Special Series Part II: Quantifying the Impact of the SVB-Led Banking Crisis - Read-Throughs To Other Sectors

In last week’s special edition of Factor Spotlight, we utilized Wolfe Research’s US Financials risk model on the Omega Point platform to uncover the key forces moving stocks during the banking crisis sparked by the run on Silicon Valley Bank.

This week, we focus on analyzing the same forces through the lens of Axioma and Barra US risk models to build read-throughs into other sectors. Our findings suggest a lopsided result, heavily favoring some sectors and negative on others.

‍Read on to learn what we discovered.

Building Blocks For Our Read-Through Analysis

How can you translate a financials-specific factor (e.g., Receivables Turnover) to the broader market?

A factor in any model can be approximated through a portfolio of assets, often referred to as a Factor Mimicking Portfolio. While several known approaches exist to build a factor mimicking portfolio, we will apply a simple yet powerful portfolio construction approach called High-Minus-Low (“HML”). HMLs can help analyze residual characteristics of a factor and “project” that factor onto additional risk models and datasets.

We built our Factor HMLs using the following method:

• Universe: Russell 3000 Index
• Long Stocks: Top Quintile Based on Exposure to the Target Factor
• Short Stocks: Bottom Quintile Based on Exposure to the Target Factor
• All stocks are equally weighted on the long and short sides of the portfolio

We built factor HMLs for the Wolfe Financials Revisions, Receivables Turnover, Short Interest, Term Spread, Earnings Yield, and Leverage Factors.

The Broad Market DNA of These HMLs

To understand the characteristics of these Financials-focused factors and their potential impacts across the US market, we’ll examine the style and industry exposures of HML portfolios in the Axioma US4 Medium Horizon model and the Barra US Total Market Model for Long-Term Investors.

Let’s start with the Style exposures, which can tell us much about the companies exposed positively and negatively without bias to any one sector.

The table below breaks down the HMLs (columns) into the positive and negative factor movers. It shows the key Barra and Axioma factors that load on these HMLs (rows), broken into the typical categories investors use.

Style tilts positively towards higher Beta & higher Growth stocks and away from Value and Quality stocks.

These findings are surprising, though not wholly unexpected.  Since 2022, the new high beta stock has been more aligned with the less profitable growth-oriented group of securities, while lower beta has been associated with Value and Quality.

We see this phenomenon more clearly when we look at the industry exposures of the HMLs:

The industry tilts strongly suggest a consistent positive alignment towards Software, Biotech, and Semis and a consistent negative alignment towards Oil & Gas and Equity REITs.

While these results seemed aligned with the market reaction we’ve seen over the past few weeks, we used another approach to understand whether a small number of securities in the industry may have skewed the above weightings.  In this method, we counted the number of securities in each GICS Sector that contained a majority positive or majority negative Style characteristics from the Barra and Axioma models that we found to be associated with the HMLs.

We confirmed our findings above in Software & Biotech but also saw a robust positive tilt in Consumer Discretionary. Most surprising are Utilities & Consumer Staples (historically associated with defensive/safety), which appear poorly positioned in this environment.

Next up:  Scenario Analysis

We have identified the characteristics associated with the banking crisis and developed factor portfolios to analyze those characteristics across the broader market.  

Next week, we will use these building blocks to develop scenarios that we can apply directly to any investor portfolio.