Quantitative Research Methods in Corporate Finance

Préface
Focuses on empirical research in corporate finance and teaches students to apply econometric methods in their own research and studies.

Auteur
Taylan Mavruk is a Professor of Finance at the University of Gothenburg. His research focuses on corporate finance, investor behaviour, home/local bias, insider trading, CSR/ESG, mutual fund performance, and economics of patents. His research has been published in leading finance journals. Taylan has been teaching in courses related to corporate finance and econometrics since 2006.

Texte du rabat

This innovative textbook develops students' understanding of empirical research in corporate finance, enabling them to apply econometric methods to their own research. Features include lab work and mini case studies. Code is provided in Stata, Python, and R, so students can develop their programming skills.

Résumé
Focused on empirical methods and their applications to corporate finance, this innovative text equips students with the knowledge to analyse and critically evaluate quantitative research methods in corporate finance, and conduct computer-aided statistical analyses on various types of datasets. Chapters demonstrate the application of basic econometric models in corporate finance (as opposed to derivations or theorems), backed up by relevant research. Alongside practical examples and mini case studies, computer lab exercises enable students to apply the theories of corporate finance and make stronger connections between theory and practice, while developing their programming skills. All of the Stata code is provided (with corresponding Python and R code available online), so students of all programming abilities can focus on understanding and interpreting the analyses.

Contenu

1. Introduction; 2. The nature of sampling, non-parametric analysis, and hypothesis testing; 3. Basic data analysis in Stata; 4. Survey data analysis; 5. Classical linear regression model; 6. Endogeneity bias; 7. Time series analysis; 8. Event studies; 9. Panel data analysis; 10. Selection of models with limited dependent variables; 11. Writing empirical papers in corporate finance; 12. Future directions: Machine learning methods.