Historical Testing of
Part IV: Using Buy and
Hold as a Comparison
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
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.
and Holding the April 2005 S&P
500 (from 1980 or on the date when
they first came out as stocks).
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 20th 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.
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
|Table 1: Stocks Added Purchased by Year
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
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.
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
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
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.
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.
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
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.
Top Losing Stocks In Our Database
10/ 03 1980
10/ 03 1980
10/ 03 1980
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.
3: The Biggest
Gainers in the 2005 S&P 500
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.
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.
we’ll do in this series will include the following:
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.
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
We’ll also try both 1) the
180 day channel breakout and 2) the linear regression to pick
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.
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.
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
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
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.