Technically Speaking, May 2013

LETTER FROM THE EDITOR

As always, the MTA Annual Symposium was a success in every sense of the word.  The presenters are chosen for their ability to deliver valuable and actionable ideas. Networking opportunities provide a chance to meet with old colleagues and make new contacts. In the past few years, the location has provided out-of-town visitors with expansive views of New York City, allowing for limited sightseeing while maintaining a busy schedule.

In the next few months, videos of many of the presentations will be added to the MTA web site and summaries of the presentations will be presented here.

While the presentations are the core of the Symposium, equally valuable are the vendor exhibits. Many of us turn to the same tools every day. We know there have been new products introduced since we selected our software but we also know that is difficult to keep up with new products and even more difficult to assess the capabilities from a description on a web site. The Symposium always brings together a small group of product partners that understand they will be facing a demanding audience.  

If you were unable to make it to New York to see what’s new in the industry, you should still consider contacting the service providers that took time to learn how their product meets the needs of the MTA’s members. Several of the Symposium sponsors have agreed to extend special offers to all members for a limited time. More details are provided in this issue and contact information for all sponsors is also provided. Please give us any feedback on vendors and let us know what you would like to see in upcoming issues of Technically Speaking by emailing us at editor@mta.org.

Michael Carr

What's Inside...

MTA SYMPOSIUM SYNPOSIS

by
JC Parets, CMT

Editor’s note: This was originally posted at All Star Charts and is republished here with permission.

This week...

Read More

QUANTIFIABLE EDGES: ASSESSING MARKET ACTION WITH INDICATORS AND HISTORY

by
Rob Hanna

Rob Hanna was a presenter in one of the breakout sessions at the May MTA Symposium. Rob’s work is focused...

Read More

DYNAMIC MARKET PROFILE

by
Mathew Verdouw, CMT, CFTe

Editor’s note: Matthew is the creator of Market Analyst software and made a brief presentation at the Symposium. This article...

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USING RANDOM PATTERNS TO PREDICT THE S&P500

by
Manuel Amunategui

After reading Jack Schwager’s latest book, Hedge Fund Market Wizards, I was particularly inspired by Jaffray Woodriff’s interview entitled ‘The...

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HOW TO MAKE MONEY IN STOCKS – SUCCESS STORIES

by
Amy Smith
by
Ajay G. Jani, CMT

A search for the term “Investing” on Amazon.com yields over 139,000 results. Each title is presumably about a strategy to...

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INTERVIEW WITH MARK ARBETER, CMT

by
Mark Arbeter, CMT
by
Amber Hestla-Barnhart

How would you describe your job and what led you to look at the particular markets you specialize in?

My...

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MEMBER PROFILE: RYAN DETRICK, CMT

by
Amber Hestla-Barnhart

Ryan Detrick is the Senior Technical Strategist at Schaeffer’s Investment Research in Cincinnati, Ohio. He joined Schaeffer’s in Read More

CHART OF THE MONTH

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MTA SYMPOSIUM SYNPOSIS

Editor’s note: This was originally posted at All Star Charts and is republished here with permission.

This week [April 3-5, 2013] was the Market Technicians Association’s annual symposium. It’s easily my favorite event of the entire year. I’ve been going to this conference annually since 2006. Just when I thought that it can’t get any better, the Association creates the best one yet. If this is something that you haven’t attended in the past, I strongly suggest coming next year. Some of the top technicians on the planet come together in one place to share insights, strategies, and market wisdom. You can’t beat it.

It’s impossible for me to share everything from the week, but I will try and highlight some of the ideas and moments that stood out to me. Hopefully it will give you some idea of just how awesome the event was.

This year marks the 40th anniversary of the Market Technicians Association.  The events kicked off Wednesday night down in the Financial District with a black tie dinner. Many former presidents and founders of the MTA got up on stage to share a bit of the history of technical analysis on Wall St. If it wasn’t for the hard work they put in back in the 60’s and 70’s, our generation of technicians wouldn’t have the opportunities that we have today. For that we’re thankful.

Art Cashin, a legend on the floor of the NYSE, received an award of recognition and shared some very thoughtful and humble remarks.

The two-day Symposium began Thursday morning with a panel discussion that included MaryAnn Bartels from Bank of America/Merrill Lynch, Rick Bensignor from Wells Fargo, and George Davis from RBC. It was great to hear their thoughts on the market and the value that technical analysis brings to investing and risk management. MaryAnn Bartels said now that they’re hitting all-time highs, she believes we’re in a new secular bull market in Consumer Discretionaries, Consumer Staples, Tech, Healthcare and possibly Transports. I thought that was interesting, but in my opinion, the best part of the panel was the moderated discussion led by MTA President Dave Keller where each panelist shared the biggest investing mistakes they’ve made recently.

MaryAnn Bartels was visiting clients in Mexico City in the summer of 2011 during the European crisis. She admitted not recognizing the climactic bottom that was being put in at the time. MaryAnn didn’t turn bullish as fast as she would have wanted and wasn’t able to tell people to buy. Rick Bensignor had a stock back in early November that was really beaten down.  He thought a bottom was put in, came into work and said buy it at the open.  Right away, the stock opened and gapped up huge. Within 45 minutes, it 
was announced that the SEC was looking into that particular company and the stock closed down 14% that day. Then it dropped another 20%. This was a really important lesson in risk management. No matter how convinced you are of your analysis, risk management is the key to mitigating the unexpected. Finally, George Davis admitted to making a huge bearish call on
US Treasuries in early 2011. He saw evidence of market healing and yields started taking out big resistance levels. He figured it was time to make a big call on the bottom in yields. The Fed authorized another round of QE and yields ended up hitting new cyclical lows.

These are three of the best technical strategists in the business, and even they make mistakes sometimes. Risk management lessons are often the most painful ones to learn.

Dr. Jason Williams, son of legendary technician Larry Williams, presented in one of six interactive sessions focusing on the mental edge in trading. He recently published a book on how to adapt your personality traits to control emotions and make smarter investments.

Larry Williams brought together many successful traders, who each allowed Jason to perform custom personality tests to see if there was a correlation between personality traits and successful trading. As it turns out, the best traders consistently had low levels of anxiety. When asked what sort of anxieties they did feel, two answers were consistent throughout the test group (1) the fear of losing money and (2) the fear of incompetence, or lacking the intelligence to trade successfully. None of them had a goal of curing those anxieties. In fact, some of them even use their anxieties as a tool.

Williams cited a quote on fear from Jerry Rice, the greatest wide receiver of all time. At his NFL Hall of Fame induction ceremony, Rice said:

I’m here to tell you that the fear of failure is the engine that has driven me throughout my entire life. It flies in the faces of all these sports psychologists who say you have to let go of your fears to be successful and that negative thoughts will diminish performance.  But not wanting to disappoint my parents, and later my coaches, teammates and fans, is what pushes me to be successful.

Fear is what drove him to succeed.

Another trait that stood out from this successful group of traders was a lack of confidence. No cockiness at all from this group. I think this particular personality trait says a lot. I firmly believe that if you think you know everything, this market will crush you. It is important to know that the market will do whatever it wants and doesn’t care what you think. That knowledge separates those who make money from those who don’t.

Maria Bartiromo moderated a panel with Ralph Acampora, Alan Shaw, Frank Teixeira, and Craig Johnson on the 40 year evolution
of technical analysis. I think the consensus seemed to be that the world is smaller, people are 
smarter, and technical analysis is definitely much more widely accepted today than it’s ever been before.

Ralph Acampora thinks we’re in a secular bull market that can go for another ten years, but is definitely due for a correction. He is seeing some short-term cracks in the transports and small caps. What could change his mind? He said an 8-10% correction that doesn’t bounce back right away would cause him to reevaluate his opinion. If we correct, and then consolidate for 6 months or so down there that would worry him. But if that breaks, it could be trouble.  

Frank Teixeira took home the crown as the funniest technician of the week.  He thinks: “The trends in US and Japan are up, so you buy them… emerging markets not so much.” Alan Shaw, now retired, admits being happy he doesn’t need to navigate the current market.

Dr. Andrew Lo from MIT is always one of my favorite speakers. The man knows how to put on a presentation. I’m fortunate enough
to have heard him speak several times before and he always brings down the house. Author of A NonRandom Walk Down Wall Street, Lo explained that technical analysis, “empirically works”. He essentially invalidates what was once written about technical analysis in the famous book, A Random Walk Down Wall Street. It is nice to see technicians get their day with mathematical facts instead of just the opinion of some guy from New Jersey.

