Why most money managers cannot beat the market, Part ONE

Some years ago, in 1988, I was asked by my friend Paul Singer of Elliot Associates, to represent the convertible arbitrage practitioners at an annual Convertible Securities conference in New York. Paul had been the speaker the year before and advised me to keep the dialogue going with the large convertible fund managers such as the many mutual funds that had increased as convertible securities became popular as a separate asset class. Paul and I had spoken many times as Cohen Feit and Elliot were one of the few funds that specialized in convertible arbitrage. We met often at lunch’s sponsored by investment bankers to promote new issues of convertible securities.

At the conference, I spoke about my firm, Cohen Feit and how we allocated capital between the various strategies we employed. I then delved into one of my pet peeves about the new, non-traditional convertible issues that were being promoted by the various bankers on behalf of their clients, the corporate issuers. I was very much against the issues that were oftentimes so complex that I just stopped looking at them at all. I complained to the audience that perhaps they should do the same. Just say NO. Hands raised and I called on one of the largest mutual funds managers in attendance. His response was echoed by many others. He had to buy almost anything that came out because his fund was growing and, wait for it… he had to almost fully invested at all times. He did not have my luxury of just saying no. He would have been penalized by his parent managing company for not being at last 95% invested at all times in various convertible securities. All of the other funds in the parent companies’ family of funds had the same requirement. Full investment. Never mind the market condition, stay invested.

The result, when the crash occurred the previous year, they had to lose. They had forgotten Warren Buffett’s famous saying: “Only when the tide goes out do you discover who’s been swimming naked.”

So, reason one on why most if not all managers cannot beat the market: They have to be fully invested. Even the index funds contain the good, the bad and the ugly.

Overall Market Signal

The following chart of the ES Future which represents the S&P 500 index clearly shows that the U.S. equity market, as measured by the S&P 500 index clearly showed a negative daily bias starting at the close of October 4, 2018.

The ES has started to recover in 2019.

For more information on how this kind of quantitative approach can help you with your investment goals, please contact me at rfeit@msn.com or by phone at 516-902-7402.  Also, look at www.medallionreasearch.com for more information.

Prices are as of 8 AM, July 11, 2019

Daily

 

 

Alibaba

According to Bloomberg on May 28, 2019 “Alibaba Group Holding LTD. is considering raising $20 billion via a secondary listing in Hong Kong after a record breaking 2014 New York debut, people with knowledge said, a mega-deal that will bring China’s larger company closer to friendlier investors at home as U.S. tensions escalate.”

If this occurs, trading hours will expand to over 20 hours 5 1/2 days a week.

I have attached a daily graph of BABA as of 8 AM today, Thursday, July 11, 2019 below.

 

 

Apple

Apple shares declined on news that sales of the iPhone were declining because of the downturn of consumer spending in China. Whether you believe that spin or realize that the costs of the new models do not offer a serious reason to upgrade the older phones is of little concern. Whatever the real reason, Apple stock went down.

Apple is one of the most widely held and more importantly one of the more actively traded stocks in the world. Share and options are traded with very small bid-asked spreads.

From a swing trading perspective, the following attached charts should be of some interest.

Daily prices are at 8:00 on Thursday,  July 11 , 2019:

 

 

 

Relative Strength with Momentum

Readers of the swingtrader.com blog will have noticed that the overall theme that I have proposed is that to be a successful swing trader one has to understand the principle of relative strength with positive momentum.

I originally proposed the concept in an e-book that I had offered in 2000. Because of my lack of web marketing, it was only downloaded a few times, although it was free.

Over the past few months, I have received multiple offerings of momentum services that offer similar strategies that I have been discussing on my swingtrader.com, relativevalue.com and perfectstormtradingstrategy.com websites for the past ten years or more.

One of the offers was a service, using only four ETF’s, that the provider stated would constantly beat the market.

Over the next few weeks and months, I am going to have on the swingtrader.com website an example, updated weekly or daily if necessary, of my 4 ETF strategy. If followed, the strategy should emulate the best of the services being offered. It is certainly not a recommendation of what to buy or sell, but an example of what can be accomplished by using a relative strength with momentum strategy. It is for illustrative purposes ONLY!

The four ETF’s chosen are the result of my own research. They should portray a representation of the changes in market sector rotation. The four ETF’s have a positive and negative correlation with each other. The ETF’s are displayed here on daily charts.

I will update the daily charts when appropriate.

Remember, Green=XLE, Energy. Red=XLY, Consumer Discretionary. Light Blue=XLU, Utilities. Yellow=XTN, Transportation.

Latest update July 11, 2019, 8:00 AM

 

 

Dividend Aristocrat strategy

Many traders only look for high probability trades without making sure that there is also a high expectancy outcome.

A great example is so-called Russian roulette. Load a six capacity revolver with five bullets leaving one chamber empty. Spin the revolver mechanism and put the gun to your head. Pull the trigger. The player has an 83% chance of not killing him or herself. High probability, 83% versus 13%, but the 13% is a total loss. Not a few ticks or pennies, but a total loss with no possibility of recovery, ever!

Expectancy knows that regardless of the probability, there is a high level of payout that outweighs the losses.

The successful trader realizes that a system of small probability can be very successful if the average trade has a very high payout for wins versus little loss if the trade doesn’t work out. The best strategy would have a high probability AND a high expectancy.

For example, if one flips a coin a few hundred times and receives $300 each time the coin shows ‘heads’ and loses $100 each time the coin shows ‘tails’, the normal distribution of approximately 50% would earn the coin flipper a high expected return. The coin flipper would have high expected return with anything better than a 25% heads versus tails distribution.

