A Letter from the desk of the CIO: Q2 2020 Review

For this letter, I want to start out with some of the datapoints I find interesting. The market tends to feel rather chaotic when we are in the thick of it, so I think it is helpful to step back and look at the data on a regular basis.

Record levels of money market assets held by investors and institutions

There is a record amount of money market assets held by both retail and institutional investors. The chart below depicts the cumulative total of money market assets of institutional and retail investors (the blue line), with the stock market index (via the Wilshire 5000 index, which is represented by the red line). Relatively high amounts of money market assets tend to act as a tailwind for the stock market as investors redeploy capital. We view this as a positive and investors can only sit in cash yielding close to 0% for so long before they get antsy and start deploying into risky assets. It looks like level of money market assets have already started to turn the corner, which is historically a net positive for equities.

US High Yield Bond Spreads continue to narrow

Spreads for the riskiest bonds in the US continue to narrow, which historically signifies a reduced probability of default by investors. We want to see a further reduction in these spreads, as it will signal that a recovery is underway. To the astute reader, you may notice in early 2008 we had a similar jump in spreads, which consolidated before skyrocketing to record levels. We will be watching this metric closely.

 

Well-functioning bond and lending markets are the backbone of modern financial markets. This chart helps us spot the catalyst of the narrowing spreads earlier this year, when the Fed announced on March 23^rd that it would backstop the bond and lending markets. This is one worth keeping an eye on, but so far it has been effective in staving off any systemic issues in a very important market.

 

 

 

 

 

Source: http://aswathdamodaran.blogspot.com/2020/07/a-viral-market-update-xi-flexibility.html?m=1

 

 

Although the major market indices in the US have recovered to the levels seen before the crisis, behind the scenes the recovery in asset prices has been anything but uniform.

What about if you broke down the YTD return on the S&P 500 by market cap and valuation?

Market-cap weighted indices such as the S&P 500 are designed to weight the constituents in the index by their size. This also means that the returns of the index will be heavily driven by the largest companies in the index. For that reason, the returns on market-cap weighted indices can be misleading as an indicator of a recovery.

This chart breaks apart the S&P 500 into buckets based on market-cap, and shows valuations and YTD returns of each bucket. The top 10 largest companies in the index are driving most of the performance YTD, and outside of the top 50, returns have been negative. To summarize, the largest companies are the most expensive by traditional valuation metrics and have the best returns year-to-date, while the smaller and more reasonable valued companies have gotten cheaper by these measures. The S&P 500 performance is almost entirely dependent on these mega-caps growing in market capitalization. This is worth keeping an eye on, and until the recovery is broader, the recent performance may not be sustainable.

Earnings Estimates

The earnings on the S&P 500 for 2019 was around $163/share. The chart below does a good job displaying earnings estimates for calendar year 2020 and 2021. The average 2020 EPS estimate from the major market strategists has the average at $129.60/share and median at $127.50/share.

Bottom line: the consensus view on the S&P 500 EPS is that we will be back to where we started out for this year by the end of 2021. While this is typical of a recession and could be viewed as a lost year, it is far rosier than what was anticipated in February.

Election Year

For the remainder of this letter, I wanted to provide a view into what may happen in the 3^rd and 4^th quarter with the upcoming presidential election in mind.

DISCLAIMER: I will not be providing a view/opinion on which party wins the election, but rather provide a view into the data and what it has meant historically.

The performance of the US stock market for the 3-months leading up to the election has been a surprisingly good indicator as to which party comes out on top in the presidential elections.

It does not sound believable, so we decided to do our own research on this anomaly. The claim is that if the stock market’s performance for the 3-months leading up to the election is positive, the incumbent party wins, and if it is negative, the challenger wins the election.

I was able to get my hands on the daily price of the S&P 500 dating back to 1928 (data was purchased here: https://www.macrotrends.net/2324/sp-500-historical-chart-data) and was able to put this theory to the test for the elections from 1928 to 2016 (23 total elections). I assembled a dataset of each election result in this period, including the presidential party, date of the election, and whether the party that won remained from the previous term or was replaced by a new party.

Methodology

I tested the hypothesis using a Logistic Regression (details can be found here for those interested: https://realpython.com/logistic-regression-python/) using binary (0 or 1) for whether or not the S&P 500 was negative for the 3-months preceding the election (x variable), and the output was defined in binary terms (0 or 1) where 1 = incumbent wins and 0 = incumbent loses.

Results

Viewing the results for accuracy as a “confusion matrix”:

 

• Correct Prediction of Incumbent Loses in the upper-left position (8 correct)
• Incorrect Prediction of Incumbent Loses in the lower-left position (2 incorrect)
• Incorrect Prediction of Incumbent Wins in the upper-right position (2 incorrect)
• Correct Prediction of Incumbent Wins in the lower-right position (11 correct)

 

Out of the 23 elections we examined, 19 were predicted correctly and 4 were incorrect, which translates to 82.61% correct and 17.39% incorrect.