Dr. Lo went into what separates humans from machines. He described how one particular computer can process an incredible amount of data in nanoseconds. It is the fastest and most brilliant computer today. But even with its power, humans are still much better at recognizing and remembering patterns. He put up a picture of a squirrel. Right away we know what it is. The computer does not. He believes that the minute machines know how to do that and think for themselves, it will be the end for us. It’s a bit dramatic, but I get his point. You can teach a machine to do
a lot, but you still need a human brain to make subjective decisions. It was funny that Larry Williams would show the next day that market returns for quant funds last year dramatically underperformed everyone else.

Thursday ended with a bang. One of my favorite authors, Martin Pring, faced off with Uber-bear Bob Prechter in an inflation/deflation debate.  Martin Pring presented first, showing a chart of US Commodity prices going back to the 1840’s. The average secular bull run is about 19 years. We’re just 12 years into this one:

US government bond yields (on the long end) have been declining for 32 years, which historically is longer than usual. But there is a very clean downtrend line that has been touched many times during this decline. This is definitely the line to be watching:

The Commodities vs Bond ratio has historically been a good leading indicator for yields. This chart shows that since 1850, when the ratio bottoms out, US government bond yields tend to turn higher shortly thereafter. The secular shift in rates has been preceded by a bottom in the Commodities/Bonds ratio 6, 2, and 8 years in advance. Currently, we are 11 years into a new uptrend in Commodities vs Bonds, but still no shift in yields:

Pring offered a number of great historic charts. But Prechter had more charts than I’ve ever seen in my life. All of them bring him to the conclusion that we should not only sell all of our stocks, but also sell bonds and also sell commodities. Cash, according to Prechter, is the only place to go. “I’m as bearish as I’ve ever been”.

Suggesting more of a deflationary period is ahead for us, here is his chart of Commodities (CRB Index) not responding to all of this quantitative easing. In other words, no inflationary impact, but actually the opposite:

Prechter isn’t a fan of gold either. I thought this was a fascinating chart of gold bullion compared to the known ETF holdings for gold. He is suggesting that the public has been buying this pullback in prices which represents “bear market buying”:

Margin debt worries Prechter as well. Here are two charts showing that margin debt has grown by 100 times in 39 years. This is now $75 billion greater than the sum of cash and available credit in all accounts. The problem here is the timing. He says that hedge funds are leveraged and Stocks are at historic highs, but in theory, they can still get even more leveraged:

Sentiment is a big problem for Prechter. In addition to the leverage, he’s worried that the NAAIM Survey of Investment Managers shows that the average manager is now leveraged on the long side for the first time ever.  This is a new record exposure to stocks. Market Vane’s Bullish Consensus is now at extreme optimism, higher than it was at the Dow’s previous all-time high in 2007. Hubert Financial Digest’s Newsletter Sentiment shows the highest readings since March 2000 for bullish advisors, again higher than the 2007 peak. Hedge Fund net long exposure rose to a 10-year high in the fourth quarter of 2012. There are all-time record low levels of cash right now in mutual funds. Credit Suisse has a proprietary fear barometer showing record complacency. BofA Merrill Lynch’s Bull & Bear index is at record highs of extreme optimism. Insiders, on the other hand, are the only ones selling heavily. Vickers’ insider sell/buy ratio is now up to 9.2 to 1.

The Dow Jones Industrial Average is at record highs. But the Real Dow (inflation adjusted) has been making lower highs since 2000. Here, Prechter compares this action to the 1960-70’s period: 

Prechter’s presentation continued with more and more charts. Student loans are bubbling – now approaching a trillion dollars up from just over $200 billion in 2003. US Real GDP is making lower highs since the 1970’s and is now near the zero level. New housing starts have only “recovered” back to levels that used to mark a low in the 60’s, 70’s, 80’s and 90’s. We see the same thing with Total New One-Family Homes Sold, “recovering” back to the old lows taken out in the crash. Government Plus Personal Savings as a Percentage of GDP has not recovered from the ’08 crash, it’s just hanging out near lows below negative 4%. A lot of charts that tell the same story – everything is bad and don’t walk away, run away from stocks, bonds and commodities.

Whether you agree with him or not, his charts were awesome. Here is a good one showing how much the public loves the Fed near market tops, but hates them near bottoms. “The Bernank” is labeled “The Hero” in a recent cover of the Atlantic:

The last thing Prechter mentioned which I thought was Interesting was the lack of belief that a deflationary period is coming. I talk about extreme sentiment all the time where if everyone believes something (or doesn’t believe it), then the market is vulnerable to act in the complete opposite direction. The Apple and real estate bubbles were two recent examples of this. Prechter showed the Google search results for various statements and questions, and I think his point is made rather nicely:

“the world is coming to an end” 4,510,000 results
“dinosaurs love to dance” 1,710 results
“plus equals minus” 48,700 results
“inflation for 2013″ 47,700 results
“deflation for 2013″ 5 results
“inflation will rise in 2013″ 50,200 results
“inflation will fall in 2013″ 7 results

After Robert Prechter’s presentation it was time for a cocktail. The MTA Symposium is great: the charts, the presentations, the learning – all good.  But the best part of the whole week is the people. Through this blog, through the MTA and technical analysis, I’ve been able to meet some of the coolest and most brilliant people in the world.

Friday morning the symposium got started nicely with a bunch of the CNBC Fast Money guys telling war stories. Dan Nathan, Jon Najarian, Mike Murphy, and Anthony Scaramucci were grilled by technicians Katie Stockton and Craig Johnson. I have to say this was one of my favorite panels. I always appreciate when people admit their mistakes and are willing to share how they learned from them.

Scaramucci started with a great story. He was 26 years old working at Goldman Sachs and thought he knew it all, not just in the stock market, but specifically in the biotech sector (as an economics major). He put about $10,000 into call options as a biotech stock was going into phase two trials.  He admits that the worst thing that could have happened to him did, he was right and turned the ten grand into $70,000. It validated his thesis that he knew something, even as a 26-year old kid. So what did he do? When the stock was going into the phase three trials, he not only rolls the $70,000 into new call options, but also takes his $40,000 savings and goes all in. He flew down to Washington, D.C. to watch the FDA say no to the drug and lost every single penny. To make things worse, he somehow was on margin and actually owed Goldman $35,000 more to cover his debit balance.

Mike Murphy has received a lot of praise for his housing bottom call 18 months ago. He nailed the bottom in homebuilders. The problem was his execution. He admits to listening to people too much and hedging his positions so that they barely made any money. If he had just stuck with his convictions, he would have and should have made a killing. Great call but poor execution. Lesson learned.

Jon Najarian shared a quick story about selling options towards the end of the day to please a very big customer. He went home with the position on at the end of the day because he felt he had to in order to make the big institutional client happy. He lost 2 million dollars on that trade. Lesson – make a trade and go home with it because you want to, not because you feel you have to. Chances are the other guy has better info that you do. And in this case, he certainly did.

Dan Nathan shared a story about getting bearish on Research in Motion back in 2003. The stock doubled and crushed him. Meanwhile, he also owned Apple which doubled that year. He laughed as he added that he sold that one.

Mark Dibble, Stewart Taylor and David Lundgren presented together on a panel to represent technical research from the buy side at an event that historically has been sell side driven. This presentation was great and I notice this shift more and more every year. The MTA started out as a small group of sell side analysts. The membership grew to include traders shortly thereafter. Then futures guys got involved. Now, any professional technician is welcome to be a part of the Market Technicians Association. Everything is interrelated and we’re all just looking at price behavior. One panelist was asked about the job opportunities for technical analysts on the sell side. The answer from the sell side analysts was that not only is it tiny, but it’s actually getting smaller. Every year at this event, I’m meeting more and more people on the buy side. It’s an interesting shift to see.

One item discussed in the discussion was how some of the top money managers in the world are going public with their use of technical analysis.  In 2011 Steve Cohen was quoted as saying, “The slow grind up with stocks exploding higher is very bullish”. UK’s Anthony Bolton said, “To be too early on a stock can be costly. [Using charts] is a health check. It’s a bit like going to the doctor”. Professors at the University of Albany did a study titled, “Head and Shoulders above the Rest? The Performance of Institutional
Portfolio Managers who use Technical Analysis”. Larry Williams showed a chart showing the cumulative net return of institutional portfolio managers using technical analysis compared to funds that do not:

It was an awesome event. It always is. The MTA did a great job of putting it all together. These are just a few of the highlights that stood out to me during the week.  If you’ve never been, you really should consider coming to next year’s Symposium. I know that I look forward to it every year. The MTA doesn’t pay me to endorse anything, I don’t owe them anything, I just feel that this event is the real deal.

I’ll say it one last time, the best part of the whole Symposium is the people.

Contributor(s)

JC Parets, CMT

JC Parets, who holds a Chartered Market Technician (CMT) designation, is the founder of All Star Charts and is one of the most widely followed Technical Analysts in the world. All Star Charts is a research platform for both professional and retail...

