Good question @Donald. All my strategies throw up multiple stocks at a given time and I select the top 10 only based on some particular sorting criteria. So, it is very likely that some great stocks will be missed out.
Let me give an example of a simple system I was testing today. It is a variation (and simplified) version of Mark Minervini’s system. Here I am just looking at stocks which have crossed over the 50 day moving average and also where the 50d MA > 100 d MA > 200d MA. Thats it.
Here are the details.
Backtest results for Modified-Minervini-10.
Periodicity - Quarterly reset. i.e. Buy and hold for 3 months.
Universe - Nifty 500
Test Period - 2007 - 2020
CAGR = 40.04% (without brokerage & slippage)
Max DD (drawdowns) - 51.3%
Top Stocks from 7-Jun-2020
- Adani Green
- JB Chem
- Navin Fluorine
- India Cements
- Escorts
- Divis
- Bayer
- Granules
- Dixon
- Shilpa
- Indostar
- Midhani
- DrReddy
- DMart
- FDC
- Tata Consumer
- Cadila
- Muthoot
- Astrazeneca
- Sanofi
Now, in a system you need to decide how many stocks you will want per bucket. I have done some testing and decided that 10 is a decent number as it gives a good diversification. Also, since I buy monthly for the quarterly strategy, I end up having 30 stocks in a quarter. It makes it like a Sensex - a basket of 30 stocks in a quarter.
It is possible that the 11th or 13th stock actually does better than any of the top 10 in a given month / quarter. The logic is that if any stock continues to do well, it will end up in the top 10 the next period.
The above list has a lot of stocks that you have highlighted. In fact, my Q30 system also has a number of overlaps from the stocks you have mentioned.
The point to take away is that you can design a system that works for you. Also, if you are able to follow the system without any discretion, knowing that at times, it will not work, then you will probably get close to the historic tested returns. I have given the month wise and quarter wise breakup of returns for the Q30 system on the quantamental.in website. It shows how the system would have performed “raw” in both good and bad times.
In the system in this post, you can actually do a lot of fine tuning to improve results (not necessarily return CAGR). A simple index filter and a stock + portfolio level stop loss would have prevented the massive drawdown in 2008 and probably delivered much better CAGR returns.