 

Those that were incorrect were the 1944 (Roosevelt/Democrat), 1956 (Eisenhower/Republican), 1968 (Nixon/Republican) and 1980 (Reagan/Republican) elections.

 

 

 

Below is a full output of each election we examined, with trailing returns and volatility leading up to the election date:

Why does this work?

The stock market has a good track record of discounting future outcomes of events into the current market price based on probabilities, and this is no different. It appears as though the market starts to price in who is favored to win the election starting 3 months out, and the closer we get to the actual election, more information is available as to the probable outcome.

While this was an interesting exercise to perform, the takeaway is simple. If you wanted to try and get ahead of the market and take advantage of thematic bets based upon policy changes and/or make an implicit bet on the election outcome, you would have to make those investments 3 months or more away from the actual election, before they begin to be priced in. This year’s election falls on 11/3/2020, so based on our conclusion above, one would have until 8/3/2020 to adjust a portfolio or make investments that would benefit from your projected election outcome.

This letter is not meant to make a prediction as to who will win. It is important to remember that regardless of who wins the presidential election, the stock market adjusts (negatively and positively depending on the industry and sector) but in aggregate moves forward with business-as-usual.

The 1-year completion rates of Presidential promises made during an election are highly dependent upon which party holds majority of the Senate and House. Morgan Stanley has published research on the subject and have included a chart below to reference.

From an objective perspective, at any point in history from WWII to today, if majority party in the House and Senate lines up with the President’s, that equates to roughly 60% of their “wish-list” getting through as legislation and if they both oppose the President’s views just over 20% get through.

So regardless of who you pick to win, you can most likely slice their list of hot-button items in half to get a more realistic picture as to the best- and worst-case scenarios under either party.

 

 

 

 

 

 

 

 

Which mix has been the best for the stock market historically?

While my research went back to 1928, Jurrien Timmer, Director of Fidelity’s Global Asset Allocation Division, assembled a dataset going all the way back to 1789 and examined what the various outcomes have meant for stock market performance.

The left side of the chart shows the stock market returns for all 4 years of the presidential term broken out by year. This shows that the first 2 years of an election have much lower stock market returns than the last two years, across all historical election outcomes.

Before getting too concerned about the next two years, the returns have still been above 7.5%, which is still much better than what you could expect to earn in bonds in the current environment.

The right section of the chart compares the first 2 years to the entire 4 years, under every historical scenario. While there is a huge disparity for the first 2 years, the returns generated over the cumulative four year term are extremely close across all scenarios, aside from a Democratic win with opposition in Congress (note: this could be due to the small sample size, since it has only occurred 6 times over the entire period.

The chart below shows the same scenarios but plotted continuously over the months of a presidential term. The two lowest yielding scenarios are Republican divided and Democratic sweep, while the remainder of the scenarios are very close to each other by the 48^th month.

Source: https://www.fidelity.com/learning-center/trading-investing/markets-sectors/stock-returns-and-elections

Overall, the conclusion we make from the sample of data presented in the above charts is that the market ends up performing fairly close to the historical average over the full presidential term, with random outcomes in between. The stock market always adjusts under even the most extreme scenarios and moves on to business as usual. There are short-term differences, but in the long-term, they tend to move closer to the average.

To summarize, if you believe that the opposing party will win the election, then the 3-months leading up to the election may be negative and very volatile. If you believe that the incumbent party will remain in office, that means you also believe the market will be positive during the 3-months leading up to the election. I do not know what the outcome of the election will be, but our job is to stay as neutral as possible and figure out the best path forward from a portfolio construction context. If history is any guide, the three months leading up to the election may provide insight into the party that ultimately wins in November. We believe that the next four months in the market will be more volatile than it has been over the last few years, so it is important to have a financial plan and stick to it. Discipline will be the key to success for the rest of the year.

Sincerely,

DISCLOSURES

The Wilshire 5000 Total Market Index is the broadest stock market index of publicly traded American corporations. It is often used as a benchmark for the entirety of the U.S. stock market and is widely regarded as the best single measure of the overall U.S. equity market. Tailwinds refers to or describes a situation or condition that will move growth, revenues, or profits higher. ICE BofA Option-Adjusted Spreads (OASs) are the calculated spreads between a computed OAS index of all bonds in a given rating category and a spot Treasury curve. An OAS index is constructed using each constituent bond’s OAS, weighted by market capitalization. S&P Global Ratings fund credit quality rating, also known as a “bond fund rating,” is a forward-looking opinion about the overall credit quality of a fixed-income investment fund. Visit this link for ratings definitions.