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QUANTIFIABLE EDGES: ASSESSING MARKET ACTION WITH INDICATORS AND HISTORY

Rob Hanna was a presenter in one of the breakout sessions at the May MTA Symposium. Rob’s work is focused on quantitative swing trading, with most of his trades lasting 2-7 days.

In addition to providing an overview of his work, Rob also offered several specific and timely trading ideas. An example of his more recent work, from his blog Quantifiable Edges, is shown in the box below.

MONDAY, APRIL 29, 20 1 3

What Recent Moves Up Suggests About the Pullback That Began On Friday

Strong, persistent moves often do not roll over immediately. Real persistency can set up a situation where strength begets more strength. An example of that concept was triggered with Friday’s setup, where we had the 1st down day after 5 consecutive higher closes.

Initially there appears to be a moderate inclination for a bounce. Once you get out 9-10 days the upside edge appears very substantial. Based on this, there appears a good chance that the dip that started Friday may not get too far before the market again moves higher.

http://go.mta.org/3497

Rob has a large number of studies like this, accumulated over years of work and added to continuously. These are not trading systems which he defines as a complete set of rules defining entries and exits. A study simply considers what the market conditions are and quantifies how the market has performed under similar conditions in the past.

A review of the daily market action is used to develop a testable statement about market conditions. In this example, there were at least two conditions he noticed:

  1. The S&P 500 index (SPX) closed lower after five consecutive up days.
  2. The close is above the 200-day moving average.

Assume you enter the trade with a buy on the close of the down day. You would exit after holding the position for a number of days. Results for holding periods of 1 to 10 days are shown with various test statistics.

In an interview with The Kirk Report, Rob noted that “Curiosity and current market conditions are what drive many of the studies. Each night I examine the market to see what happened that day. How were the breadth numbers? What about volume? What was moving the most? How about volatility? I’m basically looking for standout readings in price, volume, breadth, volatility, sentiment, or some indicator I may track. It doesn’t have to be anything earth-shattering. Just something that might provide a hint.  The market hit an X-day high or low, or breadth was extremely weak, or volume was very high. Anything like that. Here’s a simple process for coming up with ideas to test:

  1. Observe what’s happening in the market.
  2. Describe what you’re seeing.
  3. Test what you just described.”
    http://go.mta.org/3498

In that interview, which can be downloaded here, Rob walked through his daily process:

Once you have some ideas it’s just a matter of taking historical data and filtering and sorting it in a way that you can see how the market has performed under similar circumstances in the past. To do this I most often use Tradestation. For certain tests I’ll also use Excel. The tool doesn’t matter much. If you want to run studies based on observations like I discussed earlier, here’s a simple way to do it:

  1. Set up each observation as a condition. In Tradestation this would mean something along the lines of condition1 = open > yesterday’s
    close or (Open > Close[1]). In Excel it would be a column. If the opening prices were in column C and the closing prices in column F then it would look something like this: IF(c5>f4,1,0). Remember, you want a new condition for each observation you are going to test.
  2. Set up your exit criteria. For most of my testing I normally look out X number of days to see how the market has performed. In Tradestation this would be a statement like: “If barssinceentry = X then sell this bar on close” Then you would optimize on X to create a table with a bunch of different days on it.
  3. Like we discussed earlier, start with a broad test and then get more specific. Make sure you look at things a number of different ways. When testing you need to approach it all with an open mind.  You’re not trying to prove that today’s action was bullish or bearish.  You’re trying to see if there is a convincing edge either way. When you get more specific you’re doing so in search of truth. You want to understand if certain observations have a substantial effect on the results. You’re not data mining in a effort to find the perfect setup.  If you’re looking at things a few different ways and consistently seeing the same answer then there is a pretty good chance there’s an edge.
  4. Let’s say you see an edge and looking at it a few different ways confirms it. Another thing that really needs to be done is you need 
    to look at the history of your results. If you are running the test over a long period, the edge may be significantly stronger or weaker now than it was in the 70’s or 80’s or 90’s. You need to take this into account when deciding whether your results suggest an edge. Let’s take a simple example. Mondays. From 1960 to until 1987 Mondays were consistently negative. After the Crash of ’87 (which happened on a Monday) this no longer held true. Since ’87 Mondays have not had a negative bias. Instead they’ve basically performed in line with the general direction of the market. I’ll provide a chart to illustrate this. So if a condition of your test is that you’re buying on Friday’s close and selling on Monday’s close, I probably wouldn’t run that test all the way back to 1960. I’d go from ’88 – present at the longest.
  5. Compare your perceived edge to a baseline. Consider the current market. If I ran a test and found that over the last 6 months, every
    time the market closed up on higher volume there was a 60% chance it would rise the next day and the average return was about 0.25%, would you say there is an upside edge? Consider the fact that the market is up 60% in the last 6 months. That’s 60% in 120 trading days. Now the 0.25% gain per day doesn’t look so impressive. If these same results had been achieved in March when the market was down close to 50% over the previous 6 months – well now you’re talking a HUGE edge. So make sure you put the results into some context by looking at what the market has done over your test period as well.”

He also added three simple market truths that have been confirmed with studies:

  1. You’re generally better off buying pullbacks in an uptrend than you are in a downtrend.
  2. You’re generally better off shorting rallies in a downtrend that you are in an uptrend.
  3. Don’t be too eager to short rallies that are coming off a potential long-term bottom (like a 200-day low). Those might steamroll you.

After years of market observations and testing, organizing all of the data becomes a problem. Rob addressed this by developing a program that scans the historical studies and identifies which ones have been triggered in the day’s market action.

With thousands of studies, conflicting signals are common. Rob created a tool he calls the “Quantifiable Edges Aggregator” to combine projections from the past few weeks into a short-term projection based on a combination of the active signals. Rob explained that “this result provides a net expectation for the next few trading days. I then look to see how the market has done over the last few days compared to recent net expectations. What I look for to find the most significant long-side edges are (1) positive expectations over the next few days and (2) a market that has underperformed expectations over the last few days (and is therefore considered oversold). For short-side edges I look for exactly the opposite. If I have positive expectations in an overbought market or negative expectations in an oversold market then that is typically considered a neutral configuration.”

Contributor(s)

Rob Hanna

Rob Hanna has been a full-time market professional since 2001 and has served as president of Hanna Capital Management, LLC since that time.  He first began publishing his market views and research in 2003.  From 2003 to 2007 his column “Rob Hanna’s...

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DYNAMIC MARKET PROFILE

Editor’s note: Matthew is the creator of Market Analyst software and made a brief presentation at the Symposium. This article expands on some of his ideas.

In 1998 I attended an ATAA (Australian Technical Analysts Association) meeting in Canberra and heard Ray Barros speak on Market Profile. He explained its construction and how the zones and the shape of the bell curve can be used to help a trader in making trading decisions. The only problem was I thought that the tradition of placing a Market Profile on yesterday and having it dictate my decisions today would be an issue if it was neglecting what was happening today. Many years had passed and I often thought back to that presentation and thought that there must be another way to get the same benefits and have those benefits adapt as the market trades.

In this paper I would like to take you through the process of what I went through and share with you the results of what I have found. If nothing else, I would like to encourage all Technical Analysts to think beyond what we have been taught. There is an exciting new realm of analysis waiting for those that are willing to try new things, but I digress.

Market Profile

Before we go too far, let me give a review of Market Profile for those who have not seen it before. Market Profile started as an intraday charting technique created by Peter Steidlmayer, a trader at CBOT. Steidlmayer was seeking to evaluate market value as it developed during the day. The graphical form of the Market Profile was introduced to the public in 1985 as part of a CBOT product.[1]  While Steidlmayer released a second edition in 1991, Market Profile in its original form has been a common tool on many software platforms.

So how is the Market Profile constructed? Let’s start with the “crass” explanation. Did you ever play that game Tetris in the 1980’s where you had to fit the blocks together? Imagine getting the bars on your bar chart and squashing them all to the left like you were playing Tetris. What you would be left with is a Market Profile!

Figure 1 Tetris

For those who have no idea of what Tetris is, let’s run through the formal explanation.

In the following image we replaced traditional bars or candles with colored boxes and gave each box in a bar a letter, starting with “A” and ending in “Z”. The vertical size of the box is very important. Typically this depends on the security that you are trading, so for a stock it may be 0.01, for an index like the Dow Jones it could be 10. For this example we have chosen it to be 1.

Figure 2 Data Bars represented with colored and lettered boxes

Imagine that we push all the boxes to the left. Because the individual bars are made up of boxes, the bars do not stay together, rather the boxes fill up all the available space.

