Кафедра математической экономики (Нижний Новгород)

During the course, students gain practical abilities in using modern computer software and utilise the tools required to analyze financial data. The course covers the following main topics: importing financial data, primary processing and visualization, building a trading robot and evaluating the efficacy of the chosen strategy, cluster analysis, forming an investment portfolio, estimating the parameters of empirical models, forecasting.

Learning Objectives

The goal of this course is to develop and improve skills in financial data analysis with Python.

Expected Learning Outcomes

Importing data from various sources
Preprocess financial data
Visualize financial data
Perform event study
Perform cluster analysis
Perform time series analysis

Course Contents

Data import
Data preprocessing
Data visualization
Event study
Cluster analysis
Time series

Assessment Elements

Activity at seminars
Exam

Interim Assessment

2024/2025 4th module
0.5 * Activity at seminars + 0.5 * Exam

Bibliography

Recommended Core Bibliography

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

Recommended Additional Bibliography

Lewinson, E. (2020). Python for Finance Cookbook : Over 50 Recipes for Applying Modern Python Libraries to Financial Data Analysis. Packt Publishing.
Weiming, J. M. (2019). Mastering Python for Finance : Implement Advanced State-of-the-art Financial Statistical Applications Using Python, 2nd Edition (Vol. Second edition). Birmingham, UK: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2116431

Authors

LARIN ALEKSANDR VLADIMIROVICH

Data Analysis in Finanses

Преподаватель

Course Syllabus

Abstract