Feature

Historical Testing of Efficient Markets

Part IV: Using Buy and Hold as a Comparison

by

Van K. Tharp, Ph.D.

In Part II we did historical testing of an efficiency signal on today�s S&P 500 data going back to the year 1980.    Basically we bought highly efficient stocks on the first of the month and held them with a 25% trailing stop.  Once the portfolio had 25 stocks (i.e., with 1% risk we were fully invested), it only bought more stocks when we were stopped out of a loser.   The net result was a compounded annual ROI of 37%.    It took 674 trades and rejected (i.e., we were fully invested) 701.  56.7% of our trades made money and the average win was 3.87 times bigger than the average loss.  We also spent 378 days in a winning trade versus 80 days in a losing trade.  And we spent nine million four hundred dollars in trading costs, which amounted to 1% going in and 1% going out, so you can�t say that low cost influenced our results. 

In Part III, we took a look at some of the bugs in our coding.   These included a change in the way the position sizing was calculated using the total cash variable in Mechanica.   This resulted in a decrease in our returns of about 5%.   The second change was to our smoothing function (which tended to favor low-priced stocks).   This made a significant impact on our results because 1) the days in the trade were increased and 2) totally different stocks were purchased. 

At this point, I�m not sure that our smoothing algorithm is giving us stocks that I would normally  buy using my discretionary methods of looking at charts.   As a result, there is still more research to do.   First, I need to look at the charts of some of the stocks bought to see how the algorithm is doing.   I haven�t had the time to do that yet.   As a result, in this article I�m going to look at the effect of buying and holding the stock in our two databases:

1)  today�s S&P 500 going back to 1980, and

2)  the actual S&P 500, including all of those dropped from this list, going back to 1990.

Buying and Holding the April 2005 S&P 500 (from 1980 or on the date when they first came out as stocks).

In our first study, we simply bought $200 worth of today�s S&P 500 on October 3, 1980 or whenever they came out as stocks.  Thus, we were still purchasing $100,000 worth of stock, but once we bought we didn�t sell unless 1) the stock stopped trading or 2) the database ended on April 22, 2005.   There were no exits except those two.    Thus, this is a real buy and hold situation.  However, we are basically buying the BEST American companies.   We were also buying them either on the start date in 1980 or when they first came out as stocks.  That�s right, our initial entry (e.g., for a stock like DELL or MSFT) was not when it became part of the S&P 500, but when it first came out as a stock.  In addition, we are buying and holding them through the longest bull market of the 20^th century and into the secular bear market starting in 2000.     Although we probably would have trouble finding the best stocks in the U.S. 25 years from now, its gives us some idea of the absolute best performance that we could expect from a buy-and-hold philosophy under ideal conditions.

Table 1 shows a listing of the years and the number of stocks purchased during that year.   Since the last year is 2003, we were actually not fully invested until 2003. 

Only 499 stocks are listed, and I�m not sure what the missing stock is or why, but notice that we only lost money in 26 stocks out of 499.   Also notice that we were only 49.9% invested in 1980, 76.56% invested in 1990, 89.58% by 1995, and 98% invested by the end of 2000.

Let�s look at the overall statistics of our little experiment.   We started with $100,000 and ended up with $ 3,025,960.  Our gain amounts to a compounded return of 14.89%.   We made money on 94.78% of our trades and the average gain was 71.35 times the average loss.   Sounds like an ideal system doesn�t it and perhaps a strong statement for buy and hold, but remember that we were buying stocks like MSFT and DELL when the were first issued simply because they later became part of the S&P 500.

However, there was also bad news because we had a maximum drawdown of 51.27%, which occurred on October 9, 2002.    And we were in a drawdown from March 27, 2000 until the end of the run on April 4, 2005.   I�m not sure whether we�d even be out of the drawdown in 2007.

Next I decided to call 1R the full investment amount of $200.  This allowed me to calculate R-multiples for the trades and also the expectancy.  Using this calculation, the mean R-multiple (expectancy) was 29.31R, the standard deviation was 99.96R, and the ratio between the two was 0.2936.

Our compounded ROI with the efficiency algorithm was 28.59% with the �close minus close� smoothing algorithm on the same database.   And it was 14.58% with the �close divided by close� smoothing algorithm.   And I�m not convinced that either of these came close to what I trade with a discretionary judgment of what is an efficient stock.    I didn�t get any volunteers to search out trades for me and I have not yet had time to look over the stock charts myself.

You might be interested to know what the best and worst stocks were in the database.   For example, what stocks in the 2005 S&P 500 database have actually lost money?   And what American stocks have been the best since their inception?   Both of those questions can be answered with this study.

