Why Own What’s Not Bullish?

We are all familiar with the concept of diversification. The idea is to spread your bets across an array of assets in order to avoid concentration into an asset or small group of assets that could induce havoc on a portfolio in the event that any one  bet turns out to be wrong. And since no one in the market has predictive powers, there is always the possibility that some bets will be wrong, even if the decision process behind it was sound. 

One of the most common forms of diversification that we know when it comes to making our investment decisions is to own a mix of stocks and bonds with the thought being that the bonds will provide a level of protection to the entire portfolio in the event of down side in the equity portion of our holdings. While this would have been a drag on total performance in a year like 2019 when the equity market returned nearly 30%, the diversification strategy paid off in the first quarter of 2020. 

However, if we assume a standard diversified portfolio of 60% equities (we will use SPY) and 40% bonds (we will use AGG), you are stuck with the good and the bad in the equity portion. By owning the S&P 500, you own all of its sectors in the same weights as the index. This begs the question, why do you want to own what’s not bullish. Why do you want to own the areas of the market that are not likely to outperform. I say “likely” because, again, no one knows for sure.

What if there was a way to not own the bearish areas of the market? Of course we could employ a strategy that only owns the best performing sectors over a given lookback period and then rebalance at predetermined times. This concept of using momentum as a sector rotation strategy is well known and has been well researched at this point and, for what it’s worth, I am a believer. But in an effort to find new and interesting ways to look at the market and portfolio construction, today we look at this sector rotation concept in a different way. 

Thankfully, our updated white paper for PortfolioWise, written by my colleague, Marc Gerstein, provided the inspiration I was looking for. Instead of using momentum as a starting point, I wanted to see what would happen if a sector rotation strategy was employed using our ETF Power Gauge Ratings. Would we have done better than a simple 60 / 40 allocation between SPY and AGG in the tumultuous first quarter? 

From the paper: Rather than engage in an ultimately futile attempt to present academia-oriented data showing theoretical results of a large statistically random sample, we aim to depict results that could have been obtained through a realistic investor work process; such as that of an investor who seeks to implement a particular strategy by moving into and out of assumed better or lesser ETFs from a manageable-sized menu of choices. A set of work-flow-based rank performance results will be based on the following approach:

  • Start with a limited collection of ETFs that together comprise a realistic menu of potential choices for an investor seeking to implement a particular strategy (choose a large-cap value ETF, choose a cap-agnostic growth ETF, choose a Technology ETF, etc.)
  • Five hypothetical portfolios are constructed for each goal, one comprising ETFs in the group with Very Bullish rating, another consisting of ETFs ranked Bullish, etc. through Neutral, Bearish, or Very Bearish. 
  • Each portfolio will be assigned an index value of 100 as of 9/30/18, shortly after ETF PGR went live.

This is the starting value for the index, and changes in this number signify increases and decreases in the value of the index.

  • It is assumed all ETFs in a mini portfolio will be held in equal dollar amounts.
  • The total return of each portfolio is measured over the course of the upcoming calendar quarter. (If no ETFs are available for a mini-portfolio (e.g., in the quarter ending 6/30/19, there are no Bullish ranked Energy ETFs), it is assumed that the return on that portfolio for that quarter is zero.) The mini-portfolios are then reconstituted at the beginning of the next quarter.
  • Performance tracking ends 12/31/19. So rather than measuring long-term performance, we’re measuring results over a very limited five-quarter period. (As time passes, we will, of course, be able to lengthen our measurement periods.)
  • Trading costs are not considered. 
  • All numbers presented are based on data from S&P Global Market Intelligence via Xpressfeed as processed on ClariFi

First, we’ll look at an updated view of the ranking system’s ability to make bullish or bearish calls on the 11 S&P SPDR sector ETFs:

The above chart suggests it would still be constructive to use the ETF ranks to guide an S&P sector rotation strategy.

Back to Dan: So would we have done better in the first quarter of 2020 when equity markets cratered. First we will look at how a standard 60% SPY / 40% AGG portfolio would have held up. Using the closing prices for SPY and AGG on December 31, 2019 and March 31, 2020 we can see that the return for the first quarter was -10.88% with the equity sleeve declining ~20% and the bond sleeve gaining ~3%. Clearly the power of diversification was on display during the equity drawdown. These calculations assume no dividends.

Data: Chaikin Analytics

But owning SPY meant that we owned the areas of the market which were not bullish in addition to the areas that were. For instance, on December 31st both XLE (Energy) and XLRE (REITs) carried bearish ratings. The same goes for the bond sleeve as AGG actually had a neutral rating in our model on the last day of 2019.

What if we limited our equity allocation to the sector ETFs that had bullish ratings on December 31st and replaced AGG with a bond fund that had a bullish rating? In this case we could have held the following ETFs through the first quarter:

  • XLU – Utilities
  • XLV – Health Care
  • XLC – Communication Services
  • XLK – Information Technology
  • XLF – Financials
  • BLV – Long-Term Bonds

Here is a look at how the above portfolio would have performed in the first quarter without dividends. 

Data: Chaikin Analytics

We can see that the return of the portfolio would have improved by 2.69% overall and the equity sleeve would have improved by over 2%. The bond portion would have done better by more than 3.6%. 

While this is encouraging, and there was a benefit to favoring bullish funds in the strategy, this is by no means an exhaustive analysis. Was the excess return achieved by taking on more risk? Is this simply a one quarter outcome that will not stand up in the following quarters? Can risk mitigation tools be implemented to reduce exposure during the quarter if certain technical measures (such as moving averages) are breached? These are all valid questions which we will attempt to answer in future posts. For now, I am intrigued enough to continue exploring further and will use this approach in my own investment portfolio. 

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