The Complexity of Factor Exposure Analysis

Searching for Truth

October 2019. Reading Time: 10 Minutes. Author: Nicolas Rabener.


  • Factor exposure analysis is essential for performance and risk contribution
  • However, the results vary depending on methodologies, factor definitions, and other assumptions
  • A holdings-based approach is preferable over regression analysis


A large part of a capital allocator’s job is to be a detective and solve puzzles. A never-ending puzzle is explaining past performance and risk drivers, especially when capital allocations went wrong as humans suffer from a negativity bias, which is the notion of being more influenced by negative than neutral or positive events.

For example, there will be far more scrutiny of a fund manager that has underperformed compared to one that has outperformed his benchmark, although both should deserve equal attention. Although fund managers insist on being highly skilled, most excess returns, regardless if positive or negative, are explained by simple exposure to systematic factors.

A popular tool for detective work on fund managers is factor exposure analysis, which provides essential insight into which factors have been driving past performance. However, there are different methodologies and data sources that can be used, which make such an exercise as much an art as a science.

In this short research note, we will highlight the complexity of factor exposure analysis by investigating the performance drivers of a relatively simple equity portfolio.


We define the investible universe as all European stocks with a minimum market capitalization of $1 billion and utilize the sequential model to create a long-only multi-factor portfolio. First, we select the smallest 50% of all stocks ranked by market capitalization, then take this portfolio and select the cheapest 50% of stocks ranked by a combination of price-to-book and price-to-earnings multiples, and finally select the best performing 50% of stocks from this portfolio ranked by their 12-month performance, excluding the most recent month (read Multi-Factor Models 101).

The final portfolio features approximately 140 stocks that are small,