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Tharp's Thoughts Weekly Newsletter

March 12, 2008 — Issue #363
  
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ETF and Systems Workshops

Article

Does Efficiency Improve Stock Performance? by Van K. Tharp, Ph.D.

Workshops

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Trading Tip

Fed Tosses Credit Market a Life Preserver by D.R. Barton, Jr.

Special

How to Develop a Winning Trading System Home Study Program

Melita's Corner

Honesty versus Policy by Melita Hunt

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Feature

Tharp’s Thoughts

Does Efficiency Improve Stock Performance?

by Van K. Tharp, Ph.D.

For those of you are new to this series, I’m going to repeat a few things to get you up to date on the Tharp efficiency studies.  I’ve underlined the key points.  A number of times, I’ve looked at efficient stocks and found that buying such stocks long produces a very good trading system.  For example, from July 2006 through July 2007, during a quiet up market, 23 such trades produced a System Quality Number SM of 4.08.  That’s a better record than any of the newsletters I tracked in the Second Edition of Trade Your Way to Financial Freedom.  As a result, we started a series here in which I was looking at trading efficient stocks within the S&P 500.  The initial results looked quite good, but the results became poorer as we began to filter out various errors and assumptions that we were making.  The results were acceptable, but not as good as the two times that I’ve publicly traded such stocks (i.e., see Tharp’s Thoughts July 18th and in Market Mastery, issue #111) through my newsletters.

Our criteria for trading efficient stocks in our historical testing were as follows:

 

·     First, the efficiency was calculated by looking at the change in price over a set time period and dividing that value by the ATR over the same time period.  We used a composite efficiency consisting of four time periods from 180 days through 20 days.  

 

·     Second, we only looked at stocks with a composite efficiency over +8 in the S&P 500 on the first trading day of the month.

 

·    We then looked at two sorting algorithms based upon smoothness and only bought the top 10%, based upon those algorithms.

 

o    The first was the standard deviation of the daily change in the close (close minus close) over 180 days.  This gave us great results, but was biased toward low priced stocks. 

 

o    As a result, we also looked at the standard deviation of the daily close divided by close over the 180 days as a smoothing function.

But were these really the efficient stocks that I’ve gotten such good results from trading?  This is important to know because I’ve simply used efficiency as a screening tool and then selected the stocks I wanted to trade based upon a visual inspection of the charts.  As a result, in the last newsletter of this series, I asked three volunteers to look at nearly 200 stocks from both smoothing functions to tell me whether or not they were efficient.  One volunteer did 191 of the close minus close smoothing functions and concluded that only 49.2% were efficient.  Two volunteers worked on the close divided by close smoothing function and one concluded that 33% were efficient and the other concluded that 35% of them were efficient.  Those results basically told me that we were probably not looking at stocks that I wanted to trade and that I needed to go back to the drawing board with our algorithms.

However, I then started to look at the charts.  I’d told my volunteers to look at the price for the six month period prior to the entry to determine whether or not the stocks were efficient.  But when I started to look at what they were defining as “efficient" verses "not efficient,” it was clear to me that everyone had a slightly different interpretation.

I’ve always thought that my definition of efficiency was fairly subjective—the stock is going up in a fairly smooth line.  But this exercise told me that my criteria are a lot more detailed than I thought.

So let me give you some new ideas that typically describe what I thought was a fairly subjective decision.

 

·     First, the risk-reward ratio is important to me.  Thus, if I’m using a 25% trailing stop, I’d like something that I believe has the potential to go up 75 to 100%.  Thus, the first thing I’d like to do is eliminate those stocks that don’t have that potential.  I believe I have three criteria here.

 

o    I don’t want to trade something that is approaching a prior high that could provide a strong resistance point.  Thus, the stock might have been going up for six months in a straight line, but if it’s right at an old high from say six months or a year ago, I’m not interested in trading it.  

 

o    The stock doesn’t necessarily have to be at an all time high (or at an all time high with a slight retracement), but if it’s had much higher highs (e.g., in 1999 to 2000), then I want it to have the potential of at least a 3R move before it reaches those highs.

