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Financial Data Science (CFRM 502)


Course Details

This course has no current offerings.

About this Course

This course is an in-depth hands-on introduction to the R statistical programming language for computational finance. The course will focus on R code and code writing, R packages, and R software development for statistical analysis of financial data.

Topics include:

  • The R language – syntax, data types, resources, packages and history, graphics, visualization
  • Graphics in R – plotting and visualization
  • Statistical analysis of returns – fat-tailed skewed distributions, outliers, serial correlation
  • Financial time series modeling – covariance matrices, AR, VecAR
  • Factor models – linear regression, LS and robust fits, test statistics, model selection
  • Multidimensional models – principal components, clustering, classification
  • Optimization methods – QP, LP, general nonlinear
  • Portfolio optimization – mean-variance optimization, out-of-sample back testing
  • Bootstrap methods – non-parametric, parametric, confidence intervals, tests
  • Portfolio analytics – performance and risk measures, style analysis

Prerequisites: R Programming for Quantitative Finance (CFRM 463) or prior programming experience in R, or experience in a modern programming language (C family, Java, Python).

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