By Jeffrey Cohen, Investment Advisor Representative
Each night when we run our models we can see the return of the three major US Equity indices we track, the $SPY, the $IWM and the $QQQ. We look at them simply, comparing the close a year ago to the close today, and calculate the difference. Dividends are excluded, and range from ~0% to 1.32% for the $SPY.
Riskfree rate = 1.00%
Actual SPY return = -4.43%
Use floor S&P500 rate = 5.00%
Actual IWM return = -21.81%
Use floor Russell 2000 rate = 5.00%
Actual QQQ return = -10.66%
Use floor NASDAQ 100 rate = 5.00%
Market return = 4.00%
Our expected return from the market moving forward is 4%, which is based on a floor rate of return of 5% for the indices.
We ran our CQNS Down model last night. There were 644 stocks and of note, Draft Kings Inc, $DKNG, has the lowest BETA of 0.048, and Marathon Digital Holdings Inc $MARA has a BETA of 3.46.
The expected return of the down run stocks is 6.49%, and had a variance of 3.8 x 10-4. This compares to a $SPY variance of 1.2 x 10-4.
We do notice some interesting volume action. Some stocks are trading at less than 10% of last year's average volume, while other stocks are trading at 300% or more of last year's average volume. At the extreme, a few stocks are over 500%, and those stocks may be in play.
We also have stocks that are down 70% to 90% from their average trading prices over the past year. These are significant declines, and there are a few familiar names in the list. Bottom fishing targets.
On the other hand, we also have stocks that have appreciated from 30% to 114% over the past year. These may still run, or are reflecting a new macro-economic trend (e.g., oil and gas prices).
We have positive and negative SKEW stocks. Not sure how to analyze them, but I would expect returns may be correlated to extreme skew stocks.
We have another filter to find either leptokurtic and low variance stocks (there are either 3 of them, or 10 of them, depending on how tight we set the variance levels). These stocks have had fewer extreme daily price changes and tend to cluster more around the mean return. In our experience, they are less risky during extreme price movements. However, they can still move.
We also see stocks that are platykurtic with high variance. We set our variance filter to catch 20 stocks that are both platykurtic and exceed a certain multiple of the variance of the all stock portfolio. These stocks have had larger price swings around their mean returns, with both significant advances and declines.
I recognize a few of these stocks as story stocks and active stocks. One is a uranium miner, one a bitcoin miner, one drills for oil, one looks for vaccines, two electric vehicle industry suppliers, and one wants to export liquid natural gas, and one is a cannabis brand. Those examples are volatile based on company-specific factor and external commodities or political factors. These would have more volatility than leptokurtic and low variance stocks.
We also identified stocks with one-day volume spikes, and stocks that rose and fell significantly in a single day. In a way, these stocks may have been 'blown out' and not trade in normal patterns for a while.
Finally, we have negative BETA stocks, low BETA stocks, and high BETA stocks identified by our model. The high-BETA stocks are typically tech or new industry stocks which may require some 'belief' in their stock valuations. We see bitcoin miners, an ecommerce firm, a cloud services provider, a car dealer, and a quantum computing firm.
For the low BETA stocks, we have utilities, REITS, and some speculative names, along with a more industrial set of firms. We note the speculative names, because these may be moving based on specific company specific, or investor-driven sentiment and detach themselves from the movements of the overall market.
We have two negative BETA names in our CQNS down run. These are highly speculative names.
Finally, we have identified all of the stocks with dividend yields actually paid over the past year that exceed a certain level. We use the 'actually paid' dividend model and compare it to the average share price over that same time period, to reflect what an investor actually earned during that period. It is different than a forward-looking dividend yield. In today's run, higher yielding stocks were either investment companies, REITs, oil and gas MLPs, and a shipping company (likely a cash windfall from rising shipping rates).
Have a great day in the markets today.