My Wall Street trading career started at Weeden & Company in 1967.
Weeden was one of a few trading firms that made an over the counter (OTC) market in listed shares.
At the time, commissions were fixed and the commission was the same rate for 100 shares as it was for 10,000 shares or more. Weeden made a market in over 400 listed shares and for many banks stocks which were, at that time, traded only in the OTC market. It was an advantage for an institutional customer to trade with Weeden because his/her net cost would most often be less than to use a NYSE member and pay full commission. An example would be IBM. If the last sale of IBM was, for example, $225, a customer may have been able to buy 1,000 shares for $225,000 on the NYSE plus commission of $0.75 per share for a total cost of $225,750. Or, the customer may have been able to buy it at Weeden for $225.25, for a total cost of $225,250, a savings of $500.
Weeden also traded corporate and municipal bonds and notes.
I started as a trainee on the stock trading desk. The trainees did all kinds of chores like delivering coffee, changing the stock tape, and balancing out the positions of the traders to which we were assigned.
Although Weeden made markets in the largest NYSE issues, there were many smaller listed and unlisted companies that were of some interest to our customers. One of my chores was to keep a record of “Indications of interest”.
When a customer had an indication of interest in a stock that we did not trade, I would pick up the phone and note the customer name, date of inquiry, the name of issue, whether buy or sell, and the current price. I would start a card for the issue if new and note it on the customer’s card. The index cards were the standard 3X5 and we stored them in a box.
If a customer had an interest in an issue we had in the box, and was counter to an indication we already had, that is, a customer was a buyer and we had a seller, I would contact the other party and negotiate a transaction. The “box’ became a nice profit center.
One of the many customers of Weeden was Buffett Partners, a small investment partnership/hedge fund in Omaha managed by Warren Buffett. Although he traded with the main trading desk, he was an active contributor to the indications of interest box. He was getting a reputation as a very successful hedge fund manager. At this time, in the late 1960’s, there were very few hedge funds, perhaps less than ten in all.
Weeden & Company was founded by Frank and Norman Weeden in San Francisco in 1929. The trading migrated to New York City and was run by Frank’s sons Alan, Don, and Jack. They divided up the responsibilities. Don, aka “Dewey,” ran equities, Alan did bonds, and Jack was in charge of operations. Frank and Norman were active behind the scenes. When Frank Weeden came to visit, we often chatted about the Foreign Exchange market, as I had become the New York arm of the London Eurobond trading desk. He was curious about the various foreign exchange (FX) rates and the flow of buy-and-sell equity orders from Europe. He was trying to get a feel of what caused the flow to go from buy to sell and vice versa, against the change in the U.S. Dollar exchange rates. Frank also had an interest in ‘the box’, especially Buffett’s indication of interests.
Mr. Weeden decided to call Buffett and ask about his investment strategy. I think, but I am not certain, that Frank wanted to invest in one of Buffett’s partnerships. I was just a very small spoke in the Weeden wheel and had no idea of what the more senior people discussed about investments. Weeden, being a pure trading firm, had no need for an equity research department.
A few months later, during Mr. Weeden’s visit to New York, we had a cup of coffee in the small dining room adjacent to the trading floor. The conversation was just some small talk about the Euro trading and FX markets. He paused and asked if I had ever heard about Multi Discriminate Analysis (MDA). I answered that I had no idea of what that was. This was early in 1969. He had mentioned that Buffett had mentioned the term.
It wasn’t until many years later that I remembered the conversation. There was an article regarding Buffett and his new friend, Bill Gates. Buffett was explaining to Gates that he used MDA to find investment ideas. He used the Fortune Magazine list of the largest 500 companies, and he went back in time to discover how they got on the list. What were the attributes of small companies that enabled their growth? What characteristics did they have in common? I think that was what Weeden was talking about many years before. Buffett by this time had reorganized his investment strategy around his investment in Berkshire Hathaway and was well on his way to building the greatest investment success story of our time.
