Factor Exposure因子暴露

• Overview

  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.

  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.

• History

  Factor investing got its start - or at least its big break - with the publication of a series of papers in the early 1990s by Eugene Fama and Ken French, lightly titled “The Cross-Section of Expected Stock Returns”, “Common risk factors in the returns on stocks and bonds” and “Size and Book-to-Market Factors in Earning and Returns”.

  In those papers, they developed the idea that the returns of any given stock or portfolio can be beoken down into three core explanatory factors:

  • SMB: Small-minus-big, or the size difference between stocks or portfolios
  • HML: High(book value minus low(book value))

  The popular financial press has spent 25 years repeating the basics over and over again to the point where most investors no longer even think about it as a theory; they think about it as simple truisms.

  The basic view of the market became even more ingrained when Morningstar introduced the “Style Box” as a way of short-handing active managers based on their holdings.

  Since the 90’s, two things have happened that have advcanced our thingking about factors:

  1. Lots of additional data has become easily available, leading to myriad ways of measuring even the obvious factors like “value”
  2. Academics have done a lot of work identifying new factors based on all that data.

• Factor Exposure Analysis

  Factor exposure analysis can be conducted top-down by regression or bottom-up via a holding-based approach.

  The former only requires return data while the latter demands the portfolio constituents and factor ranks for all stocks, which makes it the more complex approach from a data and computational perspective. Given different methodologies, the results will likely be only approximately similar.

• Factor exposure analysis : holdings-based

  We generate a holdings-based factor exposure analysis and observe that the strategy had signigicant exposure to the Size, Value, and Momentum factors, which is expected given that the portfolio was constructed to contain small, cheap, and outperforming stocks.

• Factor exposure analysis : regression-based

  We conduct a top-down factor exposure analysis using regression analysis, which is the most commonly used methodology as historic holdings data is typically not easily available for funds or complex to analyse in the case of multi-asset portfolios.

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• References

1. THE COMPLEXITY OF FACTOR EXPOSURE ANALYSIS
2. Measuring Portfolio Factor Exposures: A Practical Guide
3. How to Understand a Portfolio’s Risk Exposure
4. Feeling the factors: how to represent factor exposures
5. How To Measure Your Factor Exposure
6. Factors and Factor Exposures
7. Factor exposures of smart beta indexes