 

o    Next, I believe that something that is starting to become parabolic is probably near the end of its potential move.  Now that was not necessarily true in 1999 when stocks could easily move 5% each day and have the potential to go up another 100 to 200%, but the stocks that did that also tended to fall 25% or more in a single day when the move was over.  As a result, I tend to avoid stocks when the uptrend gets too steep. 

 

·     Second, although I’m looking for six month efficiencies (i.e., I’d like something that’s been going up for six months), I’m fine with something  that’s been going up for three of the last six months as long as it is higher than it was six months ago.

 

·     Third, the stocks I’d trade do not need to be at new six month highs.  In fact, I’d generally prefer something that’s done a slight retracement.  These stocks, in fact are often great buying candidates.  And, although I’m using 25% trailing stocks, simply because I want to hold them for a long time, others could do well trading these stocks simply by using the retracement amount (i.e., once the trend resumes) as the potential stop.  However, these could produce very short-term trades so I don’t trade them.

 

·     Fourth, the current up movement is usually defined by some sort of trendline.  I am not interested in buying anything in which that trendline has been broken.  Will it keep going down?  I have no idea, but I think the chances are much better when the trendline is broken, so I no longer call them efficient stocks.

 

·      Fifth, I will avoid stocks that are in clear uptrends but are too volatile.

These criteria pretty much determine what I look for visually in a stock when I do my efficiency trades.  As a result, I looked at the charts I’d been sent based upon these criteria.  First, I found that my agreement with the reviewers was about 80% with their judgments of efficiency, and I had not given them the above criteria.  For the close divided by close algorithm, I’d probably take about 53% of the trades the computer took.  But his is better than the 33-35% taken by the reviewers.

Second, for the close minus close algorithm, I’d probably take about 62.6% of the trades versus about 49.2% but I might have rejected more if I could have seen back to view old highs, etc.  However, for these charts we only had six months of data, so I couldn’t tell if the old highs would have caused me to reject a lot more trades.

Thus, my key observation from all of this work is that the algorithms done so far with Mechanica, while producing nice, but not great, profits, were flawed because they really were not trading what I’d select as efficient stocks.  But what I didn’t realize is that there was a hint of how good the efficiency concept was even in our data.  And that’s where a little study done by Pater Kolf became very useful.

The Pater Kolf Study

Pater asked himself, “If I screen these stocks using Van’s criteria (above), is there a difference in the profitability of the efficient versus the non-efficient stocks?”  The following table shows the results he found.  He only looked at 50 of nearly 200 stocks from each filtering process, so there could be a sampling error.  In addition, he found that more of the stocks were efficient than I did, but could be due to 1) using a sample of 50, or 2) subjective differences.

 

Filtering Algorithm

% Profitable

Total Profit/Loss

Close divided by Close 
  Efficient (66%) 55% Profitable $667,466
  Inefficient (34%) 47% Profitable $126,783
Close minus Close
  Efficient  (66%) 61% Profitable $13,652,572
  Inefficient (34%) 67% Profitable $8,172,168

Pater’s conclusion from the results was as follows:  “It appears that the reliability (whether or not a stock is profitable) is not much different whether a stock is efficient or not, but the total profits appear to be higher in the efficient stocks.”  Indeed, it is five times higher for the close divided by close database and nearly twice as high for the close minus close database.

My conclusion is that while sampling and subjective errors could have entered into Pater’s conclusions, the results definitively support my overall conclusion that efficient stocks are good trading vehicles.  However, I have not looked at the data and don’t know how well my judgment of efficiency would agree with his.  It was certainly much lower than 66% efficient with the large number of samples that I looked at.

Bob Spear, the developer of Mechanica, will be working on a new algorithm that better fits my criteria for an efficient stock.  When that is developed, we will be continuing this series.  For more information about Mechanica visit www.mechanicasoftware.com.

About Van Tharp: Trading coach, and author, Dr. Van K. Tharp is widely recognized for his best-selling book Trade Your Way to Financial Freedom and his outstanding Peak Performance Home Study program - a highly regarded classic that is suitable for all levels of traders and investors. You can learn more about Van Tharp at www.iitm.com.

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Trading Tip

Fed Tosses Credit Market a Life Preserver:  
What Are the Market Implications?

by  D. R. Barton, Jr.

