Discovering news events that move markets

Abstract

Recently, there has been an explosion of interest in the use of textual sources (e.g., market reports, news articles, company reports) to predict changes in stock and commodity markets. Most of this research is on sentiment analysis, but some of this have tried to use the news itself to predict market movements. In this paper, we use 10-years of news articles — from a weekly, agricultural, trade newspaper — to predict price changes in a commodity market for beef. Two experiments explore the different ways in which news reports affect the market via 1) major market-impacting events (i.e., rare natural disasters or food scandals); or 2) minor market-impacting events (e.g., mundane reports about inflation, oil prices, etc.). We find that different techniques need to be used to uncover major events (e.g., LLRs) as opposed to minor events (e.g., classifiers) and show that no single unified predictive model appears to be able to do both.