In this module, we build on the tools from the previous module to develop measure of portfolio risk and return. We define and distinguish between the different sources of risk and discuss the concept of diversification: how and why putting risky assets together in a portfolio eliminates risk that yields a portfolio with less risk than its components. Finally, we review the quantitative tools that help us identify the ‘best’ portfolios with the least risk for a given level of expected return by considering a numerical example using international equity data.

In this module, we describe how investors make choices. Specifically, we look at how utility functions are used to express preferences. We review measures to describe investors’ attitude towards risk. Finally, we discuss how we can summarize investors’ preferences using a specific utility function: mean-variance preferences.

In this module, you will learn about mean-variance optimization: how to make optimal capital allocation and portfolio choice decisions when investors have mean-variance preferences. This was one of the ground-breaking ideas in finance. We will formally set up the investor’s portfolio choice problem and learn step-by-step how to solve for the optimal allocation and risky portfolio choice given a set of risky securities. You will also have an opportunity to apply these techniques to a numerical example. This module is slightly more technical than the others. Stick with it… you will not regret it!

In this module, we build on the insights obtained from modern portfolio theory to understand how risk and return are related in equilibrium. We first look at the main workhorse model in finance, the Capital Asset Pricing Model and discuss the expected return-beta relationship. We then turn our attention to multi-factor models, such as the Fama-French three-factor model.