An example of a high probability, high expectancy swing trader strategy is derived from an article in Seeking Alpha, December 23, 2016, “The 10 Best Dividend Aristocrats for 2017 And Beyond”. The piece refers to 10 stocks from a wide range of industries which have increased their dividends for at least 25 consecutive years. “Market Watch” reported on September 9, 2016, that Dividend Aristocrats stocks almost doubled the returns of the S&P stocks in 2016. Many other studies of dividend aristocrats show similar results over much longer time periods.

Below are the 10 Dividend Aristocrats mentioned in the Seeking Alpha article. Once again, the relative momentum is color coded to represent the issues that are also color coded.

It is expected that performance will be better if one were to chose only the issues that are exhibiting only positive(above the zero line, purple) momentum.

Higher probability with a higher expected outcome.

VFC=VFC Corp, ABT=Abbott Labs, JNJ=Johnson & Johnson, CAH=Cardinal Health, ABBV=AbbVie.

 

GWW=Grainger , MDT=Metronic, WMT=Walmart, BDX=Becton Dickinson, HRL=Hormel Foods.

 

Color coding on bottom chart refers to the color coding of the securities. Yellow=Yellow, etc.

Prices as of the close May 29, 2019

 

Kraft Heinz

On  (February 22, 2019) Wall Street Journal:

“Kraft Heinz Co. wrote down the value of its Kraft and Oscar Mayer brands by $15.4 billion,

disclosed an investigation by federal securities regulators and slashed its dividend, sending

it’s stock down Thursday more than 20% in after-hours trading.”

 

The following picture of weekly price history, updated at 8:30 today (March 7, 2019) illustrates that anyone

using basic PerfectStorm indicators would have stayed away since July of 2018.

How to find Swing Trading stocks.

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 bank stocks which were, at that time, traded only in the OTC market. It was an advantage for an institutional customer to deal 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 $225, a customer might 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 might 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 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” for the stocks we did not trade.

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, the date of inquiry, the name of the issue, whether the indication was a 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 dealt with the 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,” lead 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 a small spoke in the Weeden wheel and had no idea of what the most senior people discussed about investments. Weeden, being only a 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 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 conversation was early in 1969. He had told me 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 nondefault 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 rating 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 equity for a few days or a few weeks and to profit 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 oscillators that indicate that these 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 grow 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 on 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, during the week of  December 11, 2011, SHW was purchased at approximately 86.5 as indicated by the up arrows. This position was sold in the middle of August 2013 at approximately 167. During this time the financials of SHW indicated an expanding wide moat. The long position was re-established during the week beginning of April 27, 2013, at approximately 181. This position was sold during early July of 2015 at approximately 275. During this period, SHW was exhibiting sound financial reports. The long position was re-established the week of February 26, 2016, at approximately 271 and then sold during the week of September 2, 2016, at 285. Still moat. Solid financials. The long position was re-established the week of January 13, 2017, at 285 and was sold at approximately 410 on a weekly basis. The position was re-established on the long side the week of June 29, 2018, at approximately 401. The long position was exited during the week of October 12, 2018, at approximately 420. As of the close on November  26, 2018, the price of SHW was 411.54

 

  Close as of November 26, 2018.  SHW, on a daily chart.

 

 

 

 

 

Out of stocks and maybe into bonds?

Until recently, miners used to bring Canaries in the deep mines to warn them of the possibility of oxygen depletion

and carbon monoxide intrusion. Canaries, like all birds, are good early detectors of carbon monoxide because they are vulnerable to airborne poisons. They need immense amounts of oxygen to enable them to fly to heights that would make most of us altitude sick. They were small and compact and kept in small cages carried by the miners. Any sign of distress would be a warning to leave the mine.

The stock market, I believe has such a warning system. The Bond-Stock ratio.

Whenever the bond market appears to doing better than stocks, be prepared to take some preparatory action.

Such is the case, almost, right now.

Below is the daily and the 30 minute charts chart of the ETF’s: BND, and SPY.

Everything looks OK. Spy is still doing better. The red vertical line shows that SPY(red) is stronger.

But, the 30 minute chart shows another possible story. An early warning? Since June 14, 2018, BND seems to doing a little better on a 30 Minute basis.

BND vs SPY 30 MinBND vs SPY Daily

Finding Swing Trading Candidates

“The best-performing stock in the S&P 500 this year was the company behind Invisalign clear braces

  • Align Technology makes “clear aligners” that orthodontists use to straighten a patient’s teeth in lieu of metal braces and headgear.
  • Despite an onslaught of competition, Align Technology was the top-performing stock among S&P 500 companies in 2017.
  • Align Technology’s stock shot up from a $96.49 opening price on the first day of trading this year to an opening price of $223.22 on Wednesday. “(12/27/2017)
  • CNBC

ALGN performance in 2017 was most suprising because of wht it was not. It was not a tech or bitcoin stock. It is a medical device company.

In my previous post on How to Find Swing Trading Stocks (listed below), I mentioned that the basic first step is to find those stocks that have demonstrated, through their income statements and balance sheet, that the company has the potential to become a moat stock.

Once that this potential has been noted, the next step is to find the breakout stage, when the rest of the investing world notices that the company has developed some positive price momentum.

Align Technology started to show early signs of both after the annual report of the year ending December 2010.

The financial results continued to get better. The stock price followed.

The following monthly, and weekly pictures of ALGN should illustrate

the earnings and price growth of this unusual company.

ALGN WeeklyALGN monthly