Анализ данных в экономике и финансах

Бакалавриат 2020/2021

Статус: Курс по выбору (Программа двух дипломов НИУ ВШЭ и Лондонского университета по международным отношениям)

Направление: 41.03.05. Международные отношения

Кто читает: Факультет мировой экономики и мировой политики

Где читается: Факультет мировой экономики и мировой политики

Когда читается: 3-й курс, 1 модуль

Формат изучения: без онлайн-курса

Преподаватели: Ананьева Марина Евгеньевна

Язык: английский

Кредиты: 3

Контактные часы: 20

Full 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

Course Syllabus