We report our investigation of how news stories influence the behaviour of tradable financial assets, in particular, equities. We consider the established methods of turning news events into a quantifiable measure and explore the models which connect these measures to financial decision making and risk control. The study of our thesis is built around two practical, as well as, research problems which are determining trading strategies and quantifying trading risk. We have constructed a new measure which takes into consideration (i) the volume of news and (ii) the decaying effect of news sentiment. In this way we derive the impact of aggregated news events for a given asset; we have defined this as the impact score. We also characterise the behaviour of assets using three parameters, which are return, volatility and liquidity, and construct predictive models which incorporate impact scores. The derivation of the impact measure and the characterisation of asset behaviour by introducing liquidity are two innovations reported in this thesis and are claimed to be contributions to knowledge. The impact of news on asset behaviour is explored using two sets of predictive models: the univariate models and the multivariate models. In our univariate predictive models, a universe of 53 assets were considered in order to justify the relationship of news and assets across 9 different sectors. For the multivariate case, we have selected 5 stocks from the financial sector only as this is relevant for the purpose of constructing trading strategies. We have analysed the celebrated Black-Litterman model (1991) and constructed our Bayesian multivariate predictive models such that we can incorporate domain expertise to improve the predictions. Not only does this suggest one of the best ways to choose priors in Bayesian inference for financial models using news sentiment, but it also allows the use of current and synchronised data with market information. This is also a novel aspect of our work and a further contribution to knowledge.

Automated event extraction from free text remains an open problem, particularly when the goal is to identify all relevant events. Manual extraction is currently the only alternative for comprehensive and reliable extraction. Therefore, it is required to have a system that can comprehensively extract events reported in news articles (high recall) and is also scalable enough to handle a large number of articles. In this dissertation, we explore various methods to develop an event extraction system that can mitigate these challenges. We primarily investigate three major problems related to event extraction as follows. (i) What are the strengths and weaknesses of the automated event extractors? A thorough understanding of what can be automated with high success and what leads to common pitfalls is crucial before we could develop a superior event extraction system. (ii) How can we build a hybrid event extraction system that can bridge the gap between manual and automated event extraction? Hybrid extraction is a semi-automated approach that uses an ecosystem of machine learning models along with a carefully designed user interface for extracting events. Since this method is semi-automated it also requires a meticulous understanding of user behavior in order to identify tasks that humans can perform with ease while diverting the more tedious task to the machine learning methods (iii) Finally, we explore methods for displaying extracted events that could simplify the analytical and inference generation processes for an analyst. We particularly aim to develop visualizations that would allow analysts can perform macro and micro level analysis of significant societal events. / Ph. D.