LESSONS

- CFA Level 2: Portfolio Management – Introduction
- Mean-Variance Analysis Assumptions
- Expected Return and Variance for a Two Asset Portfolio
- The Minimum Variance Frontier & Efficient Frontier
- Diversification Benefits
- The Capital Allocation Line – Introducing the Risk-free Asset
- The Capital Market Line
- CAPM & the SML
- Adding an Asset to a Portfolio – Improving the Minimum Variance Frontier
- The Market Model for a Security’s Returns
- Adjusted and Unadjusted Beta
- Multifactor Models
- Arbitrage Portfolio Theory (APT) – A Multifactor Macroeconomic Model
- Risk Factors and Tracking Portfolios
- Markowitz, MPT, and Market Efficiency
- International Capital Market Integration
- Domestic CAPM and Extended CAPM
- Changes in Real Exchange Rates
- International CAPM (ICAPM) - Beyond Extended CAPM
- Measuring Currency Exposure
- Company Stock Value Responses to Changes in Real Exchange Rates
- ICAPM vs. Domestic CAPM
- The J-Curve – Impact of Exchange Rate Changes on National Economies
- Moving Exchange Rates and Equity Markets
- Impacts of Market Segmentation on ICAPM
- Justifying Active Portfolio Management
- The Treynor-Black Model
- Portfolio Management Process
- The Investor Policy Statement

# Multifactor Models

While the Market Model uses only a single risk factor to price a security’s return, Multifactor Models apply a set of risk factors to describe an asset’s returns.

## Multifactor Model Types

**Macroeconomic Factor Models**

Apply economic variable as the risk factors that explain a security’s returns.

- Surprise Factor for betas of macroeconomic factor models:
- These models will not apply the actual value for a variable such as forecasted GDP growth rate. Rather macroeconomic factor models apply a beta for the surprise factor of GDP growth describing the security’s expected return response when the variable “surprises” expectations.
**Calculating portfolio returns in a two security portfolio with a macroeconomic factor model:**- For each security, calculate the surprise return from the model; apply the security’s weighting to the surprise return; and sum the two weighted values.
- Surprise R security i = Expected R security i +β1(actual factor 1 – expected factor 1) + … repeat for # of factors
- This formula shows how the surprise beta is applied to the surprise to the factor value. When the actual values equal the expected values, then the security’s actual return will equal its expected return.
- The difference between the expected return and the actual return is the surprise return; this is the error term of the regression model.

**Fundamental Factor Models**

Apply asset-class specific variables (ex. stocks: P/E ratio, degree of financial leverage, market capitalization, etc.) to explain a security’s returns.

**Statistical Factor Models**

These models apply a variety of variables in a regression analysis to find the best fit of historical data in explaining a security’s returns.

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