A researcher at The University of Texas at Dallas has developed a mathematical model that uses events described in news articles to predict more accurately the prices of natural resources and raw materials. Commonly referred to as commodities, prices for agricultural products, energy products and metals are driven by events such as tax increases, strikes, natural disasters and wars. Their model captures this structure by identifying “event triggers,” or important words and phrases signaling the type and occurrence of events driving price fluctuations. Their new model outperformed several benchmark models by as much as 13% by leveraging the news articles’ structure. He said the model also can be used to identify the key events driving price fluctuations, lending interpretability to the forecasts.