MFE5290 Quantitative Investment

Course Description

The industry landscape of investment, trading, and risk management has been revolutionized by computing technologies, data science, and financial engineering. To progress in tandem with the changes in the industry, the topics covered in this course include Alternative ETF Construction, Portfolio Theory, and Empirical Finance.

In addition to mathematical modeling, an important part of this course is the practical aspect: computational implementations with statistical tests. Given that implementation and test procedures are involved, this quantitative finance course is algorithmic and hands-on in nature.

Lecture Slides

  • Topic 1:   Introduction     Indexes     KO   PEP     Reconstitution     Information Ratio
  • Topic 1A: Volatility Indexes     Illustration of CBOE's Method to obtain VIX
  • Topic 2:   Random Walks and Variance Ratio Tests
  • Topic 3:   Simple Linear Regression and Portfolio Hedging     Event Study
  • Topic 4:   Asset Pricing Models
  • Topic 4A: Multiple Linear Regression
  • Topic 5:   Factor Models and Analysis
  • Topic 6:   Principal Component Analysis
  • Topic 6A: PCA for Factor Model
  • Topic 7:   Portfolio Optimization

    • Homework Assignments

    Assignment 1     Assignment 2     Assignment 3     Assignment 4

    Mini Real-World Project

  • Project Description
  • Project Data: 120 stocks and risk-free rate
  • Python code to create a price-weighted index
  • Python code to create an equally weighted index
  • Python code to help you start the mini project with the consolidated index data in csv file

    • Python Programming

  • An Introduction to Python: Gettng Started (pdf file)
  • Multiple random walks
  • A random walk with drift and volatility envelop
  • Get Finance Yahoo!'s data with dividends and stock splits
  • Case study of an ETF powered by Artificial Intelligence (zip file)
  • Variance Ratio Test (zip file)   Answer
  • Annualized Implied Dividend Yield   Data (Excel file)
  • Simple Linear Regression: Demo 1   Demo 2
  • Event Study: Python Code   SPY ETF   Bear Stearns   JPMorgan Chase
  • Empirical Factors: Multiple Linear Regression   DJIA   FF Five Factors
  • Demo code of Weighted Least Squares (WLS) and 2-stage Feasible WLS
  • Demo code 1 and Demo code 2 of PCA
  • Animation and PCA of the term structure of interest rates (Excel file)
  • PCA to Construct Eigen Portfolio (zip file)
  • Portfolio Optimization Hands-on