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The Handbook of News Analytics in Finance

Book Description

The Handbook of News Analytics in Finance is a landmark publication bringing together the latest models and applications of News Analytics for asset pricing, portfolio construction, trading and risk control.

The content of the Hand Book is organised to provide a rapid yet comprehensive understanding of this topic. Chapter 1 sets out an overview of News Analytics (NA) with an explanation of the technology and applications. The rest of the chapters are presented in four parts. Part 1 contains an explanation of methods and models which are used to measure and quantify news sentiment. In Part 2 the relationship between news events and discovery of abnormal returns (the elusive alpha) is discussed in detail by the leading researchers and industry experts. The material in this part also covers potential application of NA to trading and fund management. Part 3 covers the use of quantified news for the purpose of monitoring, early diagnostics and risk control. Part 4 is entirely industry focused; it contains insights of experts from leading technology (content) vendors. It also contains a discussion of technologies and finally a compact directory of content vendor and financial analytics companies in the marketplace of NA. The book draws equally upon the expertise of academics and practitioners who have developed these models and is supported by two major content vendors - RavenPack and Thomson Reuters - leading providers of news analytics software and machine readable news.

The book will appeal to decision makers in the banking, finance and insurance services industry. In particular: asset managers; quantitative fund managers; hedge fund managers; algorithmic traders; proprietary (program) trading desks; sell-side firms; brokerage houses; risk managers and research departments will benefit from the unique insights into this new and pertinent area of financial modelling.

