Retail Investor Sentiment and Behavior [electronic resource] : An Empirical Analysis /
Burghardt, Matthias.author.
Investor sentiment and behavior play an important role in financial markets. Using a unique data set consisting of more than 36.5 million submitted retail investor orders over the course of five years, Matthias Burghardt constructs an innovative retail investor sentiment index. He shows that retail investors' trading decisions are correlated, that retail investors are contrarians, and that a profitable trading strategy can be based on these aggregated sentiment measures.
Description based upon print version of record.
Acknowledgements; Table of Contents; Abbreviations; List of Figures; List of Tables; 1 Introduction; 1.1. Motivation; 1.2. Research Outline; 1.3. Overview and Structure; 1.4. Related Publications; 2 Related Theoretical and Empirical Work; 2.1. Introduction to Behavioral Finance; 2.1.1. Are Financial Markets Efficient?; 2.1.2. Challenges to Efficient Markets; 2.1.3. Emergence of Behavioral Finance; 2.2. Theoretical Work; 2.3. Empirical Work; 2.3.1. Noise Traders; 2.3.2. Investor Sentiment; 2.3.3. Individual Investors; 2.3.4. Correlated Trading; 2.4. Discussion; 2.4.1. Behavioral Finance
2.4.2. Correlated Trading2.4.3. Return Correlation; 2.4.4. Market Efficiency; 2.5. Conclusion; 3 Investor Sentiment Construction; 3.1. Classification of Sentiment Measures; 3.1.1. Related Work; 3.1.2. Advantages and Disadvantages; 3.2. Sentiment Measures in Research; 3.2.1. The Closed-End Funds Discount; 3.2.2. Meta-Measures; 3.3. Sentiment Measures in Practice; 3.3.1. Survey-Based Measures; 3.3.2. Market-Data-Based Measures; 3.3.3. Meta-Measures; 3.3.4. Summary Statistics; 3.4. Evaluation of Sentiment Measures; 3.4.1. Direct Sentiment Measures; 3.4.2. Indirect Sentiment Measures
3.4.3. Direct vs. Indirect Sentiment Measures3.4.4. Sentiment Measures vs. Market Returns; 3.4.5. Review of Results; 3.5. Conclusion; 4 Construction of the Euwax Sentiment Index; 4.1. Introduction; 4.1.1. Securitized Derivatives; 4.1.2. European Warrant Exchange; 4.1.3. Key Facts; 4.2. Data Set; 4.3. Basic Index Calculation; 4.4. Sentiment Analysis; 4.4.1. Numbervs. Volume-Based Measures; 4.4.2. Product Types; 4.4.3. Order Types; 4.4.4. Order Volume Groups; 4.4.5. Submitted Orders; 4.4.6. Leverage; 4.5. Comparison with Other Sentiment Measures; 4.5.1. Indirect Sentiment Measures
4.5.2. Direct Sentiment Measures4.5.3. Review of Results; 4.6. Conclusion; 5 Retail Investor Herding; 5.1. Introduction; 5.2. Related Work; 5.2.1. Definitions of Herding; 5.2.2. Empirical Findings; 5.2.3. Discussion; 5.3. Evidence of Market-Wide Herding; 5.3.1. Data; 5.3.2. Herding Measure Construction; 5.3.3. Results; 5.3.4. Review of Results; 5.4. Market-Wide Herding on a Broker Level; 5.4.1. Data; 5.4.2. Results; 5.4.3. Review of Results; 5.5. Stock-Level Herding; 5.5.1. Data; 5.5.2. Herding Measure; 5.5.3. Results; 5.5.4. Review of Results; 5.6. Conclusion
6 The Predictive Power of Retail Investor Sentiment6.1. Related Work; 6.2. Data and Methodology; 6.2.1. Data Set; 6.2.2. Methodology; 6.3. Results; 6.3.1. Preand Post-Portfolio-Formation Returns; 6.3.2. Control Variables; 6.3.3. Portfolio Holding Returns; 6.4. Robustness Checks; 6.5. Conclusion; 7 Conclusion and Future Work; 7.1. Conclusion; 7.2. Summary of Contributions; 7.3. Future Work; Appendix; A Appendix to Chapter 4; Bibliography
"Dissertation Karlsruhe Institute of Technology, 2010."--T.p., verso.
Includes bibliographical references.
Investor sentiment and behavior play an important role in financial markets. Using a unique data set consisting of more than 36.5 million submitted retail investor orders over the course of five years, Matthias Burghardt constructs an innovative retail investor sentiment index. He shows that retail investors' trading decisions are correlated, that retail investors are contrarians, and that a profitable trading strategy can be based on these aggregated sentiment measures.
Finance.
Finance, general.