Bachelor
2020/2021
Data Analysis in Economics and Finance
Type:
Elective course (HSE University and University of London Parallel Degree Programme in International Relations)
Area of studies:
International Relations
Delivered by:
Faculty of World Economy and International Affairs
When:
3 year, 1 module
Mode of studies:
offline
Instructors:
Marina Ananyeva
Language:
English
ECTS credits:
3
Contact hours:
20
Course Syllabus
Abstract
In this finance-oriented intermediate R course, you will learn how to apply logistic regression to a real-world financial data and how to construct and backtest an optimal investment portfolio. By the end of the course, you will be familiar with the basics of manipulating financial datasets to perform predictive analytics in R.
Learning Objectives
- To provide an introduction to applications of R in finance and enable students to carry out a financial research in a reproducible fashion.
Expected Learning Outcomes
- Skill of using tidyverse, ggplot2
- Skill of using logistic regression
- Skill of evaluation of model predictive accuracy
- Skill of modeling banks’ probability of default.
- Skill of portfolio performance evaluation.
- Skill of backtesting naïve 1/N portfolio.
- Skill of applying Markowitz Portfolio Theory.
Course Contents
- Review of the basic data manipulation and visualization R packages: tidyverse, ggplot2. Summary statistics of a dataset, basics of linear regression models.
- Markowitz Portfolio Theory. Portfolio returns, covariance matrix, mean-variance analysis. The efficient frontier. Rolling covariations. Instability of the covariance matrix
- Portfolio performance evaluation. Financial data sources. Performance metrics. Selection of portfolio benchmarks. Backtesting and its biases. Overfitting and p-hacking.
- Introduction to logistic regression. Maximum likelihood estimation. Evaluation of model significance. P-value, confidence intervals, pseudo-R-squared.
- Evaluation of model predictive accuracy. Contingency table. ROC – curve. Selecting an optimal separation threshold.
- Modeling banks’ probability of default. Selecting an optimal set of explanatory variables. Out-of-sample verification of the model.
- Naïve 1/N portfolio. Selecting a universe of stocks. Downloading and transforming data. Simulating trading process. Benchmarking against S&P 500.
Assessment Elements
- Problem set 1
- Problem set 2
- Presentation of the group project
- Problem set 1
- Problem set 2
- Presentation of the group project
Interim Assessment
- Interim assessment (1 module)0.6 * Presentation of the group project + 0.2 * Problem set 1 + 0.2 * Problem set 2
Bibliography
Recommended Core Bibliography
- García, D., Nebot, À., & Vellido, A. (2017). Intelligent data analysis approaches to churn as a business problem: a survey. Knowledge & Information Systems, 51(3), 719–774. https://doi.org/10.1007/s10115-016-0995-z
- L. Taylor, R. Schroeder, & E. Meyer. (2014). Emerging practices and perspectives on Big Data analysis in economics: Bigger and better or more of the same? https://doi.org/10.1177/2053951714536877
- The Turing Way: A Handbook for Reproducible Data Science. (2019). https://doi.org/10.5281/zenodo.3381445
Recommended Additional Bibliography
- Shi, L. (2019). Fuzzy Evaluation Model of Economic Loss in High Density Marine Traffic Accidents Based on Big Data Analysis. Journal of Coastal Research, 93, 768–774. https://doi.org/10.2112/SI93-107.1
- Vanderplas, J. T. (2016). Python Data Science Handbook : Essential Tools for Working with Data (Vol. First edition). Sebastopol, CA: Reilly - O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1425081