Kpler Refinery product - Earnings forecast model showcase
Exponential Technology & Kpler have produced a white paper demonstrating how Kpler's daily refinery throughput and production data can be converted into actionable, pre-announcement earnings forecasts for major refiners with strong trading signals.
Trade refiners with confidence before they report
This paper demonstrates a systematic methodology for forecasting public refinery company earnings using Kpler's daily operational refinery data combined with ensemble machine learning techniques developed by Exponential Technology. Applied to three major U.S. refiners—Phillips 66 (PSX), Valero Energy (VLO), and PBF Energy (PBF)—the framework achieves mean absolute percentage errors (MAPE) of 3.13%, 1.86%, and 3.15% for quarterly production forecasts, respectively, and 3.10%, 4.30%, and 5.80% for quarterly refining revenue forecasts.
Key Takeaways
- Low-error KPI forecasts across PSX, VLO, and PBF using daily refinery data and ensemble ML
- Refining-revenue forecasts in low single-digit MAPE, powered by a unique “crack-ratio” method
- Forward-validated trade example tied directly to pre-announcement model
- Cumulative back test performance across multiple quarters and names
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