Figure 3 Moving the boxes to the left

What we are left with is a Bell curve that is called the Market Profile. If you examine the images above and below, you will see where each box finished up. This profile lets us see at what prices there has been the most action, i.e. below you will see that 19 and 22 are the prices that have had the highest number of boxes at that price.

Figure 4 The result looks like a Gaussian Curve

Steidlmayer saw the parallels that the profile had with a statistical Gaussian Curve and called the price which had the highest number of boxes the “Point of Control” (always sounds better in a deep baritone voice).

Considering the total number of boxes in the profile, he would work out the first, second and third Standard Deviations from the point of control.  This is actually quite easy to do since it is well accepted in statistics that 68.2% of the sample will fall within the 1st Standard Deviation, and 95.4% within the 2nd, so all we have to do is count the boxes from the Point of Control until we have counted 68.2% of them.

Figure 5 Normal Distribution Curve with Standard Deviations shown

One of the other things that become immediately obvious when you apply Market Profiles to all sorts of market conditions is that it parallels with distribution curves in the concept of skew. In a distribution curve, results too far one way or another are considered to be skewed either positively or negatively. The same things happen in our Market Profiles.

In a Bull Market (see Figure 6 Bull Market Profile) the majority of the boxes are at the higher prices. This could be considered as an “Over-Bought” profile and taking new long positions during this shape can be quite risky.

Figure 6 Bull Market Profile

Figure 7 Sideways Market Profile, shows the profile in a sideways market.  This is the typical “balanced” bell curve that we are used to seeing. In trading terms it is a market where there is no clear direction. The entry and exit of the zones, as the market trades up and down, can be really good signals for range trading.

Figure 7 Sideways Market Profile

Figure 8 Bear Market Profile, shows a Bear Market profile. This profile highlights an interesting complication. The profile is a Bear Market Profile with the majority of the boxes being at the bottom, but if we look at the price action that makes up this profile, we see that the prices started low, and remained there for the majority of the time, however then the price moved higher towards the end of the period. Therefore we have divergence in what the profile is telling us and what the price is telling us.  This highlights one of the issues that I am hoping to resolve.

Figure 8 Bear Market Profile

Defining Value

What I love about Market Profile is that the Point of Control is actually the price where all the market participants for the selected period deem to be fair value. Wow, that’s a mind bender. Let’s break it down. We create a profile over a specified number of bars, from that we calculate the Point of Control as explained earlier. The point of control is a fantastic way to measure fair value since in the time period we are looking at; it is the price that was hit most often.

So if the Point of Control is “Fair Value” then the further that the price moves away from fair value, the more likely it is that price will turn around and start heading back. This is one of the ways that Market Profile has been used in the past. If the price is entering the 3rd Standard Deviation at the top, then long positions may be held on to, but no new positions would be taken. In fact opportunities to reverse and go short would be sought. You can think of this as a mean reversion strategy where the Point of Control is our mean. Obviously this concept of fair value is ignoring other market factors and currency devaluation.

The actual zone lines can be used as trade signals, however I have always preferred to view the Market Profile in this form as a guide to market sentiment rather than a trading strategy.

Figure 9 Microsoft Daily Chart with long term profile, is a long term chart where I have added a profile from April 20th 2010 to January 27th 2011. You can see for the following year the price stayed within the range of the 1st Standard Deviation.

Figure 9 Microsoft Daily Chart with long term profile

Remember earlier that I said that in its traditional use the profile would be generated and then the zones used in the next day? Well that does not always work very well. Have a look at Figure 10 Intraday Microsoft with Daily Profiles, in this case we have added a profile to every day on an intraday chart.

Figure 10 Intraday Microsoft with Daily Profiles

Market Profile is a great tool and fantastic at giving us a way of reading the market sentiment, but we are relying on the last time period to dictate to us what fair value is in the current time period. There are times where it works well, but there are just as many times where it keeps us out of trades that we should have taken.

Solving this became something that I pondered for many years.

Making it Dynamic

The solution was to find a way to treat the Market Profile like we do other indicators like Bollinger Bands or Moving Averages, that is, every time we get a new bar we remove the first bar from the calculation. Another way to think of it is to be able to drag our Market Profile around the chart and recalculate the profile. Of course if we were to draw a new profile for each bar it would create a mess on our chart, but in the end all we really want to know about are the positions of the standard deviation zones. What if we just calculated the Standard Deviations for a set number of periods on each bar, joined the values and displayed them on the chart?

Figure 11 Dynamic Market Profile, shows the same 60 minute chart with this new method. In many ways it is like a Bollinger Band in that the centre “Point of Control” line is like the base Moving Average in a Bollinger Band, except that rather than being an average of closes, the point of control takes into consideration all of the prices of every bar. The Zones are also similar to the Band lines in the Bollinger Bands except that you must remember the distinction between two standard deviations, as used in Bollinger Bands, and the 2nd Standard Deviation used in the Market Profile.  They are not the same thing. Two Standard Deviations is two times the distance from the centre to the 1st Standard Deviation, so it is based on distance, whereas the 2nd Standard Deviation is based on the statistical volume.

Figure 11 Dynamic Market Profile

I can trade this in a similar manner as Bollinger Bands, considering price in relation to the Point of Control and the 3rd Standard Deviation zone. Look at the Bull Market in Figure 12 Dynamic Market Profile in Bull Market. At point 1, the Point of Control Line is still heading up even though Price has ducked below the line. We can consider the Point of Control line to be our master trend direction indicator. Having the price drop below this line while it is still pointing up is a great indication that this is a short term pull back and I can add to a long position. Contrast that to point 2 where the price has gone below the Point of Control and the Point of Control is now also heading down.

Also, similar to Bollinger Bands, the tightening of the Dynamic Market Profile is a great indication that a breakout is on its way.

Figure 12 Dynamic Market Profile in Bull Market

This is obviously a really easy trade in a nice trending market (aren’t most indicators?). The fact is that this does not work quite well enough in sideways markets. My testing has resulted in too many trades for not enough profit, regardless of which of the lines I used.

I have had much better success with applying an offset to the Dynamic Market Profile and then trading on the exits and entries of the zones. Figure 13 DMP with 10 Period offset, shows an intraday chart with a 10 period Dynamic Market Profile and a 10 period forward offset. The strategy then becomes that I only go long when price moves out of the top of the zones, and I exit my position when it re-enters the zones, doing the opposite for any shorts. You can clearly see the entries and exits that would have been indicated and even more importantly how, in the sideways portions of the market, no signals were given and we would have been kept out from trading.

Figure 13 DMP with 10 Period offset

Testing

So how does it stack up as a trading strategy? There is still more testing to be done on this but preliminary tests across the SP500 show that there are years where it does well, and then years where it does not. At the bottom of Figure 14 Backtesting Results, you can see the Profit Factor Analysis (PFA), the “fat tail” on the positive side shows that the strategy can let profits run.  The PFA also shows that the majority of trades are losing (in this case 57.6% of all trades result in a loss), fortunately the losses are small and some Money Management strategies could reduce the losses.

Figure 14 Backtesting Results

Conclusion

While the Dynamic Market Profile is a good tool on its own, applied with an offset makes it really easy to pick up trending moves. These are still early days for this technique and I am still working on refining it, but so far it looks promising. What I love about this is that we have been able to take a concept that is working really well like Market Profile and completely reengineer a new technique from it. Following on from what I stated at the start of this paper, Technical Analysis has a whole new realm just waiting to be discovered. Is it in 3D analysis? Statistical analysis? Inter-market analysis? If you are willing to explore and test then there are great discoveries waiting.

I am going to be presenting a series of lectures on this method in March 2013 where I will be able to go into a lot more depth and also run through trade simulations to highlight the effectiveness of this method.

SIDE BOX

Calculation

Let’s get in the details and look at how these are actually calculated. In software, the way this is done is as follows:

  1. Given the bars that we want to draw a profile on, we first need to determine the box size in price. Too small a number and the computer will be doing unnecessary processing, too big and we miss out on detail.
  2. We divide each bar by the box size and then at each price count the number of boxes we have.
  3. The price with the highest number of boxes (and the one closest to the centre if there are more than one) is designated the “Point of
    Control”.
  4. Then we count the total number of boxes in the whole profile and work out the number of boxes in the 1st and 2nd Standard Deviations. 
  5. Using the numbers above, we count off above and beneath the point of control until we have accounted for our deviations.

That’s it! The concept and the math behind it are very simple, but it actually is a great deal of processing for a computer to do this.

[1] http://en.wikipedia.org/wiki/Market_profile

Contributor(s)

Mathew Verdouw, CMT, CFTe

Mathew Verdouw, CEO and Founder of Optuma. As the founder and CEO of Market Analyst (1996) and Optuma (2019), Mathew has been working in the field of Technical Analysis for over 27 years. His inquisitive nature, engineering background, and passion for helping...

