The following is a chart of IBM, using what I call my dollar ($) indicators.
The Friedrich Research System is made up of 23 original abstract ratios that when combined generate a “buy” as well as a “sell” price for all stocks analyzed. After numerous successful backtests of the system, the results have been found to be very powerful, outperforming the markets in all cases.
In viewing this analysis please concentrate your attention on the price line (red), the buy line (purple) and the sell line (blue) in the chart above. In Part 1 of this analysis our key numerator used is price (stock market price or as Benjamin Graham used to call it, “Mr. Market”). Every day that the stock markets of the world are open, fractal geometry is ever present in the form of millions of trading decisions made by millions of individuals, computer systems and corporations. As an individual it is impossible to process all this information that we are constantly being bombarded with and then to successfully do a complete qualitative as well as a quantitative analysis of all stocks simultaneously without having some form of an algorithm to do the work. The algorithm that I created to do just that I call “Friedrich” (named after the German philosopher Friedrich Nietzsche who was the father or “abstract thinking”).
In the age of gigabyte processors and terabyte drives, I now have the tools at my disposal to allow a simple laptop computer to analyze some 14,000 stocks in a 48-hour period in the same manner that it would have taken 10,000 analysts a year to do in the 1950s. The secret to all this in the end is the agorithm or Friedrich and how it can successfully piece all the 300 million calculations that are necessary to analyze a complete universe of the corporations that trade on the U.S. markets as well as ADRs. Friedrich should work the same in all global markets, but unfortunately I don’t have access to the data I need to run it successfully outside of the U.S. markets.
The following are the results of just one of those 14,000 analyses that my system has generated. In order to do a successful backtest one has to go as far back as one can. I have some data on IBM going back to 1962, but have complete data going back to 1973 and that will be our starting point. I used my birth date of March 23 as the market price in all calculations for consistency. For 2012 I used the most current market closing price.
We will begin with the date March 23, 2000 in the chart above as that is when IBM’s market price broke through its sell price for the first and only time in the 39 years under analysis. Had you sold the stock at that point you would have saved yourself from a drop of -24.63% over the next six years. On March 23, 2006 you would have then noticed that IBM not only broke below its buy line, but also broke below its “true value” and “cumulative owner earnings” lines as well.
The “true value” result is the combined ideal market price results of each of my 23 abstract ratios. Basically what I do is find out what the ideal market price is for each ratio and then combine them all and divide by the total. This result is what I believe to be the ideal price per share that a businessperson would pay for the whole company on Main Street, but at the same time get the "deal of the century."
Other systems like the “Two Stage Dividend Discount Model” found in Robert Hagstrom’s “The Warren Buffett Way” (page 126) may tell us what the fully valued price would be under certain discount rates, but Friedrich actually gives you the actual bargain price that a private buyer on Main Street would get in buying the whole company at a deep discount. I designed it this way in order to follow the thinking of Benjamin Graham, who insisted on investing intelligently, and what better way to do so than to realize what the “fire sale” price is up front? Most firms never hit their “true value” price, but sometimes you get lucky.
For those new to my work and prior to trying to understand cumulative owner earnings (COE), I highly recommend that you read these articles that I wrote that introduce owner earnings:
COE is basically the totaling up of all owner earnings per share results of a company for as far back as you can go. COE can be considered similar to book value per share in that it adds up all the owner earnings results of the company into one final result, just as book value per share adds up all assets-liabilities.
COE is a qualitative ratio because it not only shows consistency, but also shows the real earnings power of the company and how well management is employing that earnings power. If a company also has a strong managerial training program in place you will know it because COE measures not just current management, but the results of all the management teams that came before it. Here is a table that represents the COE of IBM from 1973-2012E:
Now that we have the introductions out of the way let us now get back to March 23, 2006 in the chart above. Anyone buying the stock on that day would have been there at the beginning of a new paradigm shift that occurred at IBM, where the company went from being a pure mainframe computer supplier to a major high-margin, high-FROIC (owner earnings return on invested capital) services firm. That same investor would have had a 163% return as a result to date. IBM is just now only breaking out of its “true value” price point and can be currently bought at 65% of its buy price. With a sell price in the $420 range you can see why Warren Buffett took his Berkshire Hathaway (BRK.A)(BRK.B) cash hoard and recently backed up the truck on the stock.
While Friedrich has the ability to identify strong growth plays it can also be a very useful tool for the value investor/turnaround specialist. To prove this I will point you to March 23, 1994. Had we had Friedrich on that date one would have noticed that its market price dropped below its true value price as well as its SIA price.
Using Friedrich's direction would have generated a very profitable turnaround play as IBM achieved a gain of 749% until we got our sell signal on March 23, 2000. Therefore we have documented proof that had we had Friedrich at the time, we would have bought at a very low price and then sold at a very high price. From 1994 to 2000 we would have made 749% or more on the upside and then saved ourselves from losing -24.63% on the downside by selling in 2000. After being in cash for six years we would have had more fun because in 2006 we would have again had a bargain on our hands and had we bought then, we would have made another 163% return as of Friday's close.
