Corse Leader: Anna Kornyliuk and Roman Kornyliuk
Home Institution: Kyiv National Economic University named after Vadym Hetman, Ukraine
Courses pre- requisites: Basics of Finance, Statistics.
The course reviews main analytical tools that are fundamental for decision making in finance. Students successfully completing this course will achieve necessary skills to solve financial problems and make data driven decisions. Through numerous practical, hands-on exercises students are expected to learn how to gather, analyse and interpret financial data. The course also presents such popular quantitative methods as linear and logistic regressions, clustering and network analysis. In addition the course covers various types of charts and reporting tools that allows to make convincing presentations for management purposes.
This course will allow the students to:
● Interpret and discuss the outputs of given financial models and create their own models.
● Design and create visualisations that clearly communicate financial data insights.
● Gain essential knowledge and hands-on experience in the data analysis process, including data scraping, manipulation, exploratory data analysis.
● Be prepared for more advanced applied financial modelling courses.
● Improve leadership, teamwork and critical thinking skills for financial decision making.
Part 1 Data analysis process
Day 1. Intro to data analysis with R: installing, data types, functions, loops.
Day 2. Financial data scraping, cleaning and manipulation based on real datasets.
Day 3. Exploratory data analysis: stock exchange data, portfolio analytics.
Day 4. Financial data visualisation.
Day 5. Reproducible research and reporting.
Part 2. Financial modelling techniques
Day 1. Simple and multiple linear regression. Case Study ‘Evaluating Determinants of
Companies’ Capital Structure Using Multiple Regression’.
Day 2. Logit regressions. Case Study ‘Logit Modelling of Bank Solvency’.
Day 3. Clustering techniques. ‘Defining Banking Business Models’.
Day 4. Network analysis.
Day 5. Group presentations and discussing.
Lectures and practice classes, case studies, discussions, group presentations.
Software: R Studio (version 2021.09.1+372), Jupyter Notebook.
Required learning materials will be provided by the instructors during the course.
Recommended literature:
1. Wickham H., Grolemund G. (2017). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O'Reilly Media. 1st edition 520 pages. URL: https://r4ds.had.co.nz/
2. Bennett M. J., Hugen D. L. (2016). Financial Analytics with R: Building a Laptop Laboratory for Data Science. Cambridge University Press. 1st edition. 397 pages. (link)
3. Ang C. S. (2015). Analyzing Financial Data and Implementing Financial Models Using R. Springer Texts in Business and Economics. Springer. 1st edition. 367 pages. 4. Chang W. (2018). R Graphics Cookbook: Practical Recipes for Visualizing Data. O'Reilly Media, Inc. 2nd edition. 444 pages.
5. Irizarry R. A. (2019). Introduction to Data Science. Data Analysis and Prediction Algorithms with R. (link)
6. A Ggplot2 Tutorial for Beautiful Plotting in R. Posted by Cédric on Monday, August 5, 2019 (link)
7. The R Graph Gallery. URL: https://www.r-graph-gallery.com/index.html
Assessment
Practical exercises (40%)
Group project (30%)
Final exam (30%)