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USING RANDOM PATTERNS TO PREDICT THE S&P500

After reading Jack Schwager’s latest book, Hedge Fund Market Wizards, I was particularly inspired by Jaffray Woodriff’s interview entitled ‘The Third Way’. Without spoiling the read, Jaffray neither trades the trend nor the reversal, but an altogether different approach. He runs QIM, a successful systematic fund, where he uses pattern recognition based only on the daily High, Low, Open, and Close prices. From there he devises thousands of patterns to predict where the market will go over the next 24 hours.

“…I blindly search through the data. It’s nice that people want hypotheses that make sense. But I thought that was very limiting. I want to be able to search the rest of the stuff.”

(Quote from Jaffray Woodriff – Schwager, 150)

I have been pondering that theme for over six months now. The interview reveals few details on his approach, but the gist is intriguing enough to warrant an exploratory investigation. When you think about it, there is nothing wrong with breaking up data into seemingly random packages and checking if any of them comes up with predictive abilities.

Approach

In this article I will describe how I built a pattern-recognizing system and how I attempted to measure its effectiveness. This isn’t meant to be a trading system but an investigative process ignited by Jaffray’s ideas.

The first part of this two-step process is to build a library of patterns with the historical ability of predicting the S&P 500 index. The patterns are based on generalized data derived solely from the daily High, Low, Open, and Close for all stocks in the S&P 500 index from 2008 to the end of 2011. In a nutshell, the patterns are calculated by dividing a product’s current price with its previous trading day’s price. Then, the generalized patterns of High, Low, Open, Close data points are mixed into every possible combination to fulfill Jaffray’s idea of looking at everything not just what makes sense.  Finally, the combinations are stored and ranked in the library by their consistency at leading the index into a particular direction.

The second step is to measure the library’s top performing patterns and their predictive abilities on an out-of-sample segment made up of the same stocks but for 2012. Each matched pattern gets to vote on the direction it thinks the index will take during the subsequent trading day. The winning votes are recorded and compared against the index’s actual outcome.

In addition to counting successes and failures, simulated trades are placed on the Spyder (S&P 500 ETF) to see how a basic system would fare entering at the signal close, and exiting on the following trading day’s close for 2012.

Some Results

Before breaking down each step of this investigation, here are some of the results:

  • 22,000 patterns were collected between 2008 and 2011 that occurred at least 5 times and with a better than 50% forecasting ability.
  • 69% of the patterns successfully predicted whether the index would go up or down in 2012.
  • The longs predicted correctly 72% of the time and
  • The shorts 64% of the time.

Here are the cumulative up and down votes from the historical patterns matching the 2012 market (in red) and the Spyder ETF:

The same data but wrapped around the Spyder’s closing price:

Finding the Patterns

Python and TSQL were used to gather, scrub and test the data. This could have been done solely in Python by querying Yahoo finance directly and being efficient with in-memory data manipulation.

The first step is to gather the High, Low, Open, and Close prices for every stock of the S&P 500 along with the Spyder (S&P 500 ETF) from 2008 to 2012 (in and out-of-sample periods). Depending on the data provider this amounts to approximately 600,000 rows. Various sources offer this type data for free such as Yahoo and Kinetick and can easily be downloaded by various programming languages and/or financial software APIs.

Generalizing the data

If we attempt to compare the closing price of one stock with another, we aren’t going to find much commonality. The data needs to be generalized (i.e. simplified) so we can identify common behavior. I generalized the data by taking the percentage change between a stock’s current price and its previous one with a precision of up to one decimal number (multiplying it by 1000 instead of 100 and dropping everything after the decimal point):

TRUNCATE (((current_price_point / previous_price_point) – 1) * 1000)

So if MSFT closed today at 28.69 and yesterday at 28.1, it would generalize to 20. If GOOG closed today at 780 and yesterday at 764, it would also generalize to 20. Thus, both stocks behaved the same way, and could potentially make it into the pattern library. The multiplier can be adjusted depending on the desired type of pattern. Multiplying with a smaller number dilutes the patterns and the commonality happens further out either side of the distribution curve. Using a larger number, the commonality gathers closer to the center of the distribution.

The generalized data now represents the behavior of the High, Low, Open, Close from one trading day to the next. Another very important data point to add is the resulting behavior on the index for the following trading day: did the index go up or down after the pattern?

  • If the S&P 500 Index closed higher following the pattern then append 1
  • If the S&P 500 Index closed lower following the pattern then append -1
  • If the S&P 500 Index remained unchanged following the pattern then append 0

This is a crucial step as we only want to collect patterns that have mostly predicted a singular direction (i.e. whether the MSFT and GOOG patterns mentioned above both led the index in the same direction) and have done so over 50% of the time.

Combinations

Here we get to the heart of Jaffray Woodriff’s interview. He talks about looking at all data before worrying if such data makes sense. So instead of matching all 4 data points with each other, we create every possible combination (i.e. create a High value, a High and Low value, a High and Open value, etc) and then compare each with the current market for matches. As we are dealing with 4 points (High, Low, Close, Open) we end up with 15 combinations:

This fulfills Jaffray’s statement about looking at all data and avoiding any debate whether a pattern made up of the High and the Open, or even just the Low, has trading merit. Though not done here, we could take this a step further and anonymize the classifying labels, throw all of the data into a table, and run clustering indexes popular in machine learning to circumvent the whole bias issue altogether.

Group/Sum

The generalized historic numbers are injected into each combination. This process generates a list of over 7 million rows.

Here is a sample of the pattern library at this stage:

H|15|O|-4, 1
O|-10|C|8, -1
L|0|C|20, -1
C|35, 1
H|3|O|0|C|18,-1

Looking at the first sample ‘H|15|O|-4, 1’ of the ‘HO’ combination (a High with a Low), the H represents a High that generalized at 15, the O represents an Open that generalized at -4, and the pattern preceded a rise in the index (a positive number).

Thankfully we do not need to compare a market against millions of rows, we only do it with the good ones. We want to keep the patterns that have correctly predicted the direction of the S&P 500 consistently during the historical period and have done so multiple times with hopes that those behaviors will extend into the future. This is easily calculated in TSQL (Transact SQL – Microsoft’s database programming language) by grouping equal patterns, counting them and summing their effects on the index the following day.

Unfortunately here we have to choose a threshold to separate what is a random occurrence from a winning signal. Jaffray may consider this a biased decision, but I limited the patterns to those with at least 5 occurrences and a larger than 50% historical prediction rate. Applying that threshold, the library drops to a manageable 22,000 successful patterns for the 2008 to 2011 period. Here are some of the top predictors:

Testing the Pattern Library against the Out-Of-Sample Segment

Most of what was done to the historical portion of the data needs to be done to the out-of-sample segment. Here we need to generalize the test data, create the combinations and combine both into patterns. We do not group or sum the testing data as we want to compare every single pattern against the library for every trading day during the trading segment. The total out-of-sample data once generalized and duplicated into combinations for 2012 amounts to around 1.7 million data points.

The test is performed by running through each trading day for 2012 and counting long and short votes from matching patterns between the 2012 segment and the library. The majority of votes in a particular direction also trigger the simulated trades. For the 2012 period, around 450 trades were triggered, about 2 trades a day.

Above is the graph showing returns generated by entering $100 positions in the Spyder in the direction of the majority vote at the close of the signal bar and exiting the following trading day’s close.

Even though the simulated portfolio ended positive it isn’t a realistic trading system. Besides not taking into consideration any actual or corollary trading costs, it isn’t possible to calculate a result at the close and still take a  subsequent trading action at that same close (it borders on ‘peeking’ into the future). On top of it, the drawdowns are brutal, the risk management is nonexistent, and I certainly don’t guarantee the numbers as this isn’t the focus of this article.

The important point here is that a system based on combinations and ensemble theory (http://go.mta.org/3502), where everybody gets to vote regardless of whom they are and where they come from opens up interesting, and often ignored, possibilities.

Other Combinations

We don’t have to limit ourselves to the daily High, Low, Open and Close.  Experimenting with intraday data, volume, acceleration, slope, derivatives, gaps, and everything else one can imagine are all perfect combination candidates. Also we could compare multiple days’ data with each other. The caveat with combinations of large sets is that the data will increase exponentially in size and quickly reach into the billions of data points.

Trading

Though not the emphasis of the article, this approach conjures up interesting follow-up ideas; instead of entering with market on close orders (MOCs), using limit orders with pivot points or bands and varying the order size according to the quantity of votes may make for additional interesting results.

