Course Detail

Quantitative Portfolio Management (35126)

Course Description by Faculty

  • Koijen, Ralph
  • Content
    There has been a proliferation of new products and investment strategies in the asset management space in recent years. This course applies portfolio theory to understand and evaluate these products and strategies in the context of the empirical evidence about return patterns across assets (i.e., the factors such as value/growth, momentum, low volatility, and carry that drive returns) in multiple markets/asset classes (e.g., US and international equities and bonds, currencies, and commodities).

    Key questions include:

    • What factors drive asset returns? What are the economic drivers?

    • How can the facts about returns be used to build investment strategies and products?

    • How should we evaluate the performance of existing products (e.g., ETFs, hedge funds, and mutual funds) or new strategy ideas given the empirical evidence?

    The course starts from the basic theoretical framework as developed 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, sentiment measures, and asset flows, the course will see how to develop, analyze, and back-test new strategies and products in a realistic environment. The final project requires you to develop, analyze, and pitch a new investment strategy using this framework as well.

    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 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.

    The course covers recent trends in quantitative investment strategies using new data based on, for instance, asset flows, portfolio holdings, and text sentiment (e.g. Twitter) and how these strategies are offered to investors via, for instance, ETFs or hedge funds.

    • Lectures

    • Discussion

    • Case Studies

    • Group Projects

    • Group Presentations

  • 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 Quantopian templates for the investment strategies / portfolio analytics) will be made available on Canvas as well.
    • Canvas Site Available

  • Grades
    The assignments for the course consist of 4 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.
    • Graded homework assignments

    • Graded attendance/participation

    Assessment & Testing
    • Midterm

    • No auditors

  • Syllabus
  • Winter 2022Section: 35126-01M 1:30PM-4:30PMHarper CenterC04In-Person Only
  • Winter 2022Section: 35126-81M 6:00PM-9:00PMGleacher Center308In-Person Only
  • Winter 2022Section: 35126-85S 1:30PM-4:30PMGleacher Center308Dual Modality
    Faculty In-Person
Description and/or course criteria last updated: December 22 2021