ANALYSIS OF NEWS SENTIMENT AND ITS APPLICATION TO FINANCE By Xiang Yu A thesis submitted for the degree of Doctor of Philosophy School of Information Systems, Computing and Mathematics, Brunel University 6 May 2014 Dedication: To the loving memory of my mother. Abstract 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. i Acknowledgements First and foremost, I thank my supervisors Prof. Gautam Mitra and Prof. XiaoHui Liu for their continuous support and guidance during these three and a half years. It has been the most comforting thought to know that they are here to help me. I am grateful for the financial support provided to me by the Engineering and Physical Sciences Research Council (EPSRC) and OptiRisk Systems. The Department of Mathematical Sciences at Brunel University has offered me an excellent environment in which to carry out my research and more importantly given me the opportunity to meet colleagues who have become my close friends. Most importantly, I thank my family for their unconditional love and support, especially my father for directing me along this educational path. ii Table of Contents Abstract .......................................................................................................................... i Acknowledgements…………………………………………………………..……….ii List of Figures ............................................................................................................... v List of Tables .............................................................................................................. vii Abbreviations and Acronyms ................................................................................. viii 1. Introduction .............................................................................................................. 1 1.1 Focus of the Thesis .............................................................................................. 1 1.2 News as an Event ............................................................................................... 1 1.3 Sentiment and Its Evolution ............................................................................... 5 1.4 The Power of Unstructured Text...................................................................... 12 1.5 News and Its Use in Fund Management and Trading ...................................... 20 1.6 Thesis Outline and Contributions .................................................................... 23 2. Market Microstructure, Liquidity and Automated Trading ............................. 25 2.1 News and Its Relationship with Trading ........................................................ 25 2.1.1 Trading Approaches Influenced by News.............................................. 30 2.2 Market Microstructure ..................................................................................... 34 2.3 Liquidity: Measures and Implications ........................................................... 37 3. News Sentiment and Its Market Impact .............................................................. 45 3.1 News Metadata................................................................................................... 45 3.2 Sentiment score .............................................................................................. 49 3.3 News flow ...................................................................................................... 52 3.4 Impact score ................................................................................................... 53 4. Univariate Predictive Model for Asset Behaviour .............................................. 63 4.1 Introduction .................................................................................................. 63 4.2 Data .............................................................................................................. 66 4.3 The Predictive Model ................................................................................... 73 iii 4.4 Computational Results and Validation ........................................................ 77 4.5 Summary ...................................................................................................... 91 5. Multivariate Predictive Model using Bayesian Inference .................................. 92 5.1 Introduction ..................................................................................................... 92 5.2 The Models ..................................................................................................... 97 5.3 Prior Selection and Posterior Distributions................................................... 100 5.4 Data ................................................................................................................ 104 5.5 Computational Results and Validation .......................................................... 104 5.6 Summary ....................................................................................................... 113 6. Conclusions ........................................................................................................... 114 6.1 Summary .......................................................................................................... 114 6.2 Conclusions and Contributions ........................................................................ 115 6.3 Future Research ............................................................................................... 116 References ................................................................................................................. 118 Appendices ................................................................................................................ 129 iv List of Figures 1.1: Firms’ cumulative standardized unexpected earnings. .................................... 21 2.1: The trade-off between optimal trading frequency and liquidity for various trading instruments. ................................................................................................. 28 2.2: Progress in adoption of algorithmic execution by asset class from 2004 - 2010. Source: Aite Group. ................................................................................................ 29 2.3: Kyle’s λ values calculated for all trades on Barclays and AIG in the day 1 June 2010. ........................................................................................................................ 40 2.4: The market-efficient coefficient (MEC) calculated for a handful of US and UK stocks. ...................................................................................................................... 42 2.5: Illiquidity ratios for a handful of US and UK stocks. ...................................... 43 3.1: An outline of information flow and modelling architecture of news metadata ................................................................................................................................. 48 3.2: A representation of news sentiment decay. ..................................................... 55 3.3: The bid price and cumulated sentiment scores for AIG for August 2008. ...... 56 3.4: The half-life decay of sentiment scores for AIG over the month of September 2008. ........................................................................................................................ 58 3.5: The decay of sentiment scores for AIG with different decay rates.................. 60 3.6: JP Morgan August 2008: News impact score for positive and negative sentiment