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Essays on the Information Content of Microblogs and Their Use As an Indicator of Real-World Events

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Essays on the Information Content of Microblogs and Their Use As an Indicator of Real-World Events TECHNISCHE UNIVERSITÄT MÜNCHEN Lehrstuhl für Betriebswirtschaftslehre – Strategie und Organisation Univ.-Prof. Dr. Isabell Welpe Essays on the Information Content of Microblogs and their Use as an Indicator of Real-World Events Timm O. Sprenger Vollständiger Abdruck der von der Fakultät für Wirtschaftswissenschaften der Technischen Universität München zur Erlangung des akademischen Grades eines Doktors der Wirtschaftswissenschaften (Dr. rer. pol.) genehmigten Dissertation. Vorsitzender: Univ.-Prof. Dr. Joachim Henkel Prüfer der Dissertation: 1. Univ.-Prof. Dr. Isabell Welpe 2. Univ.-Prof. Dr. Christoph Kaserer Die Dissertation wurde am 09.06.2011 bei der Technischen Universität München eingereicht und durch die Fakultät für Wirtschaftswissenschaften am 20.07.2011 angenommen. “What we have to do is deliver to people the best and freshest most relevant information possible. We think of Twitter not as a social network, but an information network.” Twitter co-founder Evan Williams Tables of Contents I Table of Contents - Overview Table of Contents - Details II I. Introduction 1 II. Essays 1. Predicting Elections with Twitter: How 140 Characters Reflect the Political Landscape 9 2. Tweets and Trades: The Information Content of Stock Microblogs 38 3. News or Noise? The Stock Market Reaction to Different Types of Company-Specific News Events 101 4. Followers and Foes: Industry Classification based on Investor Perceptions of Strategic Peer Groups 153 5. TweetTrader.net: Leveraging Crowd Wisdom in a Stock Microblogging Forum 186 III. Conclusion 194 II Tables of Contents Table of Contents - Details List of Figures........................................................................................................................VI List of Tables........................................................................................................................ VII List of Abbreviations.............................................................................................................IX I. Introduction.......................................................................................................................... 1 1 Motivation......................................................................................................................... 1 2 Structure of this dissertation, key findings and contributions........................................... 4 II. Essays .................................................................................................................................. 9 II.1 Predicting Elections with Twitter................................................................................... 9 1 Introduction ................................................................................................................... 10 2 Background.................................................................................................................... 11 2.1 The German election............................................................................. 11 2.2 Related work and research questions .................................................... 11 2.3 Microblogging forums as information markets..................................... 14 3 Data set and methodology............................................................................................. 16 4 Results ............................................................................................................................ 18 4.1 Twitter as a platform for political deliberation ..................................... 18 4.2 Twitter sentiment as a reflection of the political landscape offline ...... 19 4.3 Party bias of individual users ................................................................ 24 4.4 Twitter as a predictor of the election result........................................... 25 5 Conclusion...................................................................................................................... 28 5.1 Discussion of results.............................................................................. 28 5.2 Limitations and further research ........................................................... 29 6 Appendix ........................................................................................................................ 32 6.1 Political deliberation on Twitter............................................................ 32 6.2 Changes of sentiment over time............................................................ 32 6.3 Distribution of user attention................................................................. 33 Tables of Contents III 6.4 Forecast accuracy of traditional methods.............................................. 34 7 References ...................................................................................................................... 35 II.2 Tweets and Trades ......................................................................................................... 38 1 Introduction ................................................................................................................... 39 2 Related work and research questions.......................................................................... 43 2.1 Introduction to the research of online stock forums.............................. 43 2.2 Research questions, related research and hypotheses ........................... 47 2.2.1 Bullishness .................................................................................... 48 2.2.2 Message volume............................................................................ 50 2.2.3 Disagreement................................................................................. 51 2.2.4 Information diffusion .................................................................... 52 3 Data set and methodology............................................................................................. 54 3.1 Data set and sample selection of stock microblogs............................... 54 3.2 Naïve Bayesian text classification......................................................... 55 3.3 Aggregation of daily tweet features ...................................................... 57 3.4 Financial market data ............................................................................ 58 3.5 Information aggregation in microblogging forums............................... 59 4 Results ............................................................................................................................ 60 4.1 Descriptive statistics.............................................................................. 60 4.2 Overall relationship of tweet and market features ................................ 64 4.2.1 Pairwise correlations ..................................................................... 64 4.2.2 Contemporaneous regressions....................................................... 65 4.2.3 Time-sequencing regressions........................................................ 67 4.3 In-depth analysis for selected market features ...................................... 69 4.3.1 Trading volume ............................................................................. 69 4.3.2 Return: Event-study of buy and sell signals.................................. 71 4.4 Information diffusion in stock microblogging forums.......................... 74 5 Discussion....................................................................................................................... 78 5.1 Summary of results................................................................................ 78 5.2 Limitations and further research ........................................................... 80 5.3 Conclusion............................................................................................. 81 6 Appendix ........................................................................................................................ 83 6.1 Naïve Bayesian text classification......................................................... 83 6.2 Classification of our data set................................................................. 84 6.3 Market and tweet features per company ............................................... 88 6.4 Trading strategy..................................................................................... 91 7 References ...................................................................................................................... 95 IV Tables of Contents II.3 News or Noise?.............................................................................................................. 101 1 Introduction ................................................................................................................. 102 2 Related work and research questions........................................................................ 105 2.1 News as a source to identify event days.............................................. 105 2.2 Limitations of the business press as a source of news ........................ 106 2.3 News sentiment ................................................................................... 108 2.4 Different types of news events............................................................ 109 2.5 Industry effects.................................................................................... 111 3 Data set and methodology........................................................................................... 112
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