Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.advisorLiu, X-
dc.contributor.advisorMitra, G-
dc.contributor.authorYu, Xiang-
dc.descriptionThis thesis was submitted for the degree of Doctor of philosophy and awarded by Brunel Universityen_US
dc.description.abstractWe 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.en_US
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC) and OptiRisk Systems.en_US
dc.subjectNews analyticsen_US
dc.subjectPredictive modellingen_US
dc.subjectFinancial mathematicsen_US
dc.subjectBehavioural financeen_US
dc.titleAnalysis of new sentiment and its application to financeen_US
Appears in Collections:Dept of Mathematics Theses
Mathematical Sciences

Files in This Item:
File Description SizeFormat 
Full text Thesis.pdf3.09 MBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.