Essays in empirical and behavioral finance / submitted by Sebastian Lehner. Wuppertal, October 2024
Inhalt
- Acknowledgements
- Introduction
- Summary – What Drives Robo-Advice?
- Summary – Investor Experience and Portfolio Choice
- Summary – Trust me, I am a Robo-advisor
- Summary – On the Relationship between Financial Distress and ESG Scores
- Summary – Senior Hiring Impacts: An Alternative Data Perspective
- Summary – Balancing Dispersion and Agglomeration: How Workforce Geography influences Corporate Performance
- What Drives Robo-Advice?
- Investor Experience and Portfolio Choice – Regulatory Costs from MiFID II
- Introduction
- Portfolio choice and investor experience
- Regulatory impact: Evidence from the robo-advisory market
- Advisor specific evidence
- Approximate regulatory costs using panel survey data
- Conclusion
- Trust me, I am a Robo-advisor
- On the Relationship between Financial Distress and ESG Scores
- Introduction
- Literature review
- Data, variables, and methodology
- Raw data, final dataset, and final samples
- Applied variables and descriptive statistics
- Applied methodology
- Empirical results
- Linear regression results
- Additive regression results
- Additive regression results for the ESG sub-factor
- Extensions and robustness of the regression analysis
- Regression results with multi-year capital expenditures and R&D expenditures
- Regression results with energy intensity as dependent variable
- Regression results with shareholder-stakeholder orientation as dependent and independent variable
- Regression results with exogenous shocks
- Robustness
- Conclusion
- Senior Hiring Impacts: An Alternative Data Perspective
- Introduction
- Data
- Methodology
- Results
- Conclusion
- Appendix: Figures and Tables
- Appendix: Variable Definitions
- Balancing Dispersion and Agglomeration: How Workforce Geography influences Corporate Performance