Six and a half months ago, I wrote two articles in this space about the crisis in the credit markets and talked about the effect that conduits and other finance-based gymnastics could have on the equities markets.

E-mail was generally supportive and appreciative for the educational aspects, but I also got a few e-mails about how over-hyped the credit problems were.  That was around the first of September, before any of the big banks had taken hits,  when Citibank was trading at $45 (now $22).

Yesterday (Tuesday 3/11), the Fed announced a $200 Billion band aid to the credit markets.  The equity markets responded with vigor, with the S&P 500 up 3.7% for one day.  But it may be instructive for traders and investors to see exactly what type of band aid was offered and whether it will have any long-term curative effects for the credit crunch.

First, let’s look at what the Fed offered.  Before Tuesday, the Federal Reserve policies allowed the Fed to make collateralized loans to large institutions overnight so they could meet very near-term obligations.  The new initiative allows the Fed to lend up to $200 billion of U.S. Treasury securities to its group of 20 primary bond dealers – for up to 28 days.  In addition, the collateral requirements have been greatly relaxed as well. 

One Really Big Band Aid

This move wasn’t all that the big banks on Wall Street (and elsewhere) wanted; they would have preferred a full-fledged bailout with the Fed buying this crappy paper (mortgage obligations) outright.  But it was by no means a small move.

Big numbers— really big numbers— are tossed around a bunch these days.  And I think it’s easy for us to lose perspective of just how big of a cash infusion this is.  $200 billion is more than the nominal GDP of 147 of the 181 countries recognized as members of the International Monetary Fund.  That’s more than the 2006 GDP of Portugal, Hong Kong, Venezuela, the United Arab Emirates or Singapore.

And the significance of loaning U.S. government backed securities for, shall we say “less than top drawer” credit obligations should not be lost in the shuffle either.  My good friend and market guru extraordinaire Christopher Castroviejo likened the move to turning the Fed into a giant pawn shop:  they’ll loan out government securities to anyone who can produce a trinket with any value at all (a slight exaggeration, but not by much).

So what does this mean for traders and investors? 

First of all, it’s no surprise that we get governmental intervention.  Christopher has another saying that he’s fond of using as it relates the presidential election cycle.  He suggests that “no matter what brand of scoundrel is in the White House – whether Republican or Democrat,” that the powers that be will do whatever they can to bolster the markets during an election year.  This may be just an early salvo in that process.

The bottom line is that even with the credit markets still in trouble (despite yesterday’s band aid) and recessionary indicators rearing their ugly heads (like last Friday’s unemployment numbers), it’s tough to bet on a full-blown bear market in an election year.  My crystal ball is always a bit cloudy, but the forces of economic slowdown and government intervention should continue to provide a volatile tug-of-war into the fall election season that sees strong short to intermediate term moves in both directions.  Hold onto your hats – it’s likely to be a bumpy ride.

Until next week…

Great Trading!

D. R.

About D.R. Barton:  A passion for the systematic approach to the markets and lifelong love of teaching and learning have propelled D.R. Barton, Jr. to the top of the investment and trading arena.  He is a regularly featured guest on both Report on Business TV,  and WTOP News Radio in Washington, D.C., and has been a guest on Bloomberg Radio.  His articles have appeared on SmartMoney.com and Financial Advisor magazine. You may contact D.R. at  “drbarton” at “iitm.com”. 

 

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Melita's Inspirational Corner

Honesty versus Policy

by Melita Hunt

A couple of days ago I was backing out of a car parking space in an underground garage  and was not paying due attention to what I was doing because I was distracted by a sharp pain on the left side of my body where I had previously had surgery. I subsequently let go of the wheel and the car kept rolling when I should have braked and I ever so slowly hit the car that was parked behind me.

Needless to say, I parked the car again and got out to see if I had caused any damage. I was then prepared to deal with it from there. Of course, if there is no damage done then I could just drive away, but if there was damage to the other vehicle then there are two courses of action that I could take. One is to look to see if anyone saw it, and just drive away anyway (the sneaky way) OR do what I was supposed to do, which is leave my contact details on the car. Could you guess which one I did?

