Examining Industry Alpha Trends & Opportunities for Diversification

We’ve been receiving a lot of good feedback on our alpha-driven series, including last week’s dissection of alpha trends at the Industry Group (GICS2) level since Jan 2018. To wrap up our alpha trends series, we’ll pull the lens back to perform more of a longitudinal study, so we can get an understanding of how these industry trends have played out since 2007.We’ll also provide our weekly market and factor update.

‍Historical Industry-Level Alpha-Driven Stock Analysis

As a reminder, we define “alpha-driven” stocks as securities where 2/3rds (67%) or more of total volatility is idiosyncratic. In other words, the majority of their risk is company-specific, and not subject to external forces (geopolitical or economic news, sector moves, etc). We call these alpha-driven moves “idiosyncratic risks” or “specific risks". Last week, we focused on a few industries that exhibited certain characteristics - 1) consistently high/low alpha (i.e Energy), 2) declining alpha (i.e Software), and 3) increasing alpha (i.e Retailing). Now let’s see how these have trended in the longer term, relative to the Russell 3000.For this analysis, we show the percentage of alpha-driven names in selected industry group as well as the greater Russell 3000, measured the first of each month, from 1/1/07 to 12/31/19. There were several macro shocks that affected the market during this date range, which we’ve labeled using the legend below so it’s easier to see which periods were more factor-driven than others. This also allows us to see which industries track the market more than others during these circumstances.Factor-Driven Market Periods:

1. Lehman Brothers / GFC - Sept 2008 - April 2009
2. Sovereign Debt Crisis (Part I) - April - Nov 2010
3. Sovereign Debt Crisis (Part II) - May - Oct 2010
4. Factormageddon - Jan - Mar 2016
5. 4Q18 Pullback - Oct-Dec 2018
6. Momentum Crash - Sept 2019

Sub-Industry Trends: Retailing

After starting with a high percentage of alpha-driven names in 2007 (77%), Retailing moved in step with the Russell 3000 during the factor-driven periods. Starting in 2012, alpha-driven stock picking opportunities in Retailing have exceeded those in the broader market and currently sit at 71.2%

Sub-Industry Trends: Pharma & Biotech

We had flagged Pharmaceuticals as a group that has been consistently alpha-driven since 2018. Looking back, we can see that it started at 93.8% alpha-driven in 2007 and now sits at 89.8%. During the market pullbacks, the group did become more factor driven, but has always tracked well above the Russell 3000.

Sub-Industry Trends: Software & Services

In last week’s analysis, we saw how alpha among the Software space has been on the decline since 2018. In 2007, the group was 95.7% comprised by high alpha names, vs. 57.3% today. While alpha-driven names had typically tracked higher than the market, we can see that it has fallen in step with the Russell 3000 over the past couple of years.

Sub-Industry Trends: Energy

Energy is a group that has generally been consistently lower alpha-driven than market, starting in 2007 at 17.6% and now sitting at 33.1%. Interestingly the 2014 energy crisis period marked a short decoupling of the sector from the broader market, which created a larger number of alpha-driven opportunities.

Relative Industry Alpha

In the above charts, we’ve observed how industry alpha trends (RED) tend to track the broader market (BLUE) in percentage of alpha-driven names. However, a more nuanced metric is the *relative alpha of any industry group to the broader market*. The relative alpha of an industry group is a simple difference metric:

‍Relative Industry Alpha = %Alpha Driven Names (Industry) — %Alpha Driven Names (Market)

‍Below are the relative industry alpha levels for each GICS2 Industry group vs the Russell 3000:

Why is this metric important?

This very simple metric can be used to determine which industries tend to trend together (or in opposing directions) in their alpha levels helping portfolio teams better prioritize research opportunities and design factor hedging programs.

Below we show relative alpha correlation between each industry group from 2007-2020.

There are two ways to use this data:

1. Alpha Diversification - By combining industries with negative relative alpha correlation, a cross-industry fund can design natural alpha diversification in their stock selection process. For example: an Energy PM is potentially best paired up with an Insurance PM, as their relative industry alpha levels tend to move in opposite directions over longer periods of time.
2. Common Hedging - If two industry groups have high relative alpha correlation to each other, then a cross-industry fund can design a factor hedging program that will protect both sectors during factor-driven periods. For example, Energy & Transportation, Retailing & Apparel, Software/Services & Tech Hardware.
   

US & Global Market Summary

US Market: 1/3/20 - 1/9/20

• The major US indices hit intraday highs before closing slightly down on Friday (not captured in above chart), capping off a week where they all posted gains despite heightened uncertainty stemming from the latest Middle East crisis.
• The US Labor Department provided disappointing jobs and wage numbers for December, with jobs coming in at +145k vs. +165k consensus. Unemployment remained at a 50-year low at 3.5%.
• A Chinese delegation is expected to arrive in DC on Monday to complete the first phase of the trade agreement.

‍Factor Update: US Model

• The rally in Value continued at a slower pace, as it now approaches Overbought territory.
• Volatility has started to retreat from its Jan 2 high of +1.59 SD above the mean, and remains an Overbought factor
• Growth had fallen from its recent 12/9 peak of +2.01 SD above the mean, and appears to now be ticking up after hitting +1.1 SD above the mean on 1/6.
• Profitability ticked up slightly, enough to shed its Oversold designation
• Momentum continued to revert from its recent peak of +1.31 SD above the mean on 12/9
• Earnings Yield has continued to sell off on a normalized basis, sitting squarely in Oversold space
• Market Sensitivity was the week’s biggest loser, falling 0.27 SD towards the mean
• US Total Risk (using the Russell 3000 as proxy) fell by 0.15%

‍Factor Update: Worldwide Model

• Global Growth saw the biggest move up, +0.38 SD in the past week and heading towards Overbought space.
• Exchange Rate Sensitivity continued to see gains after surpassing the mean last Friday
• Earnings Yield continued to tick down, and currently sits in between a Oversold and Extremely Oversold designation.
• Value has been reverting from its recent high of +2.01 SD above the mean on 12/26, and was the biggest loser this week
• Volatility and Market Sensitivity both saw slight declines
• Global Risk (using the ACWI as proxy) decreased by 10bps

Regards,
Omer