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 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, Red=XLY, Light Blue=XLU, Yellow=XTN

Latest update November 13, 2017 Close

4 Way Daily

 

 

 

 

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) momentum.

Higher probability with a higher expected outcome.

Dividends 6-10Dividends 1-5

 

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

Prices as of the close November 13, 2017

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 of 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 is still long. As of the close on November 13, 2017, the price of SHW was 389.71

SHW Weekly

 

 

 

AAPL Earnings

Remember when Robinson Crusoe, stranded on an island, comes across a footprint in the sand.

“I stood as one thunderstruck, or as if I had seen an apparition. … I came home not feeling, as we say, the ground I went on, but terrified to the last degree, looking behind me at every two or three steps …”

The price graph of Apple (AAPL) from Thursday, November 2, 2017, pre-earnings announcement, should indicate that some footprints are not to be afraid of, but taken advantage of. Clearly, the trading activity pointed to a very positive announcement, which was confirmed when earning were announced after the close on Thursday.

New AAPL

 

Putting It Together. Part two

Friday, October 27, 2017, The Wall Street Journal, on page B12, had an article “Celgene’s Plunge Sickens Biotech Sector.

“Celgene’s Corp biggest stock plunge in nearly 17 years propelled a popular biotechnology index to its seventh straight day of losses.

Shares declined 16% to close at 99.99 on Thursday after the company reported disappointing quarterly revenue and cut a series of long-term financial targets. Celgene’s drop was the steepest since November 2000 and carried the stock below $100 for the first time this year. The stock’s swoon weighed on the Nasdaq Biotechnology Index which fell 2.3%.”

The article compared the price action of Celgene to those of Biogen and Amgen, two other components of the Biotech index.

The following graph, not from the Wall Street Journal using some PerfectStorm indicators, should illustrate this point.

Biotech October 28

There were obvious warning signs before this decline occurred.

On October 5, Celgene stock closed below two of the Relative Strength indicators at a price of $140.01, and on October 18th the Biotech ETF IBB closed below its indicator at 335.97.

Any investor believing in my basic strategy of only being long when Relative Strength and Momentum are positive would have not been invested in Celgene after October 5th and certainly not after October 18th. ($137)

On Friday, October 27, 2017, CELG closed at $98.17, and IBB closed at 316.12. Q.E.D.

Equifax Breach

In todays Wall Street Journal there is an interesting article on multiple pages diagramming the security intrusion and the inability of one of the largest holders of private information in the United States to secure that information. Near the end of the article there is what I portray as a “very interesting” statement:

“Three Equifax officials, including the companies finance chief, sold a total of about $1.8 million in stock Aug. 1 and 2, according to securities filings. Equifax has said they didn’t know about the breach at the time of the stock sales.”

The attached graph of the price behavior of Equifax shows that someone knew something.

The Green verticals indicate a preliminary bullish indication while the green arrows indicate a purchase. Yellow line indicates a close of the current position. Red vertical indicates bullish sentiment , Red arrows says sell.

Price as of the close today, September 18, 2017

 

EFX

Putting It Together

For the past ten years or so I have been putting out a blog on my websites: swingtrader.com, relativevalue.com, and perfectstormtradingstrategy.com.

During that time I have proposed looking at the investing/trading world through a different lens, focusing on relative strength combined with absolute momentum.

Since I started my blogs, I noticed others promoting similar strategies.

A book was written a year ago highlighting some of my thoughts and a global advisory established counseling many of the worlds largest money managers, using many of the tools that I had developed.

Most studies of actively managed funds tell us that only four percent of money managers can outperform, on a risk adjusted basis, the Dow or the S&P 500 averages over a ten year period.

I believe that most, if not all of the poor performance is a result of two factors.

One) The inability of the manager to sell positions that are in decline because of the requirements that the manager has to be fully invested. That is, there is no viable alternative, so the manager stays invested, even in losing positions.

Two) The behavioral problem in admitting that you are wrong. The reason for the initial purchase is no longer valid. Not that you were wrong then, but you are wrong now. It has happened to all of us.

My strategy/system remedies both of these problems.