After looking at both cars, I did neither of those things. 

My car had a very small scratch on the bumper bar and a touch of the color pink from the rubber on the other car (the car itself was green). Yet the damage on the other car was overwhelming. And to top it off, I was imagining that the owner of this vehicle was probably affectionately known as Killer or Bear.  I was in a quandary. If I left my details, he (or she) might have thought that all of their Christmases had come at once and they could blame me for the tons of damage on their car. On the flipside, I wasn’t sure if I had actually done any damage or if the impact of the “scratch” on my vehicle could have done some of this.

So I headed off to the nearest police station to let them know what had happened and to get it on record. Then if there were any complaints, I could be responsible for any damage that I may have done.   

You may be wondering what this has to do with anything about trading or being an inspirational corner. Well I’m getting there.

As I waited for over an hour to spill my story to the police officer (who actually thought it was gallant but unnecessary after looking at my car), I got to thinking about why I was actually sitting there in the first place. And I realized that it was my conscience and my values. If I had just taken off and driven home, I know that this issue would have brought me a lot of undue stress because I regard myself as an honest person. I would have lost sleep and been worried that it would blow up at a later date.  The Law of Attraction: be careful of what you think about because you’ll create it!

If I had left my details on the car, I would have been waiting for Killer and his wife Big Bertha to turn up on my doorstep and show me the damage that I had miraculously inflicted. I would have lost sleep and worried about what they would say until I had actually spoken to them about the incident (and of course it could just as likely be a teenager or a pensioner that owned that car).   

The truth of the matter is that it was all imaginary problems and ultimately I just wanted to ensure my own peace of mind, which I did. The policeman took down my details and assured me that if anyone reported additional damage to their car that he would be sure to contact me.

Now this all may sound silly, but I like to look at why I do things and what the driving force is behind my actions; otherwise, we are just acting on automatic pilot. In this instance, I looked at whether I was being motivated by fear of being found out for hitting someone or being guided by my values. Honesty rates very high on my values list (which can actually get me in trouble sometimes), but I concluded that it was probably a little bit of both. Honesty and fear.  

This got me wondering as to how many of you actually know what your own values and driving forces are? Do you know why you do certain things? Do you ever take the time to analyze your actions? Or do you just justify them because you are so used to acting in a certain way?

We say that we know what our values are, but do we really? For example, do you ever say that family is the most important thing in your life and then find that you miss family events or work late or spend hours doing trading activities to the detriment of your family? If so, what value is overriding “family” or what driving force is making you do this? Could accomplishment, security or money be more important?

Another example could be health. Do you say that being fit and healthy is important to you, but you eat sweets, live stressfully and avoid exercising? Is it more important to work through lunch, than to stop and have a healthy meal and walk outdoors? What is the driving force behind this? Do you want approval from your boss or are you not valuing yourself very highly?

I have mentioned before that if you are in the money game, whether it’s investing or trading, then wouldn’t money or wealth need to be fairly high on your value chain? This often pushes a lot of people’s buttons because of their upbringing; it just doesn’t seem “right” to have money and wealth as values. Shame on me for suggesting it!

But it’s important to think about. If making money is not a value of yours, then how can you possibly be successful at it?

Now if honesty is higher than money on your values chain, then that could be regarded as a good thing because it means you won’t be dishonest to obtain money. From another perspective, if security is more important to you than wealth, then you may find that you are unable to take the risks you may need to take to get to where you want to go because you’d rather be safe and secure than wealthy.

See? Which value is the highest in your hierarchy? What values override other values? You would only know this if you'd taken the time to truly understand yourself and what makes you tick.

This is a huge topic that can be discussed in many different ways and if there is enough interest we can start working through some values exercises. Just let me know.

In the meantime, I feel safe at home with the knowledge that I hit a car, and although I didn’t follow standard policy, I was honest about it and can sleep well because at least Killer and Bertha do not know where I live... 

 

Melita Hunt is the CEO of the Van Tharp Institute. If you would like to keep up with Melita’s progress regarding her recently diagnosed lung cancer (she is a never-smoker). Please feel free to read her blog at www.myleftlung.com.

You can contact Melita at mel@iitm.com

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