By letting Friedrich pick our moments, we would have made a bundle investing in IBM and still protected ourselves from the downside. Investing $10,000 in IBM in 1994 with Friedrich would have grown to $223,287 by 2012 (using an IRA account where capital gains were not paid). This compares to $10,000 invested in 1994 in a buy and hold strategy that would be worth $168,306 during the same period. We would have made an annualized return of 18.83% using Friedrich, while an investor who just bought in 1994 and held would have made 16.98%.
In the end there is only a difference of just 1.85% between the gains that a Friedrich user had versus the long-term buy-and-hold investor. But that 1.85% would have resulted in an extra $54,981. By using Friedrich we sat in cash for six years and still beat the long-term buy-and-hold investor. As for our benchmark the NYSE Index, from 1994 to 2012 it went up only 6.21% on average per year. So our one investment in IBM would have beaten the market by over 203% in relative performance and that’s with being out of the market for six years!
In the article above I mention my Statistical Indicator Analysis (SIA) and that is basically my indicator for investor sentiment. Early in the 20th century and prior to that companies were not required to report earnings, so all analysts (we were called statisticians back then) could do was use technical analysis and track stock prices and try to determine patterns where they could trade successfully. I have incorporated my own unique form of technical analysis, which I call SIA, but unlike current technicians, I like to buy below my moving average and not above it like they do. I also use a 3,650-trading-day moving average (about 13.5 years) instead of the common 200-day moving averages being used by most technicians today. I do so because I did 1000s of backtests to determine the ideal moving average, where the most gains could be made and 3,650 trading days came in at the best. The following is an SIA chart of IBM from 1962 to 2012, or 50 years:
From the chart above you will notice that in 2009 IBM's stock price hit its SIA for about four days before shooting up with a real “V” formation. It also did so previously in 1993-1994 and then went up 749% after that. The SIA numbers are different here from the first chart above as this chart has every day since 1962 included (minus the first 3,650 days needed to generate an SIA) while the chart above has only one day of each year (March 23) recorded. SIA as witnessed above successfully incorporates investor sentiment into Friedrich’s programming. Since most investors do little if any research at all before buying a stock, SIA instantly gives a snapshot of their behavior over time.
Here is another chart displaying in greater detail how Friedrich operates:
The actual Friedrich ratio that you see above tests each stock and ranks it from 100% for our highest score to -50% for our lowest score. Just using Friedrich alone would have kept us out of IBM as early as 1991 as it would have broken below our 85% minimum (actually registering a -20% in 1993). If you were a long-term buy-and-hold investor in 1991 you would have lost 46% on your investment in just two years and your total average annualized gain from 1991-2012 would have been 11.45% vs. 16.98% from 1994-2012. This again is not a market timing mechanism but is an individual stock analysis system that treats all stocks the same with zero emotion and zero prejudice.
The other ratios in the chart above are: CapFlow, FROIC and the Michaelis Ratio. The Michaelis ratio is equal to the growth rate + the yield of a company (named after master investor George Michaelis of Source Capital, who unfortunately died in a bike accident). Here is a post I wrote on the Motley Fool Boards back in 1999, which explains his theory. The FROIC is owner earnings return on invested capital, while CapFlow is the percentage of capital spending in relation to a company’s cash flow. An ideal situation is for CapFlow to steadily decrease while FROIC and Michaelis steadily increase. As you can see from chart above this was the case during all the periods that we would have been invested.
Disclosure = I am Long IBM
Disclaimer: Always remember that these are the results of our research based on the methodology that I have outlined above and in other articles previously published. This research is provided as an educational tool and should not be considered investment advice, but just the results of our research. There are many ways to analyze a stock and you should never blindly follow anyone’s work without doing your own due diligence or by seeking the help of an investment adviser, if you so need one. As Registered Investment Advisors, we see it as our responsibility to advise the following: We take our research seriously, we do our best to get it right, and we “eat our own cooking,” but we could be wrong. Please note, investments involve risk and unless otherwise stated, are not guaranteed. Past performance cannot be used as an indicator to determine future results. Strategies mentioned may not be suitable for everyone. We do not know your personal financial situation, so the information contained in this communiqué represents the opinions of Peter “Mycroft” Psaras, and should not be construed as personalized investment advice. Information expressed does not take into account your specific situation or objectives, and is not intended as recommendations appropriate for you. Before acting on any information mentioned, it is recommended to seek advice from a qualified tax or investment adviser to determine whether it is suitable for your specific situation.
About the author:
Mycroft PsarasMycroft has spent most of his life as an equity analyst studying the works of the masters. He is an expert in Qualitative and well as Quantitative investing and lives by the motto of “Capital Appreciation through Capital Preservation”. He has worked as an advisor for friends and family and worked for The Motley Fool Organization for a while. Prior to starting Mycroft Research, he spent the last decade writing investment newsletters and providing research to a large following of clients.
From his work on free cash flow in the investment process, Mycroft has now decided to bring his theories to the field of money management as well as work as an independent consultant for Hedge Funds, Pension Funds and ...More Institutions in general. His dream is to someday soon open a mutual fund where he can help as many people as he can benefit from what he has learned over the years.