Conclusion

All data-driven exploratory research can be tedious, but with the right tools we can quickly get to the fun parts of the process. Python and its various libraries can take a lot of the labor out of our hands and arm us with powerful and easy to use statistical tools.

The High, Low, Open and Close data points have been normalized, generalized, analyzed and traded to death. This type of approach is almost wasted on this data. As Jaffray hints, this approach is ripe for general scientific exploration. Meteorology, a cure for cancer, personal performance, financial management, and so many other areas could benefit from fresh and unbiased analysis.

We can finally experiment with any type of data with a straight face and only involve the PhDs when more understanding or an in-depth confirmation is desired. Even NASA called on Kaggle.com (organizer of public competitions to solve data science problems) to find fresh and unbiased perspectives to some of its toughest problems that its own army of PhDs couldn’t solve. In the spirit of Jaffray’s ideas, who is more fresh and unbiased than somebody who doesn’t understand the underlying data? This is even more appropriate with the looming avalanche of big data where automating this type of approach may eventually become the only way to make any sense of it all.

A special thanks to Jaffray Woodriff for the inspiration and Professor Tucker Balch at Georgia Tech for introducing me to some great libraries (QuantSoftware ToolKit – http://wiki.quantsoftware.org). In the spirit of the open source initiative, I will post the code on GitHub – connect with me on LinkedIn and I’ll send it out as soon as it is ready.

Reference: Jack D. Schwager. Hedge Fund Market Wizards: How Winning Traders Win. Wiley, 2012.

Contributor(s)

Manuel Amunategui

Manuel Amunategui has worked on Wall Street in the options industry for over six years. He now lives in Portland, Oregon and spends most of his time in the data and data-mining industry but still finds time to experiment and run trading...

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HOW TO MAKE MONEY IN STOCKS – SUCCESS STORIES

A search for the term “Investing” on Amazon.com yields over 139,000 results. Each title is presumably about a strategy to beat the
market, or at least profit from it. The types of styles advocated vary from high frequency trading to long term trend following to deep value investing and everything in between.  Some books focus on fundamental analysis while others rely on technical patterns. Most books infer that the author was able to use the prescribed strategies to successfully navigate turbulent markets and generate attractive returns.  However, very few books describe in detail the author’s level of success as an investor; the reader is left to assume that success has in fact been achieved. Sifting through 139,000 titles to separate the wheat from the chaff can be a challenging exercise for those looking to find helpful advice on investment strategy and tactics.

How to Make Money in Stocks – Success Stories by Amy Smith, is different.  Most investors are familiar with the work of William J. O’Neil. His journey of using trading profits to become the youngest person (at the time) to purchase a seat on the New York Stock Exchange in the early 1960’s has been chronicled not only in his books but by other authors as well. Smith’s book can be viewed as the follow-up O’Neil’s seminal work. In her book, she interviews investors of all experience levels who have used the strategies espoused by William O’Neil to create their own success stories, generating in some cases millions of dollars in profits for themselves and their clients. The stories are varied but the results are similar. Through the disciplined application of William O’Neil’s investment strategy, dubbed “CAN SLIM”, these investors have generated attractive returns, and in many cases, financial independence.

One of the most poignant stories is about Barbara James, an executive assistant who lost her husband to a heart attack a few years after she began investing. Within 18 months of that tragedy, she was laid off from her job.  With no husband and no income, she turned to CAN SLIM to manage her assets, and was able to generate sufficient earnings to pay off her mortgage and car loans. Several other investors discussed in the book were able to use their investment profits to buy homes for their families. Smith also discusses the experiences of David Ryan, Jim Roppel, Kier McDonough and Edward Hornstein. These investors were able to generate substantial returns and turn their avocation into businesses managing capital for other investors. Charles Harris and Mike Webster, in-house portfolio managers for William O’Neil, are also featured in the book.

Each chapter details the experiences of investors that have succeeded in applying the CAN SLIM system. At the book progresses, the skill level of the participants increases from private investors to professional fund managers investing capital on behalf of their clients. At the end of each chapter is an incredibly useful and powerful summary of the specific tactics or lessons that were important to these investors. The book culminates with a discussion about the grand-master himself: William J O’Neil.

As with all else in life, “the proof is in the pudding”. Smith’s book highlights real-time, real-life examples that following William O’Neil’s strategy has indeed lead to investment success for others, making it very unique in the body of investment literature. William O’Neil is fond of saying: “You can do it too!” and this could just as easily be the title for Smith’s book. It is certainly the message.

Contributor(s)

Amy Smith

Bio Coming Soon

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Ajay G. Jani, CMT

Ajay G. Jani, who holds a Chartered Market Technician (CMT) designation, is the Managing Partner and CIO of Single A Capital. He has been in the investment business as a technical analyst since 1989, and was previously the Managing Director of Gramercy, an...

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INTERVIEW WITH MARK ARBETER, CMT

How would you describe your job and what led you to look at the particular markets you specialize in?

My investment career is probably different than most. I quit college after one week (engineering) and started working for a very successful manufacturing company that was listed on the AMEX. I saw how successful they were and how well they were run so I started buying their stock at the ripe old age of 19. I did very well and decided to start taking business classes at night. After five years of working and investing, I made enough money to go back to college full-time and pay for it myself. I finished undergrad and grad school in four years. It helped living at home. Along the way, a broker gave me a copy of S&P’s Trendline chart book and I guess the rest was history. Charts made sense in evaluating an investment, accounting and finance did not. I guess I’m a visual person. In fact, I wrote a paper in grad school for an accounting class and titled it “An Accountant’s Job is to Distort, Not Report.” I don’t think the professor was happy but I passed the course. Interestingly, this was back in 1982, well before the accounting
scandals of the ‘90’s.

I started at Standard & Poor’s (now S&P Capital IQ) in August 1987, the week the market peaked. I also had just gotten married and was planning on buying a house later that year with the money I had invested in the market. I of course got slammed in the crash and buying a house and keeping my new wife didn’t seem like a high probability. Well, everything worked out, somehow.

While a newbie at S&P, a VP gave me a copy of William O’Neill’s book and to this day, I still think he has developed the magic formula for making big money in the market. The one key that stands out for me becoming a technician is that charts almost always lead the fundamentals. This can be seen at every major top and every major bottom. Also at intermediate-term tops and bottoms. One of my proudest moments came in the latter part of 2011 when it seemed the whole world was bearish and there were horrific headlines everywhere you looked. However, the charts, in my view, were turning very bullish and I started putting out targets well above most. Also, in 2012, I started talking about the potential that the secular bear was ending and that a new secular bull may be upon us. I was seeing major breakouts in many sectors as well as the major indices. In addition, there were a fair amount mega-caps that were breaking out of 10+ year bases to new all-time highs. I think over the past couple of years I finally was able to divorce myself from the headlines and trust the price action as well the many technical indicators I follow. Unfortunately, that learning process took way to long.

I have had a very unique job at S&P over the past ten to fifteen years. The Research Director at the time thought that my talents as a technician may help our fundamental analysts. I don’t think the combination of both disciplines was very popular when we started to embark on it, but it has worked out well. We have the STARS system at S&P, 1 being a strong sell and 5 being a strong buy. I have become both proactive and reactive with the fundamental analysts here. I probably help out the most when a stock is
going against the fundamental analysts’ STAR ranking. I provide a different viewpoint that is totally objective. I know, we are all supposed to be objective, but that is almost impossible at all times.

I write a weekly technical comment that appears on MarketScope Advisor and do a short mid-week technical write up. I cover the S&P 500, of course, as well as many of the other major indices. I also write about the bond market, commodities, and currencies. I am a member of S&P’s Investment Policy Committee, chaired by Sam Stovall. We set 12-month targets for the S&P 500 as well as set asset allocation. I also am part of the sector team, devising which 10 S&P sectors to overweight and underweight.

Which technical indicators do you rely on and how do you combine them?

First and foremost, liquidity rules. I don’t think that’s technical, but never ignore what the Federal Reserve is doing. Besides basic chart reading, I use many technical indicators that measure momentum, market internals, and market sentiment. I also use a “little” Elliott Wave. I use nothing fancy and have no complicated models. I think there are too many indicators canned in charting packages and while they are fun to test, there is no simple answer to what we do or magic indicator. Usually when I start leaning on one indicator because it has had a hot streak, all of the sudden it stops working. Funny how that works. Well, that’s the market. When you think you got it beat and you let your ego and emotions get out of control, Mr. Market takes care of that and smacks you in the head. I have learned that when I get on a hot streak and I start feeling really good about my calls, almost always I get knocked down. You just have to keep fighting. Predicting the future is a tough almost brutal profession at times. When your right, and especially when you’re sitting on the opposite side of most, you feel like the “King of the Street.” When wrong, you feel like changing professions. It’s a very challenging lifestyle full of second guessing, whipsaws, people with short memories regarding their calls, etc. But, it’s the only world I know.

