Магистратура
2025/2026
Научно-исследовательский семинар "Методы исследований в финансах"
Статус:
Курс обязательный (Магистр аналитики бизнеса)
Где читается:
Факультет экономических наук
Когда читается:
1-й курс, 2, 3 модуль
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
3
Контактные часы:
18
Course Syllabus
Abstract
The course provides a practical toolbox of empirical methods in finance, emphasizing core econometric techniques – particularly panel data models and approaches to endogeneity – alongside specialized topics of event‑study methods and advanced topics such as machine learning in finance. Students learn method’s intuition, study applications in published papers, and apply methods to conduct research using real-world data, fostering both practical skills and theoretical knowledge. The course raises awareness of methodological rigor when utilizing qualitative research methods and approaches.
Learning Objectives
- The primary aim of the seminar is to equip participants with a comprehensive understanding of when and how to effectively employ key empirical methods in their future research and for practical purposes.
Expected Learning Outcomes
- build a working knowledge of key empirical methods used in corporate finance, including how and when to apply each tool
- apply core, specialized and advanced econometric techniques relevant to finance, understanding their assumptions and limitations
- implement empirical analyses using statistical software (e.g., Stata, R, or Python)
- critically read, assess and present scholarly finance literature
- apply qualitative data analysis methods, considering limitations and ethical implications
- interpret and present empirical results in a manner consistent with academic and professional standards
Course Contents
- Event study methodology: theoretical foundations and underlying econometrics techniques
- Panel data methods
- Dealing with endogeneity, omitted variables bias, reverse causality, measurement error
- Quantile Regression: Methods and Applications
- Machine Learning in Finance: Textual Analysis
- Case study research
- Bibliometric and systematic literature reviews
- Thematic analysis and content analysis
Assessment Elements
- Event study: analytical task on real data
- Panel data methods and econometric problems team assignment
- Using machine learning to analyze texts
- Thematic analysis and content analysis
Interim Assessment
- 2025/2026 3rd module0.22 * Event study: analytical task on real data + 0.34 * Panel data methods and econometric problems team assignment + 0.22 * Thematic analysis and content analysis + 0.22 * Using machine learning to analyze texts
Bibliography
Recommended Core Bibliography
- Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics : An Empiricist’s Companion. Princeton: Princeton University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=329761
- Bird, S., Loper, E., & Klein, E. (2009). Natural Language Processing with Python. O’Reilly Media.
- Brooks,Chris. (2019). Introductory Econometrics for Finance. Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.cup.cbooks.9781108422536
- Handbook of Corporate Finance Eckbo, Bjørn Espen; Eckbo, B. Espen Elsevier Science & Technology 2008
- Mostly harmless econometrics : an empiricist's companion, Angrist, J. D., 2009
Recommended Additional Bibliography
- Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics : Methods and Applications. New York, NY: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=138992