Federal Open Market Committee (FOMC) sets policy for the Federal Reserve.
Data to Monitor Once A Month \\ 50 Plus Year Average \\ Acceptable Range
Consumer Price Index YOY (CPY) Avg 3.8% Range 3%-5%
Case Shiller Home Price Index YOY (HPY) Avg 5.29% Range 3.5%-7%
Unemployment Rate (UR) Avg 5.73% Range 4.5%-7%
Tools Available
Raise/lower Federal Funds Rate if CPY is too high/too low
Raise/lower Federal Mortgage Rates if HPY is too high/too low
Raise/lower Quantitative Easing if UR is too low/too high
Auction Off Troubled Financial Institutions
Financial Institution Tests
Uninsured deposits / domestic deposits < 50%
Loans and held-to-maturity securities / total deposits < 50%
Financial Institutions that fail both tests for two consecutive quarters are auctioned off.
RESULT: No peer pressure, no human error, no recessions!
FOMCGPT(tm) To The Rescue!
A ChatGPT bot (FOMCGPT) has been designed to generate Federal Reserve policy once a month incorporating the algorithm above using easily modifiable configuration files. It is being tested using Federal Reserve FRED data from April 2021 to April 2023.
First, mutual funds were created where your broker bought a batch of stocks at the end of a day for the long term. These have usually been industry aka sector specific.
Next, Electronic Traded Funds or ETFs came along that did the same as Mutual Funds with a long term bias by industry/sector but they have been allowed to be traded in real time.
Then came Inverse ETFs which were ETFs but instead of long positions of stocks, these were only invested in shorted stocks for a particular sector or index. Surprisingly, Inverse ETFs have not garnered a huge following though they perform well during a recession.
One unique recession strategy involves timing the move from regular ETFs to inverse ETFs. But timing the market is well known to be very difficult and very risky.
What’s New?
Vanguard created possibly the first long-short ETF called the Vanguard Market Neutral Fund or VMNFX. It tries to balance long investments in profitable growth stocks with short sales on failing stocks.
This is intriguing. Most analysts already concentrate on an industry/sector and know the best companies as well as the worst ones. These analysts already know which ones will beat and which ones will miss earnings estimates.
Unfortunately, Vanguard makes several mistakes with VMNFX in terms of the long/short mix and the industry/sector mix.
First, VMNFX is neutral meaning it is 50% long and 50% short ALL THE TIME as seen in the following portfolio composition figure.
Even though this last year has seen VMNFX beating many normal ETFs, it does NOT increase the percentage of long stocks when the market is going up NOR increase the short position when the market is going down. In other words, VMNFX never changes it long/short percentages.
Secondly, the VMNFX fund is charted to analyze A LOT of industries, as the previous composition figure shows, but with just a few analysts. A group of normal ETFs covering many industries would have hundreds of analysts in comparison. Consequently, the VMNFX fund has grown a mere 20% in 10 years as seen below in a hypothetical growth chart.
Is There A Better Way?
A better approach would be to have a group of sector specific ETFs that invest in a mix of long stocks and shorted stocks. These could be called Super ETFs because they are profitable in good times and could easily make money even in bad times like a recession. How is that possible you ask?
Besides concentrating on a particular sector, Super ETFs would have several fund managers to determine the variable ratio of long purchases versus shorted positions. For example, the ratio might be 75% long and 25% short during a normal market year.
Fund Manager 1 might suggest 70% long and 30% short. Fund Manager 2 might suggest 80% long and 20% short. The third Fund Manager might agree on the average of the other’s recommendations and end up with 75% long and 25% short.
But soon as a recession approaches, such as this year, a Super ETF might have a higher percentage of short positions. For example, 80% short and 20% long.
Fund Manager 1 might suggest 85% short and 15% long. Fund Manager 2 might suggest 75% short and 25% long. The third Fund Manager might agree on the average of the other’s recommendations and end up with 80% short and 20% long.
Returning to Vanguard’s long-short VMNFX ETF, it is clear from the many industries in the portfolio above that the goal was to automate a complete investment portfolio mix for any investor. But as already mentioned, this new long-short investment product took on too many sectors and, thus, failed to consistently beat other ETFs. For example, the Fidelity Biotech ETF (FBIOX) grew 50% in ten years compared to VMNFX which grew only 20%.
Why Super ETFs?
A Super ETF has the higher probability of success because of its variable long-short mix yet dedication to a single sector as most normal ETFs do today.
Each Super ETF could also use the following standard recession predictors to change the percentage of long versus short positions.
1. Yield curve – Treasury 10 -year minus 2 year rates
2. Dow Jones Transport Index
3. GDP Now
4. VIX – Volatility Index
A better example than VMNFX is the 18-month old Leatherback Long/Short Alternative Yield ETF or LBAY. As the following LBAY performance chart shows, it has grown from $24.50 per share to $29 YTD or 18% when most stocks are down this year. Also, it has grown 45% since its inception in November 2020. By the way, FBIOX is down 46% in that same 18 months.
But unfortunately, LBAY is hindered by the same philosophy as VMNFX of using a few analysts to cover a lot of industries/sectors.
How Should We Invest In Multiple Sectors?
Multiple Super ETFs can be used to achieve the intended VMNFX and LBAY goals of automation of a complete investment portfolio for any investor.
Several brokerages have created Robotic aka Software Tools such as Fidelity’ Go that invest in a mixture of funds, usually their own. The goal of these is similar to VMNFX and LBAY: create a complete, automated, diversified portfolio. But Fidelity Go does not perform well in bad times because all of its ETFs are normal ETFs invested only in long-term products.
A RoboInvestor software tool could be designed to utilize just Super ETFs. That way it leaves the long/short mix to the funds and can concentrate on creating a diverse portfolio.
A RoboInvestor can take advantage of cyclical or seasonal sector trends. It is common knowledge that cyclical stocks are volatile and tend to follow trends in the economy. Also non-cyclical stocks tend to outperform the market during an economic slowdown. For example, the RoboInvestor can invest in the retail sector before the end-of-the-year holiday rush and increased consumer sales. The RoboInvestor can also rotate out of cyclical stocks and into defensive stocks depending on where in the business cycle the economy is headed.
Here is a chart of historical performance of sectors across the business cycle that Fidelity provides and that a RoboInvestor tool can use.
Summary
In conclusion, you do not have to sell your soul to have an automated successful investment portfolio. That is because investment success can come with a combination of Super ETFs and a RoboInvestor. Together they can automate a complete investment portfolio for any investor.
Super ETFs will have a proper mix of long and short stocks in a specific sector. A RoboInvestor software tool will pick the correct mix of cyclical and non-cyclical sector Super ETFs. The result is investors will see profitability automatically in both good times and during bad times such as recessions.
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Michael E Kelley 