In general, there are a couple things I may do different than some technicians and many fundamental analysts. I try my hardest to be proactive in my market calls, not reactive. One note – I am not talking about individual stocks here. I also will stick my neck out at times. Of course my head gets chopped off at times, but so be it. There are too many stock market analysts that just play it to close to the vest. Why do we need them? Just put 60% in stocks and 40% in bonds and walk away.

When analyzing/forecasting the S&P 500 for instance, my job is to get my customers in the market for the meat of a bull market and out for the meat of the bear market. Taking that down a level, I attempt to capture the majority of intermediate-term rallies and miss the majority of intermediate-term pullbacks or corrections. Sounds simple, rarely is. When we get a confluence of technical warnings I wave the yellow flag and tell customers to reduce equity exposure. These warnings relate to price momentum
divergences, market internal divergences, frothy sentiment readings, weakness in key sectors, weakness in leaders, or just a general weakness under the surface that many times is not showing up in the biggest indices.  These warnings generally come before the peak in prices, and generally I am early at tops. I prefer to sell strength than to be panicked out by weakness. I don’t wait for this moving average or that moving average to get taken out.  That means you’re selling weakness and we all know the market falls a lot faster than it rises. In addition, many of us our managing decent-sized 401K’s which don’t allow you to capture intraday prices. I find it a gross injustice to be reactionary in this business which so many are. When you are reactionary, you really aren’t predicting the future, just reacting to the present.

Why do you think sentiment indicators are important in current times?  Which are the top Sentiment indicators that you use?

Sentiment is a big part of my overall analysis and I use it for all the markets I cover. I must first give kudos to the kings of market sentiment, Jason Goepfert, at www.sentimentrader.com as well as Bernie Schaeffer and his staff, at www.schaeffersresearch.com. If you want something related to sentiment, they have it. Their sites are also very educational, even for us so called market veterans. I keep track and follow many sentiment indicators including put/call ratios (equity-only, OEX, Total CBOE, ISEE (call/put)), market sentiment polls (Investor’s Intelligence, AAII, Consensus, MarketVane, NAAIM), Commitment of Trader’s (COT) data and Rydex flows.  So, obviously, there are a lot of inputs that go into analyzing sentiment of a particular market.

Sentiment is, has been, and will always be an important part of the investment process for the simple reason that individuals are driven by emotion. There are great charts that show the “cycle of market emotions,” running from depression to euphoria and back again. It was relevant 100 years ago and its relevant today, no matter what market you are looking at.  The great traders, investors, and money managers, whether fundamental or technical, all have conquered the emotions game with discipline. As Sir John Templeton wrote, “Bull markets are born on pessimism, grown on skepticism, mature on optimism and die on euphoria. The time of maximum pessimism is the best time to buy, and the time of maximum optimism is the best time to sell.” Baron Rothschild, an 18th century British nobleman and member of the Rothschild banking family, is credited with saying that “The time to buy is when there’s blood in the streets.” He should know.  Rothschild made a fortune buying in the panic that followed the Battle of Waterloo against Napoleon. But that’s not the whole story. The original quote is believed to be “Buy when there’s blood in the streets, even if the blood is your own.”

The one tough thing about using sentiment and being a contrarian, which seems to suit my personality well for some reason, is that the majority can be right in their market calls for extended periods of time. This is where you have to decide what type of market you are in – secular bull or secular bear.  During long-term bull markets, the range of sentiment observations will be much higher than it is during a long-term bear market. Of course, there are times when you don’t know what type of market you are in. But in general, we are trying to capture the majority of advancing moves during a long term uptrend and would rather sit on the sidelines the rest of the time.

It’s truly amazing watching the swings in market sentiment. As we said, it seemed like the whole world was bearish in late-2011, with some sentiment indicators more fearful then they were at the bear market bottom in 2008 and 2009. This really caught our eye as sentiment generally flows with prices and the lows in 2011 were well above the lows in 2009. We think we are currently seeing the opposite right now, except for individual investors.  Many so-called experts have turned bullish this year with the current mantra elicited by many being that as long as the FED is in the game, the market won’t even see a decent pullback. Well the FED has been in the game since 2008/2009 and they weren’t reciting this over the past couple of years. So we currently think there is a need to reset the bullish sentiment and shake up the late-comers and band wagon jumpers before the next leg of the bull market begins.

How do you use intermarket analysis in your work?

The concept of intermarket analysis was pioneered by John Murphy in 1991 with his book Intermarket Technical Analysis. We certainly watch the interactions between many markets, but believe the concept has almost become too popular. What we mean by this is that if market “A” moves higher with market “B” for more than a month or two, they must be correlated. I see way too many analysts talking about correlation that just makes no sense. The other issue with intermarket analysis is that at times, markets can move together for long periods of time, then move opposite for long periods of time. Then there’s the FED meddling in the bond market year after year, messing up the relationship between stocks and bonds. I rely on intermarket relationships when they make sense, and this sometimes has to do with fundamentals and the economy. Obviously, a great part of the commodities boom last decade correlated nicely with a declining U.S. Dollar Index. Commodities were also aided by booming emerging markets so the three were all tied at the hip. During this period, copper become known as Dr. Copper and did a great job of preceding moves in the U.S. stock markets. However, that connection has broken, at least for now. Copper peaked February 2011 and currently in a nasty bear market while US stock markets have moved to all-time highs. If you would have stayed with this one relationship as a key input to your analysis, you would have been bearish on US stocks and dead wrong. So, overall, these relationships work until they don’t work, and if you don’t realize when markets are disconnecting, it can really hurt your forecasting ability.

Do you look at any fundamental or economic inputs to develop your opinions?

I follow the Conference Board’s Consumer Confidence Index, which is just another piece of sentiment data related to the individual. Generally, bull markets peak when confidence is high to very high, and bear markets bottom when confidence is low or very low. This is what trips up many individual investors, economists, and fundamental strategists. What’s interesting about this index as well as other individual investor polls right now is that despite the bull market since 2009, confidence remains very fragile. One reason may be that confidence fell to its lowest level in many decades during the financial crises, so it has jumped from below 30% to a current reading of near 60%. However, we will note that the 2007 bull market peak coincided with a confidence level of about 110% while the 2000 bull market peak saw confidence soar to 145%. This combined with the very bearish AAII data could be very bullish for stocks over the next couple of years if individuals become more confident with both the economy and the stock market. In other words, there would seem to be a lot of additional fuel for stocks still sitting on the sidelines.

Contributor(s)

Mark Arbeter, CMT

Mark Arbeter, CMT, is currently the President of Arbeter Investments LLC and runs a newsletter entitled “On The Mark” focusing on technical analysis of the U.S. markets. Mark recently ended a 26-year career with Standard & Poor’s Equity Research as their Chief...

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Amber Hestla-Barnhart

Bio coming soon.

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MEMBER PROFILE: RYAN DETRICK, CMT

Ryan Detrick is the Senior Technical Strategist at Schaeffer’s Investment Research in Cincinnati, Ohio. He joined Schaeffer’s in 2003 and is a frequent speaker and writer on stock market and economic issues and is widely sought after by financial media for his expertise and commentary. With a decade of financial industry experience in the investment and financial services area, strengths include short-term trading with an eye toward timely technical- and sentiment-based trading opportunities, and advanced option trading strategies. Ryan’s formal education includes a BA in finance from Xavier University and an MBA in finance from Miami University. His ongoing professional education included earning his CMT.

To learn more about how to build a successful career in the financial industry I asked Ryan for some more information about where he works, his job, and what he thinks about the current state of the market.

Schaeffer’s Investment Research was founded over 30 years ago by Bernie Schaeffer. The company now publishes one of the world’s largest option newsletters, providing research and real-time option plays to subscribers.

Ryan is the Senior Technical Strategist at the firm, which is located in Cincinnati. The main function of his job is to research all optionable stocks and provide actionable option plays for subscribers. He is part of team consisting of seven other traders and quants. Finding trades means slicing and dicing the market every way you can imagine, constantly doing research and trying to find a slight edge. The goal is not to be right all of the time, but with enough hard work to tilt the odds in favor of the trader and to be consistently profitable.

Ryan also works closely with the media, doing both live TV and print media.  He is often a common guest on CNBC, Fox Business, and Bloomberg Television and has been quoted in outlets such as The Wall Street Journal, BusinessWeek, and USA Today among other. Wire service reporters working for Reuters and the Associated Press will also seek out his opinion which broadens his reach.

There is no question that Ryan’s career has been successful in the long-term although thinking about the long-term was not a part of his initial market education.

