By identifying the persistent drivers of long-term returns in their portfolios, investors can understand which risks they are exposed to, and make explicit choices about those exposures. When it comes to information about factors, providers offer analytic toolkits to identify the factor exposures of an investor's portfolio. However, these analytic tools do not employ academically grounded factors and their factor finding process maximises the risk of ending up with false factors. These non-standard factors also lead to mismeasurement of exposures and may capture exposure to redundant factors. In the end, analytic tools for investors do not deliver on the promise of factor investing and they also lack transparency. Additionally, we may question the way in which the measurement of factor proxies is implemented. This webinar will review the issues of factor risk measurement and show how these may be countered.
Overview
Factor investing offers a big promise. By identifying the persistent drivers of long-term returns in their portfolios, investors can understand which risks they are exposed to, and make explicit choices about those exposures.
When it comes to information about factors, providers offer analytic toolkits to identify the factor exposures of an investor's portfolio. However, these analytic tools do not employ academically grounded factors and their factor finding process maximises the risk of ending up with false factors. These non-standard factors also lead to mismeasurement of exposures and may capture exposure to redundant factors. In the end, analytic tools for investors do not deliver on the promise of factor investing and they also lack transparency.
Additionally, we may question the way in which the measurement of factor proxies is implemented. Most popular factor analysis tools used by investors deviate from the models used in research because they choose to use factor scores instead of betas. An additional problem is that the one-dimensional nature of factor scores does not take into account correlations across different factors. This leads to the double counting of the exposures of factors that are highly correlated. Lastly, many popular factor scores combine variables into composite factor scores. Combining factor scores into composite scores makes the mismeasurement problems worse as composites from skewed score distributions may be biased towards one of the variables.
This webinar will review these issues of factor risk measurement and will show how these can be countered.
Slides
To receive the slides from the webinar, please click here.
Host
The webinar was hosted by Felix Goltz, Research Director at ERI Scientific Beta. Dr. Goltz carries out research in empirical finance and asset allocation, with a focus on alternative investments and indexing strategies. His work has appeared in various international academic and practitioner journals and handbooks. He obtained a PhD in finance from the University of Nice Sophia-Antipolis after studying economics and business administration at the University of Bayreuth and EDHEC Business School.
Date/Time
15 November, 2018 at 4.00pm CET.