Table of Contents

  1. Cover Page
  2. Copyright
  3. Contents
  4. Preface
  5. Acknowledgments
  6. About the editors
  7. About the contributors
  8. Abbreviations and acronyms
  9. Chapter 1: Applications of news analytics in finance: A review
    1. 1.1 INTRODUCTION
    2. 1.2 NEWS DATA
    3. 1.3 TURNING QUALITATIVE TEXT INTO QUANTIFIED METRICS AND TIME-SERIES
    4. 1.4 MODELS AND APPLICATIONS
    5. 1.5 SUMMARY AND DISCUSSIONS
    6. 1.A APPENDIX: STRUCTURE AND CONTENT OF NEWS DATA
    7. 1.B REFERENCES
  10. Part I: Quantifying news: Alternative metrics
    1. Chapter 2: News analytics: Framework, techniques, and metrics
      1. 2.1 PROLOGUE
      2. 2.2 FRAMEWORK
      3. 2.3 ALGORITHMS
      4. 2.4 METRICS
      5. 2.5 DISCUSSION
      6. 2.6 REFERENCES
    2. Chapter 3: Managing real-time risks and returns: The Thomson Reuters NewsScope Event Indices
      1. 3.1 INTRODUCTION
      2. 3.2 LITERATURE REVIEW
      3. 3.3 DATA
      4. 3.4 A FRAMEWORK FOR REAL-TIME NEWS ANALYTICS
      5. 3.5 VALIDATING EVENT INDICES
      6. 3.6 NEWS INDICES AND FX IMPLIED VOLATILITY
      7. 3.7 EVENT STUDY ANALYSIS THROUGH SEPTEMBER 2008
      8. 3.8 CONCLUSION
      9. 3.A APPENDIX
      10. 3.B REFERENCES
    3. Chapter 4: Measuring the value of media sentiment: A pragmatic view
      1. 4.1 INTRODUCTION
      2. 4.2 THE VALUE OF NEWS FOR THE US STOCK MARKET
      3. 4.3 NEWS MOVES MARKETS
      4. 4.4 NEWS MOVES STOCK PRICES
      5. 4.5 NEWS VS. NOISE
      6. 4.6 REGULATED VS. UNREGULATED NEWS
      7. 4.7 THE NEWS COMPONENT OF THE STOCK PRICE
      8. 4.8 MATERIALITY IS NEAR
      9. 4.9 SIZE DOES MATTER
      10. 4.10 CORPORATE SENIOR MANAGEMENT UNDER THE GUN
      11. 4.11 A CASE FOR REGULATED FINANCIAL NEWS MEDIA
      12. 4.12 WALL STREET ANALYSTS MAY CREATE “MATERIAL” NEWS
      13. 4.13 TRADERS MAY CREATE NEWS
      14. 4.14 EARNINGS NEWS RELEASES
      15. 4.15 NEWS SENTIMENT USED FOR TRADING OR INVESTING DECISIONS
      16. 4.16 NEWS SENTIMENT SYSTEMS
      17. 4.17 BACKTESTING NEWS SENTIMENT SYSTEMS
      18. 4.18 THE VALUE OF MEDIA SENTIMENT
      19. 4.19 MEDIA SENTIMENT IN ACTION
      20. 4.20 CONCLUSION
    4. Chapter 5: How news events impact market sentiment
      1. 5.1 INTRODUCTION
      2. 5.2 MARKET-LEVEL SENTIMENT
      3. 5.3 INDUSTRY-LEVEL SENTIMENT
      4. 5.4 CONCLUSION
      5. 5.A MARKET-LEVEL SENTIMENT DATA
      6. 5.B INDUSTRY-LEVEL SENTIMENT DATA
      7. 5.C REFERENCES
  11. Part II: News and abnormal returns
    1. Chapter 6: Relating news analytics to stock returns
      1. 6.1 INTRODUCTION
      2. 6.2 PREVIOUS WORK
      3. 6.3 NEWS DATA STRUCTURE AND STATISTICS
      4. 6.4 IMPROVING NEWS ANALYTICS WITH AGGREGATION
      5. 6.5 REFINING FILTERS USING INTERACTIVE EXPLORATORY DATA ANALYSIS AND VISUALIZATION
      6. 6.6 INFORMATION EFFICIENCY AND MARKET CAPITALIZATION
      7. 6.7 US PORTFOLIO SIMULATION USING NEWS ANALYTIC SIGNALS
      8. 6.8 DISCUSSION OF RNSE AND PORTFOLIO CONSTRUCTION
      9. 6.9 SUMMARY AND AREAS FOR ADDITIONAL RESEARCH
      10. 6.10 ACKNOWLEDGMENTS
      11. 6.11 REFERENCES
    2. Chapter 7: All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors
      1. 7.1 RELATED RESEARCH
      2. 7.2 DATA
      3. 7.3 SORT METHODOLOGY
      4. 7.4 RESULTS
      5. 7.5 SHORT-SALE CONSTRAINTS
      6. 7.6 ASSET PRICING: THEORY AND EVIDENCE
      7. 7.7 CONCLUSION
      8. 7.8 ACKNOWLEDGMENTS
      9. 7.9 REFERENCES
    3. Chapter 8: The impact of news flow on asset returns: An empirical study
      1. 8.1 BACKGROUND AND LITERATURE REVIEW
      2. 8.2 ASPECTS OF NEWS FLOW DATASETS
      3. 8.3 UNDERSTANDING NEWS FLOW DATASETS
      4. 8.4 DOES NEWS FLOW MATTER?
      5. 8.5 NEWS FLOW AND ANALYST REVISIONS
      6. 8.6 DESIGNING A TRADING STRATEGY
      7. 8.7 SUMMARY AND DISCUSSIONS
      8. 8.8 REFERENCES
    4. Chapter 9: Sentiment reversals as buy signals
      1. 9.1 INTRODUCTION
      2. 9.2 THE QUANTIFICATION OF SENTIMENT
      3. 9.3 SENTIMENT REVERSAL UNIVERSES
      4. 9.4 MONTE CARLO–STYLE SIMULATIONS
      5. 9.5 CONCLUSION
      6. 9.6 ACKNOWLEDGMENTS
      7. 9.7 REFERENCES
  12. Part III: News and risk
    1. Chapter 10: Using news as a state variable in assessment of financial market risk
      1. 10.1 INTRODUCTION
      2. 10.2 THE ROLE OF NEWS
      3. 10.3 A STATE-VARIABLE APPROACH TO RISK ASSESSMENT
      4. 10.4 A BAYESIAN FRAMEWORK FOR NEWS INCLUSION
      5. 10.5 CONCLUSIONS
      6. 10.6 REFERENCES
    2. Chapter 11: Volatility asymmetry, news, and private investors
      1. 11.1 INTRODUCTION
      2. 11.2 WHAT CAUSES VOLATILITY ASYMMETRY?
      3. 11.3 WHO MAKES MARKETS VOLATILE?
      4. 11.4 CONCLUSIONS
      5. 11.5 ACKNOWLEDGMENTS
      6. 11.6 REFERENCES
    3. Chapter 12: Firm-specific news arrival and the volatility of intraday stock index and futures returns
      1. 12.1 INTRODUCTION
      2. 12.2 BACKGROUND LITERATURE
      3. 12.3 DATA
      4. 12.4 RESULTS
      5. 12.5 CONCLUSIONS
      6. 12.A TECHNICAL APPENDIX
      7. 12.B REFERENCES
    4. Chapter 13: Equity portfolio risk estimation using market information and sentiment
      1. 13.1 INTRODUCTION AND BACKGROUND
      2. 13.2 MODEL DESCRIPTION
      3. 13.3 UPDATING MODEL VOLATILITY USING QUANTIFIED NEWS
      4. 13.4 COMPUTATIONAL EXPERIMENTS
      5. 13.5 DISCUSSION AND CONCLUSIONS
      6. 13.6 ACKNOWLEDGEMENTS
      7. 13.A SENTIMENT ANALYTICS OVERVIEW
      8. 13.B REFERENCES
  13. Part IV: Industry insights, technology, products, and service providers
    1. Chapter 14: Incorporating news into algorithmic trading strategies: Increasing the signal-to-noise ratio
    2. Chapter 15: Are you still trading without news?
    3. Chapter 16: News analytics in a risk management framework for asset managers
    4. Chapter 17: NORM—towards a new financial paradigm: Behavioural finance with news-optimized risk management
      1. 17.1 INTRODUCTION
      2. 17.2 THE PROBLEM OF INCOMPLETE INFORMATION IN MARKET RISK ASSESSMENT
      3. 17.3 REFINING VaR AND ES CALCULATION USING SEMANTIC NEWS ANALYSIS
      4. 17.4 THE IMPLEMENTATION OF SEMANTIC NEWS ANALYSIS
      5. 17.5 NORM GOALS
      6. 17.6 NORM USES SEMANTIC NEWS ANALYSIS TECHNOLOGY
      7. 17.7 CONCLUSION: NORM CONTRIBUTION TO RISK ASSESSMENT
    5. Chapter 18: Question and answers with Lexalytics
    6. Chapter 19: Directory of news analytics service providers
      1. Company name Event Zero
      2. Company name InfoNgen
      3. Company name Kapow Technologies
      4. Company name Northfield Information Services, Inc.
      5. Company name OptiRisk Systems
      6. Company name RavenPack
      7. Company name SemLab BV
      8. Company name The Chartered Institute for Securities & Investment
      9. Company name Thomson Reuters
  14. Index