If you are an investor in equities, commodities, foreign exchange, long term, short term, or day trader, if interested in adding significant value to your investing/and or trading portfolio, please contact me at:

rfeit@msn.com

Pairs Trading Strategy as Proxy for Swing Trading

It should be no surprise to learn that the most successful quantitative hedge fund founders were, at the beginning of their careers, successful convertible arbitrageurs. I was fortunate to be one of the earliest inventors/discoverers of the basic convertible arbitrage strategy.

The basics of convertible arbitrage revolve around the concept of relative value. When the convertible is demonstrably more valuable than the underlying shares, than the convertible arbitrageur purchases the convertible security and shorts the underlying shares. Reversing the trade when the relative values return to normal.

In the early days of the convertible arbitrage strategy, the position carried a positive cash flow and the reversal of the position could take many months until a more favorable opportunity made the position less favorable.

The relative value strategy follows into other quantitative strategies.

Most pairs trading strategies use two securities in the same economic sector that have movements that are highly correlated and co -integrated. They track each other almost perfectly, the ratio of the price of the two such stocks should be almost the same. When their relative movements deviate from their expected behavior, the strategy dictates that the relatively cheaper one be purchased and the more expensive one be sold short. A reversion to the mean relationship. Traders waiting for various deviations, trying to put trades on at maximum deviations. Hundreds if not thousands of pairs traders follow the same highly correlated co-integrated pairs. It becomes a game of chicken. Each trader trying to get the trade on at the best possible time.

Similar to what happened to convertible arbitrage, the returns on the strategy go down as the number of players participating increase. It is a limited universe. The amount of funds devoted to mean reversion pairs trading decreases the amount of profit to be made.

I have developed a swing trading strategy that uses the relative value of the pairs components. Like convertible arbitrage, the strategy uses a large portfolio approach, putting on lots of different positions in differing economic sectors to diversify risk. Many investors may find it useful in a long only portfolio.

Swingtrading for farmers

Corn-Wheat 8-15-2014BTodays(Monday Aug 18, 2014) Wall Street Journal on page C1 has an article “U.S. Farmers Are Up to Ears in Corn”

To no ones surprise, the economic factors that led farmers to plant increasingly more acreage in corn has caused an oversupply of corn just when the demand is declining. This demand fall off is due to a decline in livestock herds and declining purchases from China.

In addition to the supply/demand problem, more farmers in certain parts of the country which have traditional planted wheat have moved to corn due to the changes in weather patterns over the last few years. In other parts of the country, the opposite is happening.

The choice to plant corn or wheat or a new combination of both is happening in farms all over North America.

Fortunately there are ways for farmers to hedge their crops. Traditionally that has been in the futures market.

In September 2011, Teucrium introduced an ETF designed to replicate the returns that mirror the movements in the spot prices of wheat. WEAT. It has developed other commodity ETF’s that follow corn,soybeans and others. The ETF for corn is CORN.  For more information on the construction and costs please go to the Teucrium website.

The following daily chart of CORN versus WEAT illustrates a Swingtrading approach to corn and wheat. Both commodities have been in a decline, but at various times, the better play was to follow the relative momentum. Prices as of the daily close, Friday, August 15,2014

Corn-Wheat 8-15-2014A

and a closer view:

Corn-Wheat 8-15-2014B

For further information on all the topics covered and how you can implement these and many others in your trading plan. Please contact me at rfeit@msn.com See www.relativevalue.com for day trading ideas.

Swing trading using pairs, Emerging versus Frontier Markets

A recent Wall Street Journal article, Saturday/Sunday February 1-2, 2014,tracked the relative performance of ETF’s representing emerging markets and even less developed economies referred to as ‘frontier markets’. The article points out that the frontier markets have seen a ‘steady trickle of investment from fund managers hoping to ride years of steady growth’.

I have used the ETF IEMG to represent emerging markets and the ETF FM to represent frontier markets.

Since the end of 2013, IEMG is down almost eight per-cent while FM has flat performance. The U.S. market as represented by SPY is down a little more than five per-cent.

As a portfolio manager who is looking to diversify into less developed emerging markets, a look at the relative strengths of IEMG versus FM would be of some value.

The following graph illustrates this point.

 

Pairs IEMG-FM 1-31-