Fast-forward in time to the summer of 1986. Jeff Cohen and I had left our jobs at Dean Witter in 1985 where we were both Senior Vice Presidents to start Cohen Feit & Company, a NYSE member firm structured as a partnership. Jeff did risk arbitrage and I did convertible arbitrage. Every summer, we hired some interns to get some experience. This summer we hired a friend of one of our limited partners’ sons. He was from Israel and lived in Kenya. He had finished his Israeli army service and was in the middle of his college years in the United States.
We were subscribers to a credit analysis tool called Zeta Services. Zeta Services was an improved version of Z- Score. Z Score was the concept developed by Edward Altman, a professor of Finance at NYU, which purported to predict a corporate bond default a year or two into the future. It did that by analyzing corporate defaults in the past and using various financial ratios to predict future events. It compared defaults with comparable non defaults using Multi Discriminate Analysis to determine which financial ratios were more important than others over time. Subscription-based Zeta Services, which used additional factors, were more accurate and predicted default more years into the future. The Z Score study had been published in the September 1968 issue of The Journal of Finance. I asked my intern to use our library of a few years of monthly issues of Zeta Services to determine if the Zeta score changes were a predictor of rating services changes. Rating service’s change of bond ratings would have an effect on the underlying bond price value of the convertible bond. Our intern went to the Moody’s library in Manhattan and compared Moody’s ratings changes to Zeta score changes. His results were inconclusive. However he did discover that changes in the overall Zeta score and certain financial ratios did have an impact on equity price changes. Rising and falling Zeta scores had a corresponding effect on the change in equity prices when compared to the Dow Jones Index (DJIA). A company with a rising Zeta score did better on average than the DJIA. The results on falling Zeta scores were more dramatic, as predicted by the basis of the Zeta score design.
Finding the best candidates for swing trading is different than finding stocks for long term investment.
Swing trading is, by definition, the holding of an equity for a few days or a few weeks and to profit in the short term by a rise in price that occurs during this period. Longer term investors make decisions to hold assets for many weeks and probably many months.
Most swing trading recommendations are made based upon technical indicators and not fundamental factor analysis. These technical indicators portray, on a graph, past price movements and have moving averages and/or oscillators that indicate that these past movements will have their trend continue somewhere into the future, based on the concept of the persistence-of-trend continuation.
Mr. Buffett made long-term equity purchases based on the idea that certain financial characteristics of smaller growing companies would enable them to succeed to become much larger companies in the future.
Mr. Buffett talks about the idea the ideal company is a castle and its management is its resident knights. The ideal castle is surrounded by a moat. As Berkshire Hathaway Vice Chairman Charlie Munger states: “The only duty of [the] corporate executive is to widen the moat.”
To find the best candidates to purchase for swing trading, first, I find those companies with wide moats. The wider and deeper, the better. I use basic financial statements and ratios like free cash flow, asset turnover, return-on-equity, assets, and others. I compute these numbers and ratios in most public companies and sort the results into many baskets. The best companies, those with large moats, are put into the best basket. That basket contains those equities that will be purchased. Second, I found that certain metrics are most important for shorter-term price movement. These metrics were discovered when, instead of looking at financials that influenced long-term growth, I looked at those metrics that impacted shorter-term price movement. I found these metrics by looking at stocks making new highs, and worked backward like Buffett to discover why. Third, shares were purchased when my PerfectStorm technical indicators suggested they are in an upswing. The result is that a significantly difficult problem, when and how to successfully swing trade, has been solved.
The following example of Sherwin Williams Company should illustrate the point.
Near the end of the year, approximately December 16,2011, SHW was purchased at approximately 85.5 as indicated by the up arrows. This position was sold in the middle of August 2013 at approximately 175. During this time the financials of SHW indicated an expanding wide moat. The long position was re established at the beginning of November 2013 at approximately 185. This position was sold during early July of 2015 at approximately 280 During this period, SHW was exhibiting sound financial reports. It still does, but PerfectStorm indicates no long position.
The basic strategy is be long the wide moat financially secure best basket equities when Indicated by the PerfectStorm indicators, and to be short the weaker, worst basket no moat equities when appropriate.
Examples will be available in the next update.