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Preface. Acknowledgements. About the editors. About the contributors. Abbreviations and acronyms. 1 Applications of news analytics in finance: A review (Leela Mitra and Gautam Mitra). 1.1 Introduction. 1.2 News data. 1.3 Turning qualitative text into quantified metrics and time-series. 1.4 Models and applications. 1.5 Summary and discussions. PART I QUANTIFYING NEWS: ALTERNATIVE METRICS. 2 News analytics: Framework, techniques, and metrics (Sanjiv R. Das). 2.1 Prologue. 2.2 Framework. 2.3 Algorithms. 2.4 Metrics. 2.5 Discussion. 2.6 References. 3 Managing real-time risks and returns: The Thomson Reuters NewsScope Event Indices (Alexander D. Healy and Andrew W. Lo). 3.1 Introduction. 3.2 Literature review. 3.3 Data. 3.4 A framework for real-time news analytics. 3.5 Validating Event Indices. 3.6 News indices and FX implied volatility. 3.7 Event study analysis through September 2008. 3.8 Conclusion. 4 Measuring the value of media sentiment: A pragmatic view (Marion Munz). 4.1 Introduction. 4.2 The value of news for the US stock market. 4.3 News moves markets. 4.4 News moves stock prices. 4.5 News vs. noise. 4.6 Regulated vs. unregulated news. 4.7 The news component of the stock price. 4.8 Materiality is near. 4.9 Size does matter. 4.10 Corporate senior management under the gun. 4.11 A case for regulated financial news media. 4.12 Wall Street analysts may create material news. 4.13 Traders may create news. 4.14 Earnings news releases. 4.15 News sentiment used for trading or investing decisions. 4.16 News sentiment systems. 4.17 Backtesting news sentiment systems. 4.18 The value of media sentiment. 4.19 Media sentiment in action. 4.20 Conclusion. 5 How news events impact market sentiment (Peter Ager Hafez). 5.1 Introduction. 5.2 Market-level sentiment. 5.3 Industry-level sentiment. 5.4 Conclusion. PART II NEWS AND ABNORMAL RETURNS. 6 Relating news analytics to stock returns (David Leinweber and Jacob Sisk). 6.1 Introduction. 6.2 Previous work. 6.3 News data structure and statistics. 6.4 Improving news analytics with aggregation. 6.5 Refining filters using interactive exploratory data analysis and visualization. 6.6 Information efficiency and market capitalization. 6.7 US portfolio simulation using news analytic signals. 6.8 Discussion of RNSE and portfolio construction. 6.9 Summary and areas for additional research. 6.10 Acknowledgments. 6.11 References. 7 All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors (Brad M. Barber and Terrance Odean). 7.1 Related research. 7.2 Data. 7.3 Sort methodology. 7.4 Results. 7.5 Short-sale constraints. 7.6 Asset pricing: Theory and evidence. 7.7 Conclusion. 7.8 Acknowledgments. 7.9 References. 8 The impact of news flow on asset returns: An empirical study (Andy Moniz, Gurvinder Brar, Christian Davies, and Adam Strudwick). 8.1 Background and literature review. 8.2 Aspects of news flow datasets. 8.3 Understanding news flow datasets. 8.4 Does news flow matter? 8.5 News flow and analyst revisions. 8.6 Designing a trading strategy. 8.7 Summary and discussions. 8.8 References. 9 Sentiment reversals as buy signals (John Kittrell). 9.1 Introduction. 9.2 The quantification of sentiment. 9.3 Sentiment reversal universes. 9.4 Monte Carlo style simulations. 9.5 Conclusion. 9.6 Acknowledgments. 9.7 References. PART III NEWS AND RISK. 10 Using news as a state variable in assessment of financial market risk (Dan diBartolomeo). 10.1 Introduction. 10.2 The role of news. 10.3 A state-variable approach to risk assessment. 10.4 A Bayesian framework for news inclusion. 10.5 Conclusions. 10.6 References. 11 Volatility asymmetry, news, and private investors (Michal Dzielinski, Marc Oliver Rieger, and Tonn Talpsepp). 11.1 Introduction. 11.2 What causes volatility asymmetry? 11.3 Who makes markets volatile? 11.4 Conclusions. 11.5 Acknowledgments. 11.6 References. 12 Firm-specific news arrival and the volatility of intraday stock index and futures returns (Petko S. Kalev and Huu Nhan Duong). 12.1 Introduction. 12.2 Background literature. 12.3 Data. 12.4 Results. 12.5 Conclusions. 13 Equity portfolio risk estimation using market information and sentiment (Leela Mitra, Gautam Mitra, and Dan diBartolomeo). 13.1 Introduction and background. 13.2 Model description. 13.3 Updating model volatility using quantified news. 13.4 Computational experiments. 13.5 Discussion and conclusions. 13.6 Acknowledgements. PART IV INDUSTRY INSIGHTS, TECHNOLOGY, PRODUCTS AND SERVICE PROVIDERS. 14 Incorporating news into algorithmic trading strategies: Increasing the signalto-noise ratio (Richard Brown). So, how can one incorporate news into algorithmic strategies to improve trading performance? So, how does one increase the signal-to-noise ratio, ensuring protection from unforeseen exposures without an excessive number of halts or items to review? Sounds logical, right? So how exactly can this be done? So what about offensive strategies? How can one generate alpha using news? 15 Are you still trading without news? (Armando Gonzalez). The underpinnings of news analytics. Quantcentration and news. Detecting news events automatically. Finding liquidity in the news. 16 News analytics in a risk management framework for asset managers (Dan diBartolomeo). 17 NORM towards a new financial paradigm: Behavioural finance with newsoptimized risk management (Mark Vreijling and Thomas Dohmen). 17.1 Introduction. 17.2 The problem of incomplete information in market risk assessment. 17.3 Refining VaR and ES calculation using semantic news analysis. 17.4 The implementation of semantic news analysis. 17.5 NORM goals. 17.6 NORM uses semantic news analysis technology. 17.7 Conclusion: NORM contribution to risk assessment. 18 Question and answers with Lexalytics (Jeff Catlin). 19 Directory of news analytics service providers. Event Zero. InfoNgen. Kapow Technologies. Northfield Information Services, Inc. OptiRisk Systems. RavenPack. SemLab BV. The Chartered Institute for Securities & Investment. Thomson Reuters. Index.
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