About

CommodityNode Editorial Team

Our Research Approach

CommodityNode publishes commodity market intelligence under the CommodityNode Research team name, representing a systematic, data-driven approach to mapping commodity price ripple effects across industries, companies, and portfolios.

Every analysis on the platform is produced following a consistent methodology grounded in quantitative data — not editorial opinion. Our research draws on regulatory filings (SEC 10-K/10-Q), official commodity data (IEA, USDA, LME, WBMS), central bank publications, and historical price regression analysis.

Editorial Standards

What We Publish

  • Commodity Impact Maps — How a 10% price move in a raw material ripples through industries, ETFs, and companies, based on historical regression and supply-chain input-output analysis
  • Signal Reports — Specific price movements or structural market changes with quantified impact estimates, historical analogues, and relevant equities
  • Cross-Commodity Signals — Historically validated ratio and spread patterns with documented win rates and time horizons
  • AI Price Forecasts — 90-day projections powered by Amazon Chronos-2 time series models with P10/P90 confidence bands

What We Don't Do

  • We do not manage money or provide personalized investment advice
  • We do not receive payments to cover specific commodities or companies
  • We do not publish price targets without historical basis and stated methodology
  • We do not use unverified statistics or make claims without data sources

Data Sources & Credentials

Our analysis is grounded in primary data sources including:

  • Price data: Yahoo Finance API (real-time), CME Group, LME, ICE
  • Fundamentals: SEC EDGAR (10-K, 10-Q filings), company investor relations
  • Commodity data: International Energy Agency (IEA), USDA World Agricultural Supply and Demand Estimates (WASDE), World Bureau of Metal Statistics (WBMS), London Metal Exchange (LME)
  • Macro data: Federal Reserve FRED, BLS, central bank publications
  • AI forecasting: Amazon Chronos-2 time series foundation model

Corrections Policy

If you identify a factual error in our analysis, please contact us at contact@commoditynode.com. We review all correction requests within 48 hours and publish updates prominently on the affected page.

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