Big Data Meets the Turbulent Oil Market

We use topic modeling to construct novel news-based measures for tracking energy markets. Our parsimonious yet comprehensive set of indicators summarizes the information content of millions of news articles and forecasts oil spot, futures, and energy company stock returns, and changes in oil volatility, production, and inventories. Using an econometrically robust framework to evaluate both in- and out-of-sample predictive performance, we show that our measures are not spanned by existing text and nontext variables. A version of our text-based measures derived from rolling topic models delivers economically meaningful out-of-sample forecasts.