Forecasting crude oil futures prices using global macroeconomic news sentiment

We propose a method of incorporating macroeconomic news into a predictive model for forecasting prices of crude oil futures contracts. Since these futures contracts are more liquid than the underlying commodity itself, accurate forecasting of their prices is of great value to multiple categories of market participants. We utilize the Kalman filtering framework for forecasting arbitrage-free (futures) prices and assume that the volatility of oil (futures) price is influenced by macroeconomic news. The impact of quantified news sentiment on the price volatility is modelled through a parametrized, non-linear functional map. This approach is motivated by the successful use of a similar model structure in our earlier work, for predicting individual stock volatility using stock-specific news. We claim the proposed model structure for incorporating macroeconomic news together with historical (market) data is novel and improves the accuracy of price prediction quite significantly. We report results of extensive numerical experiments which justify our claim.