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Value of Social Media for Predicting Stock Returns : Preconditions, Instruments and Performance Analysis

By: Nofer, MichaelMaterial type: TextTextSeries: eBooks on DemandPublisher: Wiesbaden : Springer Fachmedien Wiesbaden, 2015Description: 1 online resource (140 p.)ISBN: 9783658095086Subject(s): Computer science | Data mining | Finance | Management information systemsGenre/Form: Electronic books.Additional physical formats: Print version:: Value of Social Media for Predicting Stock Returns : Preconditions, Instruments and Performance AnalysisDDC classification: 006.312 LOC classification: QA76.9.D343Online resources: Click here to view this ebook.
Contents:
""Foreword""; ""Acknowledgements""; ""Table of Contents""; ""List of Figures""; ""List of Tables""; ""List of Abbreviations""; ""1 Introduction""; ""1.1 Synopsys""; ""1.2 Research Contexts""; ""1.2.1 Market Efficiency""; ""1.2.2 Wisdom of Crowds""; ""1.2.3 Mood Analysis""; ""1.2.4 Privacy and Security""; ""1.3 Structure of the Dissertation""; ""2 Market Anomalies on Two-Sided Auction Platforms""; ""Abstract""; ""2.1 Introduction""; ""2.2 Previous Research""; ""2.2.1 Two-Sided Markets""; ""2.2.2 Efficient Markets and Market Anomalies""; ""2.3 Empirical Study""; ""2.3.1 Platform Description""
""2.3.2 Descriptives""""2.3.3 Analysis""; ""2.4 Discussion""; ""2.4.1 Limitations and Future Research""; ""2.4.2 Conclusion""; ""3 Are Crowds on the Internet Wiser than Experts? � The Case of a Stock Prediction Community""; ""Abstract""; ""3.1 Introduction""; ""3.2 Previous Research""; ""3.2.1 Domain Background""; ""3.2.2 Theoretical Background""; ""3.3 Setup of Empirical Study""; ""3.3.1 Data Collection""; ""3.3.2 Data Analysis""; ""3.4 Results of Empirical Study""; ""3.4.1 Comparison of Forecast Accuracy between Professional Analysts and the Crowd""; ""3.4.2 Diversity and Independence""
""3.5 Discussion""""3.5.1 Implications""; ""3.5.2 Summary and Outlook""; ""3.6 Appendix""; ""4 Using Twitter to Predict the Stock Market: Where is the Mood Effect?""; ""Abstract""; ""4.1 Introduction""; ""4.2 Previous Research""; ""4.2.1 Behavioral Finance""; ""4.2.2 Influence of Mood on Share Returns""; ""4.2.3 Predictive Value of Social Media""; ""4.3 Empirical Study""; ""4.3.1 Data Collection and Method""; ""4.4 Results""; ""4.4.1 Descriptive Statistics""; ""4.4.2 Relationship between Social Mood and the Stock Market""
""4.4.3 Relationship between Follower-Weighted Social Mood and the Stock Market""""4.5 Trading Strategy""; ""4.6 Conclusion""; ""4.7 Appendix""; ""5 The Economic Impact of Privacy Violations and Security Breaches � A Laboratory Experiment1""; ""Abstract""; ""5.1 Introduction""; ""5.2 Related Work""; ""5.3 Theoretical Background""; ""5.3.1 Privacy""; ""5.3.2 Security""; ""5.3.3 Trust""; ""5.4 Research Model""; ""5.5 Laboratory Experiment""; ""5.5.1 Method""; ""5.5.2 Results""; ""5.5.3 Robustness Check""; ""5.6 Discussion""; ""5.6.1 Summary""; ""5.6.2 Limitations and Future Research""
""5.7 Appendix""""6 Literature""
Summary: Michael Nofer examines whether and to what extent Social Media can be used to predict stock returns. Market-relevant information is available on various platforms on the Internet, which largely consist of user generated content. For instance, emotions can be extracted in order to identify the investors' risk appetite and in turn the willingness to invest in stocks. Discussion forums also provide an opportunity to identify opinions on certain companies. Taking Social Media platforms as examples, the author examines the forecasting quality of user generated content on the Internet.
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Item type Current location Call number URL Status Date due Barcode
Electronic Book UT Tyler Online
Online 		QA76.9.D343 (Browse shelf) http://uttyler.eblib.com/patron/FullRecord.aspx?p=2094759 Available EBL2094759

