, emphasizing quality and stability (84% of fundamental weight)) with a machine-learning signal (20% weight) trained on 82 features across 30 years of data.
Minor adjustments (-1.6 points) applied. CHRW's rank of #246 primarily reflects its factor profile relative to the conservative weight vector.
These features are direct inputs to the machine learning model. The model was trained on these signals alongside 100 features (including 12 momentum/technical indicators) to produce the ML percentile score.
No risk factors identified.
| Stock | Score | P/E | Rev Growth | Margin | Mkt Cap |
|---|---|---|---|---|---|
| CHRW | 62.8 | 36.5 | -8.4% | 3.6% | $20.8B |
| FER | 83.7 | 12.2 | 7.4% | 35.3% | $51.9B |
| NDSN | 76.3 | 35.1 | 3.8% | 17.4% | $16.6B |
| OMAB | 76.1 | 20.1 | 4.3% | 32.7% | $6.2B |
| ODFL | 75.8 | 39.8 | -0.9% | 20.4% | $40.6B |
| FAST | 72.7 | 42.6 | 8.7% | 15.3% | $53.3B |
| WWD | 70.9 | 47.8 | 7.3% | 12.4% | $22.8B |
| CSX | 70.9 | 26.5 | -3.1% | 20.5% | $76.0B |
| LECO | 70.9 | 31.5 | -4.4% | 11.6% | $16.2B |
| CTAS | 70.4 | 41.9 | 7.7% | 17.5% | $77.7B |
| PCAR | 70.4 | 28.2 | -4.2% | 12.4% | $66.7B |
| RYAAY | 69.7 | 13.0 | 3.8% | 11.6% | $34.3B |
| CPRT | 69.1 | 23.0 | 9.7% | 33.4% | $36.3B |
| FELE | 68.5 | 35.5 | -2.1% | 8.9% | $4.8B |
| AEIS | 67.8 | 81.2 | 21.4% | 8.3% | $11.9B |
| IESC | 67.7 | 30.9 | 16.9% | 9.1% | $10.3B |
| Sector Average | 45.2 | 64.5 | 19.0% | -48.2% | â |
| Year | Low | High | Range | Status |
|---|---|---|---|---|
| 2021 | $84.67 | $108.06 | 24.3% | Narrow |
| 2022 | $86.57 | $121.23 | 33.4% | Moderate |
| 2023 | $78.69 | $108.05 | 31.4% | Moderate |
| 2024 | $65.00 | $114.82 | 55.4% | Wide |
| 2025 | $84.68 | $167.90 | 65.9% | Wide |
Growth estimates have been dampened based on technical and fundamental signals. This is a post-hoc adjustment to prevent overly optimistic projections for stocks showing declining momentum or deteriorating fundamentals.
Scores are generated by a multi-stage ML pipeline combining fundamental analysis, ensemble predictions, and structural risk signals. All data is for research purposes only and does not constitute financial advice. Past performance does not guarantee future results.