Магистратура
2025/2026
Методологический научно-исследовательский семинар
Статус:
Курс обязательный (Стратегическое управление финансами фирмы)
Кто читает:
Школа финансов
Где читается:
Факультет экономических наук
Когда читается:
1-й курс, 3, 4 модуль
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
3
Контактные часы:
52
Course Syllabus
Abstract
The research seminar is aimed to introduce the master students to the world of research in the area of finance. Research in this area applies specific mathematical and econometrical methods, as well as the perfect data mining skills. The course includes the methodological part and the topic development part. Both parts of the research seminar aim to help students to develop the academic writing skills and a number of soft skills that are useful for a financier (e.g. presentation skills, team work, project management). The first part of research seminar aims at fostering discussion and interaction on some topics as Omitted variable problem, Simultaneity, Measurement error, Selection, Event Studies etc. with examples of Corporate Finance papers. Key recent papers will be selected and discussed among the participants. The objective is to develop the necessary tools and understanding for future identification and implementing in research. Simulation of real conference/seminar format as well as writing of a short literature review will help participants to get the initial core techniques of scientific work. To follow this course the basic courses of finance and microeconomics are the prerequisites.
Learning Objectives
- To provide the student with proper tools and skills for starting their own research in the area of finance.
Expected Learning Outcomes
- To be able to formulate and verify his or her own research question
- To be able to structure a research paper, formulate research question, write abstract
- Students will learn how to use the main databases of academic sources available through the HSE library.
- To be able to retrieve data from open statistical databases, archives, and other public sources.
- Deal with a number of econometric problems frequently faced in financial research (including Omitted variable problem, Simultaneity, Measurement error, Selection Bias).
- To demonstrate the ability to write valid and understandable research proposal.
- This course will prepare students to write proposals for their diploma topics.
- To understand the rationale and value of text analysis in corporate finance, and to master its foundational concepts and tools.
- To master the core methodologies for transforming unstructured text into quantitative variables for empirical research.
- To learn how to design a robust research project using text analysis, address critical empirical challenges, and explore its frontier applications.
- Learn how to collect financial data from Windows, Yahoo Finance, and FRED databases using Excel and Python add-ins.
- Learn how to use AI in a proper way for the literature review
- Learn how to use DEA methodology. EventStudies. Diff in Diff
- Learn how to apply cluster analysis
- Learn how to apply AI in financial modelling
Course Contents
- Introduction to research questions choice
- Literature review. AI pros and cons
- Financial databases and data processing
- Replication papers and resources for replications
- Econometrics problems in finance
- Econometric methods and problems in finance.
- Cluster Analysis
- AI in financial modelling
- Text Analysis
- Research proposal
Assessment Elements
- Home assignment 4
- Home assignment 1financial data collection project for a master's thesis
- Home assignment 2
- Home assignment 3
- Home assignment 5
- Home assignment 6
Interim Assessment
- 2025/2026 4th module0.15 * Home assignment 1 + 0.15 * Home assignment 2 + 0.2 * Home assignment 3 + 0.2 * Home assignment 4 + 0.15 * Home assignment 5 + 0.15 * Home assignment 6
Bibliography
Recommended Core Bibliography
- Econometric analysis of cross section and panel data, Wooldridge, J. M., 2002
- Econometric analysis of cross section and panel data, Wooldridge, J. M., 2010
- Grimmer, J., & Stewart, B. M. (2013). Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis, 3, 267.
- Grimmer, J., & Stewart, B. M. (2013). Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.BC6A6457
- Jeffrey M Wooldridge. (2010). Econometric Analysis of Cross Section and Panel Data. The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.mtp.titles.0262232588
- McKinney, W. (2012). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython. Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=495822
- McKinney, W. (2018). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython (Vol. Second edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1605925
- Python for data analysis : data wrangling with pandas, numPy, and IPhython, Mckinney, W., 2017
- Wooldridge, J. M. . (DE-588)131680463, (DE-576)298669293. (2002). Econometric analysis of cross section and panel data / Jeffrey M. Wooldridge. Cambridge, Mass. [u.a.]: MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.095629173
- Wooldridge, J. M. . (DE-588)131680463, (DE-576)298669293. (2010). Econometric analysis of cross section and panel data / Jeffrey M. Wooldridge. Cambridge, Mass. [u.a.]: MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.263114414
- Wooldridge, J. M. (2002). Econometric Analysis of Cross Section and Panel Data. Cambridge, Mass: MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=78079