He began investing while he was still college, in 1999. He found immediate success, doubling an $8,000 trading stake that his father had given him to start with. His strategy was simple – buy whatever networking company CNBC was touting that day. Being fully margined, Ryan was on a surefire path to riches until, within only months of his entry into the ranks of trading wizards, he lost his entire initial investment after the tech bubble burst.

This was where Ryan started thinking long-term. He learned from his mistakes and realized that leverage works both ways. As quickly as a stock  went up in 1999, it seemed to go down twice as fast in 2000. He also learned that it’s possible to make money when things go lower, a valuable lesson in any market environment.

After college, Ryan joined Schaeffer’s, a firm that specialize in using sentiment as a major driver to trading decisions. He is able to trade anything that is liquid and has options. Although his main focus is on larger U.S. companies, he also trades some of the larger commodity and bond funds with options.

He believes that everyone looks at fundamentals and technicals, but believes that his success is attributed to the fact that the major driving force behind his trading decisions is incorporating sentiment.

At Schaeffer’s he has learned to “buy low expectations, not low prices.” A focus on option sentiment allows analysts to get a feel for what the crowd might be thinking. If everyone thinks one way, that is a crowded trade and there very well could be opportunities to go the other way. That’s why he believes it is important to include technicals in the decision making process.

If a stock is going lower, for example, but everyone is trying to pick a bottom this could be an ideal time to fade the crowd. A great example of this, in his experience, was huge call buying on various financial names during the financial crisis. Remember, bottoms form at despair, not when every fundamental guy is telling you they are a good value. Catching a falling knife looks neat, but odds are you’ll get cut. Sure enough, a lot of bulls on TV told us that buying bank names was a good idea when they were down 50% because the values were compelling. The eventual bottom didn’t occur on most till they were down nearly 90%. The price action was weak and most technicians were right here, but the major edge was in knowing that the crowd was trying to pick a bottom the entire way down.

In addition to technicals, Ryan does pay some amount of attention to fundamentals and economic data. This data is secondary to what equity prices might do. Last year, he noted, the Greek stock market was up more than 35% in the face of a horrific economic recession because the expectations were too low going into 2012. The horrible news that was priced in would in reality prove to be just bad news.

As another example of how technicals are more important than economic data, Ryan points to the numerous stories over the past four years about how badly the U.S. consumer was doing. From an economic point of view, this is probably true, he concedes. Yet, from an investing point of view, he points out that retail stocks have actually led most of this rally. The SPDR S&P Retail ETF (XRT) has led for years, yet the wide chorus we’ve heard the majority of this time has been, “the U.S. consumer is tapped out.”

XRT is an example of why you should “buy low expectations, not low prices.” When you have low expectations, as we have established for consumers, it makes it much easier for those expectations to be exceeded.

Schaeffer’s has examined a number of ideas, including whether something as simple as GDP growth can predict equity prices. In that study, Ryan explained, they found that weaker GDP numbers are actually more bullish than strong numbers. Consumer confidence is another useful indicator.  Consumer confidence was over 145 in 2000 and is currently less than half of that. Their research found that equities do better when consumer confidence is low as opposed to high. The lesson is simple, as Ryan said,
“again, it all comes down to expectations being low. When everyone is bullish, be very wary.”

The next example Ryan shared provided a great deal of insight into how combines an analysis of sentiment and price action:

I’d rather use a magazine cover than fundamentals. Last spring, many technicians were noting weakening price action during the second half of April, and the fact that small caps were greatly under performing was viewed as a sign the market was about to crack. Yet, it was a Newsweek cover that came out in late April that was the final nail in that bull run. They had a cover that said “America is Winning” with a man tearing off this suit to reveal a dollar sign shaped like the Superman symbol. The media finally caught on that things were improving, and that was the exact time to get out before a big dip into early June.

As technicians, we all know the power of allowing price to be our guide. So much of the fundamental backdrop is already priced in.  The more you trade, the more you realize metrics like a discounted cash flow, or whatever else you learned in college, simply don’t work. Yet, looking at the overall sentiment backdrop to get a feel for what the crowd is doing — that does work. Near the June lows last summer, we noticed as much individual equity put buying after nearly a 10% SPX correction as we did at the depths of the financial crisis. Think about that for a second: after a 10% drop, we saw as
much fear, in one facet, as we saw after the worst drop since the Great Depression. That is what matters. Once the S&P formed a doji just beneath its 200-day moving average and everyone got all beared up and it formed a nice bear trap, there was plenty of negative sentiment out there to spark a big rally. Which is exactly what we saw.

I always ask everyone I meet about two things. First, I want to know what advice they have for someone like me who is just starting in the business today. Ryan’s answer is worth quoting in its entirety:

You aren’t as smart as you think you are. It is a very tough game and you are going against some of the very smartest people in the world. This, of course, is what leads so many of us to this profession.  The challenges are high, but so are the rewards. Just know that life as a trader is anything but easy, and you will fail repeatedly over time. You had better learn from your mistakes, because if you don’t you won’t have much capital left.

Now, being “smart” isn’t necessarily a recipe for success. There are so many very smart people who are horrible at trading. Sir Isaac Newton discovered gravity and is widely considered one of the smartest people to ever live. Nonetheless, he lost a huge amount of his fortune investing in the peak of the South Sea bubble. This led him to proclaim, “I can calculate the motion of heavenly bodies, but not the madness of people.”

This is a humbling game and you have to make it your life. The environment is always changing, and what worked yesterday or last year might not work anymore. You had better be able to adapt or you will fail. “Adapt or die” is a phrase I’ve seen used before and I really like that.

While thinking about this, I also want to know what the most interesting piece of work they’ve seen related to technical analysis. Ryan had an answer that built on his advice to stay informed and ready to adapt to the constantly evolving market environment:

I had the pleasure of going to the MTA Symposium 2013, so it is tough to pick a favorite. I saw and learned so many awesome things in just two days. On a side note, if you’ve never been to the annual Symposium, I’d highly advise you go. Words can’t describe how good this year was. You just have to go to see it for yourself.

Dr. Jason Williams might have been the most interesting one out of the whole event. He is a psychiatrist and the son of famous technician Larry Williams. Whereas everyone else looked at charts and how they use them, he instead focused on the mental aspect. If you don’t go into a trade with the right mindset, the odds of winning are already greatly reduced before you even start. We all have certain personality traits and we have to live with them. What he discussed was how we can learn to adapt our traits to make better investments. With the help of his father, he was able to test many of the greatest traders and see what their personality traits consisted of.

Many of the best traders had low levels of anxiety, but every single one was worried about a good trade going bad. The interesting thing is not one of them had ever done anything to cure their anxieties. In a lot of cases, though, their fears were a major driver in their success. He ended with a quote from Jerry Rice on the fear of failure.

“I’m here to tell you that the fear of failure is the engine that has driven me throughout my entire life. It flies in the faces of all these sports psychologists who say you have to let go of your fears to be successful and that negative thoughts will diminish performance.  But not wanting to disappoint my parents, and later my coaches, teammates and fans, is what pushed me to be successful.”

Ryan continued:

One of the other major themes from the MTA Symposium was the combination of quantified analysis and technicals. I see this as the next wave in our field. How many technicians know that a death cross on the SPX actually has bullish near-term results? Or that a VIX beneath 15 is actually more bullish for the SPX than a VIX above 15?  Or that the SPX being up six straight weeks has more bullish results going out than the average returns? Or that an RSI above 70 on a weekly chart for the SPX is actually a strong bullish signal? Or that May during the first year of a Presidential cycle is normally strong and negates the whole “Sell in May” adage? Using charts and patterns is one thing, but further incorporating quantified analysis is the next step in the evolution of price interpretation.

Lastly, social media is becoming a game changer. So many smart technicians are on Twitter sharing ideas and insights. You can learn more in such a short amount of time compared to the past. When we can see what the Godfather of TA, Ralph Acampora, is thinking on Twitter, that’s a great way to learn exponentially. The potential for technical analysis to gain even more acceptance in the investing community due to social media is very exciting. Be a part of it.

His last point shows how much has changed in the last 40 years. Ralph Acampora’s thoughts have been central to the MTA and technical analysis for over four decades. Access to those thoughts has changed over time. You no longer have to live in New York and spend time on Wall Street to learn what Ralph, or other talented analysts think. With access to so much information, we are likely to see a number of rapid advancements in the next 40 years.

Ryan’s story also points out that immediate access to information can lead to mistakes as he demonstrated with large gains and quick losses in his first steps towards a career in finance. His ability to learn from the internet bubble allowed him to succeed in the business he loves.

Contributor(s)

Amber Hestla-Barnhart

Bio coming soon.

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CHART OF THE MONTH

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