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

  • Formatas: PDF+DRM
  • Išleidimo metai: 21-Apr-2015
  • Leidėjas: Springer Vieweg
  • Kalba: eng
  • ISBN-13: 9783658095086
Kitos knygos pagal šią temą:
Value of Social Media for Predicting Stock Returns: Preconditions, Instruments and Performance AnalysisDidesnis paveikslėlis
  • Formatas: PDF+DRM
  • Išleidimo metai: 21-Apr-2015
  • Leidėjas: Springer Vieweg
  • Kalba: eng
  • ISBN-13: 9783658095086
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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.
Foreword v
Acknowledgements vii
List of Figures
xiii
List of Tables
xv
List of Abbreviations
xvii
1 Introduction
1(10)
1.1 Synopsys
1(2)
1.2 Research Contexts
3(3)
1.2.1 Market Efficiency
3(1)
1.2.2 Wisdom of Crowds
4(1)
1.2.3 Mood Analysis
5(1)
1.2.4 Privacy and Security
5(1)
1.3 Structur: of the Dissertation
6(5)
2 Market Anomalies on Two-Sided Auction Platforms
11(16)
Abstract
11(1)
2.1 Introduction
11(2)
2.2 Previous Research
13(3)
2.2.1 Two-Sided Markets
13(1)
2.2.2 Efficient Markets and Market Anomalies
14(2)
2.3 Empirical Study
16(7)
2.3.1 Platform Description
17(1)
2.3.2 Descriptives
17(3)
2.3.3 Analysis
20(3)
2.4 Discussion
23(4)
2.4.1 Limitations and Future Research
25(1)
2.4.2 Conclusion
25(2)
3 Are Crowds on the Internet Wiser than Experts? --- The Case of a Stock Prediction Community
27(36)
Abstract
27(1)
3.1 Introduction
27(3)
3.2 Previous Research
30(6)
3.2.1 Domain Background
30(1)
3.2.2 Theoretical Background
30(6)
3.3 Setup of Empirical Study
36(8)
3.3.1 Data Collection
36(6)
3.3.2 Data Analysis
42(2)
3.4 Results of Empirical Study
44(7)
3.4.1 Comparison of Forecast Accuracy between Professional Analysts and the Crowd
44(4)
3.4.2 Diversity and Independence
48(3)
3.5 Discussion
51(4)
3.5.1 Implications
51(1)
3.5.2 Summary and Outlook
52(3)
3.6 Appendix
55(8)
4 Using Twitter to Predict the Stock Market: Where is the Mood Effect?
63(26)
Abstract
63(1)
4.1 Introduction
63(2)
4.2 Previous Research
65(6)
4.2.1 Behavioral Finance
65(2)
4.2.2 Influence of Mood on Share Returns
67(2)
4.2.3 Predictive Value of Social Media
69(2)
4.3 Empirical Study
71(5)
4.3.1 Data Collection and Method
71(5)
4.4 Results
76(4)
4.4.1 Descriptive Statistics
76(1)
4.4.2 Relationship between Social Mood and the Stock Market
77(1)
4.4.3 Relationship between Follower-Weighted Social Mood and the Stock Market
77(3)
4.5 Trading Strategy
80(2)
4.6 Conclusion
82(3)
4.7 Appendix
85(4)
5 The Economic Impact of Privacy Violations and Security Breaches --- A Laboratory Experiment
89(20)
Abstract
89(1)
5.1 Introduction
89(2)
5.2 Related Work
91(1)
5.3 Theoretical Background
92(4)
5.3.1 Privacy
92(1)
5.3.2 Security
93(2)
5.3.3 Trust
95(1)
5.4 Research Model
96(3)
5.5 Laboratory Experiment
99(6)
5.5.1 Method
99(3)
5.5.2 Results
102(2)
5.5.3 Robustness Check
104(1)
5.6 Discussion
105(3)
5.6.1 Summary
105(1)
5.6.2 Limitations and Future Research
106(2)
5.7 Appendix
108(1)
6 Literature
109
Michael Nofer wrote his dissertation at the Chair of Information Systems | Electronic Markets at TU Darmstadt, Germany.
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