Mitra / Mitra | News Analytics in Finance | Buch | sack.de

Mitra / Mitra News Analytics in Finance



1. Auflage 2011, 384 Seiten, Gebunden, Format (B × H): 179 mm x 252 mm, Gewicht: 810 g Reihe: Wiley Finance Series
ISBN: 978-0-470-66679-1
Verlag: Wiley & Sons


Mitra / Mitra News Analytics in Finance

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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


Mitra, Gautam
Gautam Mitra (London, UK) is an internationally renowned research scientist in the field of computational optimisation and modelling. He has developed a world class research group in his area of specialisation with researchers from Europe, UK, USA and Asia. He has published three books and over one hundred refereed research articles. He was Head of the Department of Mathematical Sciences, Brunel University between 1990 and 2001. In 2001 he established CARISMA: The Centre for the Analysis of Risk and Optimisation Modelling Applications. CARISMA specialises in the research of risk and optimisation and their combined paradigm in decision modelling. Professor Mitra is also a Director of UNICOM Seminars and OptiRisk Systems; OptiRisk specialises in the research and development of optimisation and financial analytics tools.

Leela Mitra (London, UK) is a Quantitative Analyst at OptiRisk Systems. Dr Mitra joined OptiRisk System as a Quantitative Analyst in 2004. She received her PhD in Operational Research on the topic of "Scenario generation for asset allocation models" from CARISMA, Brunel University. Topics included "mixed" scenario sets for investment decisions with downside risk, pricing and evaluating a bond portfolio using a regime switching Markov model and desirable properties for scenario generation. She has a first class BA (Joint Honours) degree in Mathematics and Philosophy from King's College (University of London). Prior to joining OptiRisk, Leela worked in the pensions industry as an actuarial consultant for Mercer HR and subsequently with Jardine Lloyd Thomson. She is part qualified as an actuary.


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