Course Detail

Quantitative Portfolio Management (35126)

Course Description by Faculty

  • Koijen, Ralph
  • Content

    This course develops a framework to understand, evaluate, and develop quantitative investment strategies. The course starts from the basic theoretical framework as discussed in the Investments course and then covers much of the recent research on quantitative investment strategies. As many modern investment strategies use big data on securities prices, company fundamentals, text sentiment measures (e.g., Twitter), portfolio holdings, and asset flows, the course will teach you how to develop, analyze, and back-test new strategies and products in a realistic big-data environment. The final project requires you to develop and pitch a new investment strategy using this framework as well.

    Key questions include:

    • What are the key empirical patterns in returns across securities and asset classes (including return predictability, riskiness, and connections to the macroeconomic environment)?
    • How can these facts be used to build realistic investment strategies and products, while accounting for market frictions?
    • Considering this evidence, how can we evaluate the performance of existing products (e.g., ETFs, hedge funds, and mutual funds) or new strategy ideas?

    We will use a programming language called Python to build and analyze investment strategies. However, no prior knowledge of Python is required for the course. As part of the course, we will discuss modern data analytics tools and some basic Python programming. I will provide templates for all major quant strategies that will help you to develop and critically analyze your own investment ideas. The problem sets will familiarize you with Python, and quantitative investment strategies and big data analytics more broadly.

  • Prerequisites
    Business 30000, 33001, and 35000 are non-strict (non-enforced) prerequisites. Business 41000 (or 41100) is a strict (enforced) prerequisite. Students must be comfortable with statistics, regression analysis, microeconomics, and investments at the level of the above courses. No prior knowledge of Python is required.
    • Strict Prerequisite

  • Materials
    There is no required textbook and all lecture notes will be made available on Canvas. In addition, problem sets and supplementary materials (such as academic papers and Python code templates for the investment strategies / portfolio analytics) will be made available on Canvas as well.
  • Grades
    The assignments for the course consist of 3 problem sets, 2 cases, a midterm, and a final project. There will be no final exam. Problem sets and case questions will be available on Canvas and should be submitted via Canvas.  No auditors.
    • No auditors

  • Syllabus
  • Winter 2023Section: 35126-01T 8:30AM-11:30AMHarper CenterC03In-Person Only
  • Winter 2023Section: 35126-02TH 8:30AM-11:30AMHarper CenterC03In-Person Only
  • Winter 2023Section: 35126-81T 6:00PM-9:00PMGleacher Center206In-Person Only
Description and/or course criteria last updated: January 17 2023