Table 2 shows the big losers in our study.   Who would have thought that if you had bought one share of AT&T in 1980, that you�d lose money over the next 25 years?  Who could predict the government breakup of AT&T in 1983.  In addition, old AT&T holders got shares of all of the companies that AT&T broke into and that�s probably not accounted for in this database.   And there may be other such examples in the data that are not so obvious � again, more data problems. ²

Incidentally, the new AT&T is what used to be SBC Communications (or Southwestern Bell).   It�s not the same as the AT&T that was around in 1980, but it has reacquired much of the old AT&T.  And Lucent, which used to be Bell Labs, the research arm of AT&T, had more patents than any other company.   It was a powerhouse of invention.  But when it separated from AT&T and went out on its own (in 1996) it failed miserably after a nice start.

Table 3 shows the big winners in our study. The big winner, of course, is DELL computer.  If you had purchased $200 worth of Dell in June 1988 when it came out, today you would have an investment of $356,099.  That�s an R-multiple of 1780R.   That�s about five times bigger than the 9th largest winner Microsoft, which only became a 240R winner. 

You can tell what happened to the price of the various stocks by looking at the number of shares purchased when we initially bought the stocks.   For $200 we were only able to buy one share of ATT back in 1980.   But for the same $200 (split and divided adjusted, of course), we were able to buy 10,000 shares of DELL.

In Part V of this research, we�ll look at our accurate S&P 500 database and see what happened with buy and hold.  In this case, we�re only buying DELL when it becomes part of the S&P 500 and that should make a big difference.

Other research we�ll do in this series will include the following:

1.    Determining what happens when we allow ourselves to take as many as 250 trades (i.e., half the S&P 500 database) at any one time with the two smoothing functions.   With 1% risk and a 25% trailing stop we are limited to 25 trades.   With a 0.1% risk and a 25% trailing stop, we are will be limited to 250 trades.   We�ll simply increase our starting equity to $1M so that we�ll be investing the same amount ($4000) with each trade.

2.    Research to convince me that I�m really buying the stocks I�d normally buy when looking at a chart will follow that.  One way would be to look at charts of the 100 trades from both smoothing algorithms to determine how many of them look like �efficient� stocks.  This will allow us to determine if we are looking at efficient stocks or not.  If any of you would like to do that and save me some time, I�d appreciate it.  Please let us know and we�ll send you the data.   And if there are a number of you, we�ll simply split them up.

3.    We�ll also try both 1) the 180 day channel breakout and 2) the linear regression to pick our trades.

4.   And, lastly, when I feel I have some of the answers I�m looking for, we�ll  then move to the real S&P 500 database that we have.

Notice at this point I still have not yet 1) looked at the effect of any trend following algorithm and compared it with efficiency, 2) looked at the data on a S&P 500 database that added and subtracted stocks as the index did, or 3) made position sizing adjustments to see what�s really possible with this sort of trading.   All of that is still to come in subsequent articles and it looks like this series might continue for some time.

_____________________________________________

1 By the way, if you have some interest in Mechanica, which we are using in these tests, then visit the Mechanica web site -- http://www.mechanicasoftware.com.  Mechanica is the new Windows version of Trading Recipes.

²  We checked this with our more accurate database from Bloomberg.  It shows AT&T with share price was $3.3656 (adjusted for splits/dividends) at our start date in 1980.  The last trade was $20.35 in 2005, at which point it presumably merged with SBC and disappeared.   Again, this points out the huge problem you have with any database you might have � accuracy.    In this example, we have a winner (which goes from 3.3656 to 20.35) that turns into an 73% loss just because of a data problem.   And the only way I know that is because it turned out to be the biggest loser and we decided to check that out.   How many data problems are there?   And how can you trust any historical study when such data problems exits?

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

Lessons from �The Blow-Up Artist�

by D.R. Barton, Jr.

By all accounts, Victor Niederhoffer is a creative and brilliant thinker when it comes to the financial markets.  He was educated at the best schools, including Harvard and the University of Chicago.  And during that time, he was a world-class squash player.

There is no doubt that Niederhoffer attracts top trading and analytical talent to work by his side.  And despite his very public money management blow-up from 1997, he has attracted hundreds of millions of dollars of new money to manage.

Everything seemed to be going well � several years of 30%+ returns and a talented team around him.  Many stated that he had �learned his lesson� in discipline and risk management.

In a recent article in The New Yorker, Neiderhoffer offered the following explanation for his losses in 1997, that were largely due to a huge bet on the Thai market:  �You asked for reasons�I could name another ten. We had no stops. We picked the wrong country to invest in. We were too illiquid. We had too big a percentage of the market, and we didn�t have the ability to get out of our positions. We were too financially vulnerable to the brokers. I didn�t take account of the fact that I could be squeezed and that customers could withdraw their money. But mainly I didn�t have a proper foundation for my investment there. I had no knowledge of the country. I�d never even visited the country. All I had done was finance a trip by Bo Keeley to the brothels there.��

If I were reviewing this with Van�s principles in mind, I would say that I can hear at least a moderate lack of responsibility for what occurred.

And in the last few of months lightening struck the same tree again.

