Factor Investing Through Principal Component Analysis

Factor investing is a well-known investment strategy used mostly by quant funds. Even though the factors are well published, it’s important to distinguish 2 types of factors:

  • Explicit factors: these are for example momentum, value, size, quality, etc.
  • Implicit factors: these are statistical features determined by using e.g. maximum likelihood, Principal Component Analysis (PCA).

Thanks to their statistical nature, the implicit factors are often easier to calculate. However, we often don’t know what they exactly are, and we need to use intuition and statistical techniques in order to assign sensible economic variables to them. For example, in fixed income, the first PCA eigenvector (factor) can be interpreted as the level of the interest rate, while the second one would be the slope of the yield curve. Similarly, in the volatility space, the first eigenvector would be associated with the volatility level, while the second one would be the volatility skew.

Applying Principal Component Analysis to a basket of stocks and assigning sensible market variables to the eigenvectors is, however, less trivial. The first eigenvector is usually the market level, but the second and higher ones often carry no meaning and/or are difficult to interpret.

To mitigate this problem, Reference [1] proposed a so-call Hierarchical Principal Component Analysis (HPCA), a variant of the PCA method in which stocks are divided into clusters that are believed to share common features such as an industry sector, a country, or a statistical measure,

To mitigate this problem and account for hidden risk factors, we adopt a purely statistical technique. This is a simple and still powerful tool that dynamically adapts to changes in market conditions over time, which makes it suitable for managing trading portfolios. Also, it is a parsimonious approach since it does not rely on too many parameters. The user only needs to define the number of clusters, which depends on the number of K eigenvectors, without specifying any other parameters or hyper-parameters

Using a statistical clustering technique, the authors developed an investment portfolio and managed to outperform the market,

To illustrate an application, we show it in the context of portfolio optimization for the US stock market. We provide evidence that using HPCA statistical-based factor models outperform other classical portfolio construction methodologies such as the shrinkage covariance matrix and the HPCA GICS-based factor models.

We find that it makes sense to use statistical features to partition stocks into clusters. We believe, however, that the traditional PCA can also be used, in conjunction with common sense and intuition, to identify clusters; e.g. we were able to use the second eigenvalues to divide utility stocks into regulated/unregulated groups. Similar results were also obtained in the fixed income space.

References

[1]  M. Avellaneda and JA. Serur, Hierarchical PCA and Modeling Asset Correlations (2020). https://ssrn.com/abstract=3903460

Further questions

What's your question? Ask it in the discussion forum

Have an answer to the questions below? Post it here or in the forum

LATEST NEWSStocks slide with all eyes on debt-ceiling vote: Stock market news today
Stocks slide with all eyes on debt-ceiling vote: Stock market news today
Stay up-to-date with the latest news - click here
LATEST NEWSKey product announcements boost NVIDIA's AI position - BofA
Key product announcements boost NVIDIA's AI position - BofA
Stay up-to-date with the latest news - click here
LATEST NEWSUK has bigger core inflation problem than other economies - BoE's Mann
UK has bigger core inflation problem than other economies - BoE's Mann
Stay up-to-date with the latest news - click here
LATEST NEWSSkyline Corporate Communications Group, LLC Joins the Canadian Securities Exchange Directory as an Approved Service Provider for Investor Relations Services
Skyline Corporate Communications Group, LLC Joins the Canadian Securities Exchange Directory as an Approved Service Provider for Investor Relations Services

NEW YORK, May 31, 2023 (GLOBE NEWSWIRE) — Skyline Corporate Communications Group, LLC (“Skyline” or the “Company”), a consulting agency that provides customized corporate communications and strategic investor relations advisory services to publicly traded and pre-IPO companies, is pleased to announce its approval as a…

Stay up-to-date with the latest news - click here
LATEST NEWSIZEA Wins Three Gold Awards at 2023 TITAN Business Awards
IZEA Wins Three Gold Awards at 2023 TITAN Business Awards

Company Honored for Excellent Achievement in Influencer Marketing Campaigns Orlando, Florida, May 31, 2023 (GLOBE NEWSWIRE) — IZEA Worldwide, Inc. (NASDAQ: IZEA), the premier provider of influencer marketing technology, data, and services for the world’s leading brands and agencies, today announced that it won three…

Stay up-to-date with the latest news - click here

Leave a Reply