We are not just Stock Market Analyst, we are researchers that do applied research to the stock market, develop new stock market indicators, and new models through our going research. I personally spend 20 to 40 hours a month on research and development.
Why is this so important?
I will give you a real example we are working on now. Many market analysts, Mutual Funds, and Hedge Funds have one "key model" that they get all their timing signals from. I spoke to a Hedge Fund Manager the other day, who said that their primary model worked in bull markets, bear markets, and trading ranges.
Does it work "just as good" in all these markets? I asked. "No, the model works better in some markets, and not as good in others", was the answer.
I did not ask how bad "not so good" was. Instead, I asked, "Forget bull and bear markets for a minute, how does it work on large cap stocks versus the NASDAQ 100, and mid cap stocks?" "I don't know, why are you bothering to ask that?", he replied.
Why was "my question" extremely important?
Here's why ... As part of this month's research, I took 1 key variable in one of our Market Models and back tested the profit and lost results from January 3rd. 2006 to August 22nd., 2007. Specifically, I tested the key variable against the "Top 75 core holdings" that Institutions owned, the NASDAQ 100, and the S&P 600 Mid Caps.
Remember, that this was the same market model tested against whether or not a group of stocks were large caps versus Mid Caps. The NASDAQ 100 sits in between the two groups. The three charts below show the performance differences one model can have over differences in capitalization.
Chart 1: Our Mountain Model against the Top 75 Stocks owned by Institutions. (Remember that we only used one key element in out Big Cap Model. Pay particular attention to the % of stocks that made a profit and the % of stocks that had a loss. ... here are the results:
The results were not bad. 84% made a profit and the worse loss was 20% of the capital invested in that particular stock. (I assumed $100,000 was invested in each stock for the test.) Now, you might be saying that this was because they were the Top 75 Institutional stocks ... but that is not the case, because we ran the same test on the DJI 30 stocks and the results were within 1% of this test.
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Chart 2: Our Mountain Model against the NASDAQ 100. Again, pay particular attention to the % of stocks that made a profit and the % of stocks that had a loss. ... here are the results:
The results were not as good. Only 67.3% made a profit and the worse loss was close to double the loss on the large caps .... coming in at 40.75% of the capital invested for that particular stock.
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Chart 3: Our Mountain Model against the S&P 600. Again, pay particular attention to the % of stocks that made a profit and the % of stocks that had a loss. ... here are the results:
The results were worse again. Only 58.4% made a profit and the worse loss was more than double the loss on the large caps .... coming in at 49.18% of the capital invested for that particular stock.
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Conclusion: Investing models have huge differences in Results when they are applied to bull markets, bear markets, trading ranges AND the capitalization size of a stock. Not only did the % of wins drop from 84% to 58.4% on the same model between large caps and mid caps, but just as important, the largest loss on a single stock on the mid caps was 238% larger than the worse loss on the Institutional large caps.
The point is, there is not "one" magical formula that works across all conditions, and there is a critical need for analysts to run their models against multiple conditions and criteria. Have we done all we can do on this yet? I will be honest and say no. We are mid way through our research, and we already see how important the implications are relative to matching our models to capitalizations as well as bull and bear markets. What this means is that we will be coming out with some strategic advances that others have not yet begun to even think about.
This is our "research update" for the beginning of September. Our initial results will have us making some profound modeling changes in the near future that will benefit our paid subscribers. We still have much testing to do in September on this project, such as maximum draw downs, the percentage of winning versus losing trades per stock, etc. We will also being looking at other factors ... such as, what if we reduced the S&P 600 stocks to the top 20% with the highest volume? What would that show for results?
