Multivariate Hawkes Process Modeled News Flow: Forecasting Financial Markets

Abstract: Within the quantitative financial community there are a lot of different approaches in forming profitable trading strategies. This is frequently performed by analyzing historical prices from different perspectives. Some have analyzed other factors than price that might provide insight in which way the market is heading, which in some cases have been successful. This thesis investigates if a news flow model based on a multivariate Hawkes process could give a peek into the future news flow, and if it can be used to successfully predict financial market movements in terms of logarithmic returns by utilizing regression and classification models such as support vector machines. The results show that the trained models perform poorly in general in terms of common regression and classification metrics. Applying the trained models in simple trading strategies show that in some cases they perform better than a buy-and-hold strategy. The ambiguous results indicate that the models might be profitable in trading strategies, but that the predictions might not be very reliable. The trained models cannot seem to find important structures in the predicted news flow relating to market returns, but before dismissing the news flow model entirely it might altered in some sense by, e.g., expanding the dataset with more observations and by looking at other granularities of time.