Factor Based Asset Allocation

Date03 Dec 2019
Time13:00 - 17:30
LocationZurich

Prof. Dr. Pierre Collin-Dufresne, SFI Senior Chair, Ecole Polytechnique Fédérale de Lausanne

Factor models have been at the heart of optimal portfolio construction since the original diagonal market model of Bill Sharpe (1963). Recently, there has been renewed interest in factor-based investing among practitioners. The Master Class on “Factor Based Asset Allocation” provides the background to understand factor models and how they are used to build risk-models and investment portfolios. It reviews some of the most popular factor models and discusses benefits and pitfalls of factor-based investing.

 

Context

Factor models have been at the heart of optimal portfolio construction since the original `diagonal market model’ of Bill Sharpe (1963). They are widely used to construct risk-models (e.g., MSCI-Barra factor models) and to estimate expected returns (e.g., Fama-French (1993)). Recently, there has been renewed interest among practitioners to develop factor-based investing approaches (e.g., BlackRock, AQR…). The Master Class on “Factor Based Asset Allocation” provides the background to understand factor models and how they can be used to build investment portfolios. It reviews some of the most popular factors among investment professionals and discusses benefits and pitfalls of factor-based investing. 

 

Content of the Master Class

1-    Factor Models, the APT and the CAPM
2-    The Barra factor model of the covariance matrix of returns
3-    The Fama-French-Carhart factor Model of expected returns
4-    The anomaly factor Zoo: risks, characteristics, or data-mining?
 

Practitioner Objective

Factor Based Asset Allocation for institutional investors: What explains the renewed interest among practitioners? 

 

Target Audience

Institutional Banking, Asset Management, and Investment Banking. 

 

SAQ Recertification

This Master Class is an acknowledged SAQ recertification measure for the CWMA and CCoB profile and comprises four learning hours.