""Foreword""; ""Acknowledgements""; ""Table of Contents""; ""List of Figures""; ""List of Tables""; ""List of Abbreviations""; ""1 Introduction""; ""1.1 Synopsys""; ""1.2 Research Contexts""; ""1.2.1 Market Efficiency""; ""1.2.2 Wisdom of Crowds""; ""1.2.3 Mood Analysis""; ""1.2.4 Privacy and Security""; ""1.3 Structure of the Dissertation""; ""2 Market Anomalies on Two-Sided Auction Platforms""; ""Abstract""; ""2.1 Introduction""; ""2.2 Previous Research""; ""2.2.1 Two-Sided Markets""; ""2.2.2 Efficient Markets and Market Anomalies""; ""2.3 Empirical Study""; ""2.3.1 Platform Description""

""2.3.2 Descriptives""""2.3.3 Analysis""; ""2.4 Discussion""; ""2.4.1 Limitations and Future Research""; ""2.4.2 Conclusion""; ""3 Are Crowds on the Internet Wiser than Experts? � The Case of a Stock Prediction Community""; ""Abstract""; ""3.1 Introduction""; ""3.2 Previous Research""; ""3.2.1 Domain Background""; ""3.2.2 Theoretical Background""; ""3.3 Setup of Empirical Study""; ""3.3.1 Data Collection""; ""3.3.2 Data Analysis""; ""3.4 Results of Empirical Study""; ""3.4.1 Comparison of Forecast Accuracy between Professional Analysts and the Crowd""; ""3.4.2 Diversity and Independence""

""3.5 Discussion""""3.5.1 Implications""; ""3.5.2 Summary and Outlook""; ""3.6 Appendix""; ""4 Using Twitter to Predict the Stock Market: Where is the Mood Effect?""; ""Abstract""; ""4.1 Introduction""; ""4.2 Previous Research""; ""4.2.1 Behavioral Finance""; ""4.2.2 Influence of Mood on Share Returns""; ""4.2.3 Predictive Value of Social Media""; ""4.3 Empirical Study""; ""4.3.1 Data Collection and Method""; ""4.4 Results""; ""4.4.1 Descriptive Statistics""; ""4.4.2 Relationship between Social Mood and the Stock Market""

""4.4.3 Relationship between Follower-Weighted Social Mood and the Stock Market""""4.5 Trading Strategy""; ""4.6 Conclusion""; ""4.7 Appendix""; ""5 The Economic Impact of Privacy Violations and Security Breaches � A Laboratory Experiment1""; ""Abstract""; ""5.1 Introduction""; ""5.2 Related Work""; ""5.3 Theoretical Background""; ""5.3.1 Privacy""; ""5.3.2 Security""; ""5.3.3 Trust""; ""5.4 Research Model""; ""5.5 Laboratory Experiment""; ""5.5.1 Method""; ""5.5.2 Results""; ""5.5.3 Robustness Check""; ""5.6 Discussion""; ""5.6.1 Summary""; ""5.6.2 Limitations and Future Research""

""5.7 Appendix""""6 Literature""

Michael Nofer examines whether and to what extent Social Media can be used to predict stock returns. Market-relevant information is available on various platforms on the Internet, which largely consist of user generated content. For instance, emotions can be extracted in order to identify the investors' risk appetite and in turn the willingness to invest in stocks. Discussion forums also provide an opportunity to identify opinions on certain companies. Taking Social Media platforms as examples, the author examines the forecasting quality of user generated content on the Internet.

Description based upon print version of record.

Author notes provided by Syndetics

Michael Nofer wrote his dissertation at the Chair of Information Systems | Electronic Markets at TU Darmstadt, Germany.

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