Over the past two weeks we have been running the Chicago Quantum Net Score often, and using the information to make trades. Overall, the model has been right (even today), but that does not make stock trading easy. We are making decisions on buying, selling, holding and leverage and these impact our results.
Here is one example:
We ran the "UP" version of the model and did some (not much) due diligence on the top 50 stocks chosen.
1) The top 25 were high expected return stocks (high BETA values) with relatively low risk.
2) The next 25 were low risk (low closing price variance) with relatively high expected returns.
However, the top 25 stocks were 'story' stocks concentrated in a few industries, and so holding those meant taking a position in a market segment. It also meant investing in growth stocks that in some cases had poor fundamental business results.
The next 25 stocks tended to have stronger fundamental business results. Some of them trade in somewhat regularly repeating patterns (up and down).
3) Finally, another set of stocks we evaluate have a leptokurtic distribution (fat tails) and low price volatility. These are stocks that do not move very much each day, but have moved explosively over the past year more than would be suggested by a normal distribution. In other words, they are mostly 'safe' but occasionally jump up or down.
This third set of stocks also may have a trading range and somewhat regular periodicity or repeating patterns. They also tend to have lower cost options premiums, so their options can be purchased and if the stock 'leaps' during your option period in your direction, you could make a profit. In some cases, you can buy puts and calls at the money, and if the stock moves explosively in any direction, you can make a profit.
However, all of these signals and 'edges' you may have in the market may not be enough for a value-based investor that does fundamental research to take a position in a stock. Just because a stock, or portfolio, is efficient, it may not be compelling enough to go long the stock(s). Just because a stock or portfolio is inefficient, it may not be compelling enough to short it/them.
What are we looking to add to the model?
1) We are looking at adding a data validation filter before we do the 'solving' that incorporates a fundamental valuation 'hurdle.' For example, our UP run may only include well performing businesses and our DOWN run may only include poorly performing businesses.
- There are many historical metrics we can use, and we are beginning our experimentation now.
2) Our Chicago Quantum Net Score balances out the effects of expected returns and price volatility. Would we consider shifting that balance to more greatly favor either returns or risk? We have this as a parameter today, so clients can request higher or lower risk runs. Maybe this is a factor where we can do more analysis?
3) Regardless of how we change the model, we could add additional data to the output spreadsheet where we provide clients the CQNS score for every stock that passed data validation. Here is the data we include today (with an example):
There are six metrics for each stock:
Please add comments to this post if you would like to start a dialog, or email me directly at firstname.lastname@example.org.
Good luck to all.