FIN 643: Financial Analytics

This course introduces methods and tools useful in decision making in the financial industry, including macroeconomic event studies, analysis of term structures, Morningstar equity data, style analysis, credit card receivables, trading analytics and execution algorithms. Students learn to analyze financial data sets using different analytical methods. Topics include analytical techniques, nonlinear estimation, portfolio analytics, risk measurement, extreme value analysis, forecasting and predictive techniques and financial modeling. A final project will apply techniques to the construction of an online application.

Learning Objectives:

  • Develop ability to apply data analytical concepts to standard finance industry models
  • Develop proficiencies in coding and modeling using R and RStudio
  • Draw insights and verify conclusions based on inferences from model simulations
  • Demonstrate a comprehensive understanding of analytical concepts via final project application 

Tools & Concepts:

  • R / RStudio / R Markdown
  • Shiny / shinydashboard
  • Data structures
  • Statistical computing
  • Functions, loops and control bootstrapping
  • Empirical characteristics of economic and financial time series
  • Term structure of interest rates
  • Market and credit risk
  • Measuring volatility
  • Risk management analytics

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