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Бакалавриат 2019/2020

Анализ данных в экономике и финансах

Направление: 41.03.05. Международные отношения
Когда читается: 3-й курс, 1 модуль
Формат изучения: Full time
Преподаватели: Камротов Михаил Владимирович
Язык: английский
Кредиты: 3

Программа дисциплины

Аннотация

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.
Цель освоения дисциплины

Цель освоения дисциплины

  • To provide an introduction to applications of R in finance and enable students to carry out a financial research in a reproducible fashion.
Результаты освоения дисциплины

Результаты освоения дисциплины

  • Skill of using tidyverse, ggplot2
  • Skill of backtesting naïve 1/N portfolio.
  • Skill of applying Markowitz Portfolio Theory.
  • Skill of portfolio performance evaluation.
  • Skill of modeling banks’ probability of default.
  • Skill of evaluation of model predictive accuracy
  • Skill of using logistic regression
Содержание учебной дисциплины

Содержание учебной дисциплины

  • 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.
Элементы контроля

Элементы контроля

  • неблокирующий Created with Sketch. Problem set 1
  • неблокирующий Created with Sketch. Problem set 2
  • неблокирующий Created with Sketch. Presentation of the group project
Промежуточная аттестация

Промежуточная аттестация

  • Промежуточная аттестация (1 модуль)
    0.6 * Presentation of the group project + 0.2 * Problem set 1 + 0.2 * Problem set 2
Список литературы

Список литературы

Рекомендуемая основная литература

  • 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

Рекомендуемая дополнительная литература

  • 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