Relative Strength with Momentum

Readers of the swingtrader.com blog will have noticed that the overall theme that I propose 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, but 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 an almost similar strategy that I have been discussing on my swingtrader.com, relativevalue.com or perfectstormtradingstrategy.com websites for the past ten years or more.

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

Among the offers are those by Market Geeks, Wyatt Investment Research, Portfolio Research Partners and Investors Business Daily’s Swing Trader. All have merit. I am sure that there are others.

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 a four ETF strategy, that if followed, 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 research. They 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 monthly, weekly and a daily charts.

I will update the daily and weekly charts when appropriate.

 

 

 

4SectorsWeekly4Sectors Daily4SectorsMonthly

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 was 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 are able to 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 under 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 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 initial 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 basic 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

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 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 $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 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 that 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 and equity 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.

SHW test

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.

 

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.

Where are we now?

The basic premise of swing trading is to on the right side of the trend.

After a few days of market gyrations, the market as measured by the S&P 500 and represented

by the EFT SPY looks like it is still in a continued upward bias.

The following weekly and daily chart illustrates this very nicely.

 

SPY-July 18,2014

Looking For New Direction June 29, 2014

DIA,KBE etc Weekly 6-29-14Not all securities follow or lead the Dow Jones or the Standard & Poor’s 500. Often times, differing groups of stocks go off on their own and neither lead or follow the equity averages. The following correlation study, using PairMatcher ,highlights this point. DIA is the Dow Jones 30 Industrials, KBE is the ETF that tracks the S&P Banks, USO is the oil fund, IAU tracks gold. and IYR is the Real Estate ETF.  Screen Shot 06-29-14 at 09.14 PM You will notice that although there is some correlation between the DIA and the others, the securities are not highly correlated. The period of the test was six months ending June 27, 2014. The following weekly chart of these securities illustrates how the uncorrelated price behavior can lead to profitable swing trading. The up arrows signify purchase triggers. Prices end on June 27, 2014   DIA,KBE etc Weekly 6-29-14 The following chart is the daily chart of the same securities. DIA,KBE,USO,IAU,IYR, Daily 6-27-14 Off course, all these securities can be structured into pairs and traded inter day. Please see www.relativevalue.com for more information on day trading.

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-

Pairs trading. VIX versus SPY

One of the groups that are active in LinkedIn is Automated Trading Strategies(Algorithmic Trading of Stocks,…). A current questions is about the VIX and the SP500.

The VIX, first introduced by the CBOE in 1993 using the S&P100 (OEX) was a weighted measure of the implied volatility of at the money put and call options on the S&P 100. Some years later it was changed and is now based upon the S&P500 index. The ETF of the S&P500 is SPY. The VIX index is often called a fear index by traders because in most cases when the market is calm and moving in a narrow trading range, volatility is low. As the market sells off, anxiety among traders increases and volatility increases. The VIX raises in periods of higher volatility.

The VIX tends to move in an opposite direction as the S&P500.
The following chart of the SPY versus the VIX for the daily period ending January 10, 2014 should illustrate that point.

Pair-SPY-VIX 1-10-2014

The top wriiten symbol in green is SPY, the bottom security with the symbol written in red is the VIX.
The chart clearly illustrates that as the SPY rises, the VIX declines. As SPY declines, VIX rise.

The vertical lines indicate turning points in the direction of the SPY and the VIX. Green lines tell the trader to purchase SPY, the symbol written in green and sell VIX, the symbol written in red. Red vertical lines indicate the decision to purchase VIX the red symbol and sell SPY the green symbol.

The latest trading decision was indicated on October 16, 2013. Buy SPY, sell VIX.
If a trader had purchased SPY the next day at the high of 173.32 and held it through the close of 184.14 on January 10, 2013,it would represent a profit of 6.24%. A sale of VIX at the low of the next trading day after October 16 at 52 and still holding the sale through the close of 40.84 on January 10, 2014 would represent a 27.3% profit.

Pairs Trade: Target versus Walmart

On Thursday, December 19, 2013, Target(TGT) confirmed that someone had hacked onto its systems and had stolen 40 million debit and credit cards from stores across the country. The breach lasted from Black Friday, November 29, 2013 to Sunday December 15, 2013.

Target(TGT) has generally been ‘paired’ with Walmart(WMT) in many pairs trading strategies.
The following chart shows one such strategy, the Swingtrader method using relative momentum and NOT mean reversion.

Pair-TGT-WMT Dec27-2013

On July 25, 2013 a signal was given and the next day WMT would have been purchased at its high price of 78.03 and TGT would have been sold at its low price of 70.55.

On December 27, 2013 the price at the close was 78.47 for WMT and 62.15 for TGT. A profit on both side of the pairs trade.

Returns would depend on what type of trader you are. It has been our thesis that generous returns are available using Swingtrader based pairs trading.