Niederhoffer�s funds suffered losses up to 75%, and at least two of the three funds were closed down.  Once again, there were position sizing and leverage issues and a probable lack of stop losses.  And the volatility expansions, which I have previously written about, were more than the overexposed fund could handle.

What lessons can we take from this unfortunate series of events?

Taking Responsibility:  It is difficult to nearly impossible to make meaningful changes in our trading behavior if we don�t take full responsibility for our actions.  When we see outside forces as the reasons for losses, then we never get an urgent sense to change our own actions.

Having a Definitive �Get Out� Point:  Markets will always move farther and faster than we can imagine.  Trying to respond in the heat of the moment is almost always a poor plan.  We can always rationalize that if we wait just a little longer, the market will turn in our direction�  Every trade needs to have a definitive point at which the market tells us we�re wrong. 

Position Size for Worst Case:  If we use position sizes that take into account �worst case� scenarios, then when those big events hit against us, we will be damaged, but not destroyed.  In your position sizing modeling, always make a run or two with losses that are twice as big as you�ve experienced in your trading or back testing.  If your position sizing strategy can survive a Monte Carlo simulation under that added stress, then you are setup to survive for the long haul.

It�s easy to throw stones when others make missteps.  Instead, let�s learn from others� experiences to make ourselves better traders and investors.

For a well-written account of Victor Niederhoffer�s latest trading escapades, see The New Yorker article here:  http://www.newyorker.com/reporting/2007/10/15/071015fa_fact_cassidy.

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 where he is one of the most widely read and followed traders and analysts in the world.

He is a regularly featured guest analyst on both Report on Business TV,  and WTOP News Radio in Washington, D. C., and has been a guest analyst on Bloomberg Radio.  His articles have appeared on SmartMoney.com and Financial Advisor magazine. You may contact D.R. at [email protected].

 

Workshop attendees are often invited to dinner at Dr. Tharp's Home

 Click to view photos from the November 9th dinner.

Melita's Inspirational Corner

Two Paths

by Melita Hunt

There are times in our lives when things change dramatically, and there is a natural progression to this. These are the changes that we are prepared for and expect. Examples include leaving school, entering the work force, moving in with someone, getting married, having a first child, etc�  We don�t exactly know what to expect or what the change is actually going to feel like for us personally. We know that it will change us as a person, but we at least have some indication of what to expect because there are a plethora of people that have come before us who seem to be readily available to share their wisdom and know how. Somehow, we are just prepared and ready to experience these things: we look forward to them.

I call it natural transitions. It�s the art of growing up and experiencing life in a progressive, predictable way. Just like a flower going through the process of growing. We, as humans, grow along this path. I guess we could call it the easy path.

Then there are the times in our lives when things change dramatically and there is no rhyme or reason to it. These are the changes that we think will never happen to us, and we certainly don�t expect them. Examples include a major illness, the death of a loved one, the loss of a job, a divorce, a financial smack down or some other catastrophic occurrence that �just shouldn�t happen to us.� These are the moments that we say we look back on and although they felt like our worst moments at the time of their occurrence, they are often regarded at a later time as blessings in disguise.

I call these sledgehammer moments. When we experience something whether we want to or not. These incidences usually occur out of the blue; they are completely unpredictable, and they tear our �flowerbeds� apart. I guess we could call this the hard path. And we, as humans, grow along this path too. Probably more so! 

However, we usually do it kicking and screaming and with complete resistance.

I have had plenty of sledgehammer moments in my life. I have gone through breakups, job changes, financial problems, many deaths and painful moments. Each time I thought that my world was crashing down, but I got through it. As most of you know, my current sledgehammer moment is a cancer diagnosis.

This time, I am doing things differently. I am not being dramatic; I am not acting as though my world is crashing down around me. I am not resisting cancer or tumors. I am embracing this so-called �hard path� and looking for the lessons that I am supposed to learn much earlier. I am noticing the blessings in disguise as they are happening and I am being open to a new world of opportunities. Seeing the things that this can lead me to, rather than focusing on how it could keep me down. I am not going to say that it is easy, nor do I particularly like my current circumstances; I don�t have to. I just need to accept them for what they are. Simple.

Life brings change, it�s inevitable. Sometimes the path is easy and sometimes the path is hard.

Take a quick look at your life, and if you feel that there are some things that are currently on the hard path, maybe you can look at them through different eyes. Are you resisting a divorce, heartbroken over an illness or death, or are you having financial woes? Whatever the problem may be, take a moment to just accept it and let it be as it is. Be willing to learn the lesson that is being taught and look for the �blessing� in the so-called problem. Why look back in five to ten years and learn from this transition by sledgehammer when you can do actually do it right now and reap the benefits?

I wish you happiness and peace on whichever path you find yourself on.

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. Please feel free to read her blog at www.myleftlung.com.

You can contact Melita at [email protected]

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