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Tharp's Thoughts Weekly Newsletter

October 10, 2007 — Issue #342
  
Workshops Early Enrollment Discount Expires Next Week
Article

Historical Testing of Efficient Markets, Part II, by Van K. Tharp Ph.D.

Trading Tip

Bubbles, Bubbles Everywhere, by D.R. Barton, Jr.

New NEW Second Edition, How to Develop a Winning Trading System Home Study
Melita's Corner

Update on Melita

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with D.R. Barton and Christopher Castroviejo
Nov 10-11-12

 

Feature

Historical Testing of Efficient Markets

Part II

by Van K. Tharp, Ph.D.

Historical testing is the primary method that some people rely on to develop confidence in trading but it is replete with pitfalls. For example, I talked about the data pitfall in last month’s article. As a result, my goal in the next few articles is to take a trading methodology that I feel confident in and determine what might be involved in doing historical testing with it. I’ll probably devote at least three articles to that topic, but the exact number will depend upon how the research develops.

First, I feel confident that if you buy stocks that show efficient uptrends (i.e., they are basically fairly straight lines going up), with a 25% trailing stop and risking 1% then you will make nice profits under most market conditions. I illustrated this in the Market Mastery newsletter in 2001 and in the recent evaluation of our portfolio. However, a number of questions remain. 

1) Is it possible to automate this form of trading? In other words, how do you take something that’s fairly discretionary and turn it into something that’s objective? Right now, it’s my subjective judgment that determines whether or not something is a good efficient stock. (This question, by the way, is probably the most difficult question for most trading systems.)

2) If the method can be automated, what is the formula that will totally define an efficient stock for us?

3) How can we overcome some of the data problems that are present in historical stock data?

4) What market conditions are favorable for this method and what market conditions should be avoided?

5) And what can we expect from this method long term?

I don’t expect to be able to answer all of those questions in these studies, but if you can begin to understand some of the problems involved in backtesting, then I’ve met my objective.

STUDY: First an Attempt to Automate Efficiency

In this study I’ve been working with Bob Spear, the developer of Mechanica software, to develop an algorithm to buy efficient stocks automatically. We decided to work with the S&P 500 database, which presented our first problem because it basically represents today’s S&P 500 even though the database goes back to 1980. Wouldn’t you love to know what the S&P 500 of 25 years from now would consist of? What new stocks will be so strong that they will be considered one of America’s top 500 stocks? All you’d have to do is just buy and hold the stocks that are not on it now and you’d make a fortune. But that’s what we were dealing with. Furthermore, all of the stocks were split and dividend adjusted. That means that a stock like Microsoft, which came out in 1986 begins at a price of about 15 cents (which also isn’t very realistic). But this is taken care by virtue of the fact that we buy an outsized position (10s of thousands of shares) so the accounting arithmetic works.

Automatic Entry

The problem with automatic entry is that my efficiency algorithm, which is a composite of four efficiency algorithms, is just a preliminary screening tool. You could probably use a channel breakout or a list of stocks making new highs and do a similar screening. It doesn’t matter because I still have to look at the charts to pick the stocks I want to buy. Our first solution to that was to do some sort of smoothing function. Bob programmed Mechanica to rank the S&P 500 in terms of smoothness for each day of the 25 year database and store that ranking for later use. Our first algorithm was to rank the inverse of the standard deviation of the change in daily prices. We took the inverse because we assumed that the smallest standard deviation would be the smoothest.

Mechanica, then did the following: on the first trading day of each month, it calculated the composite efficiency of each of the stocks in S&P 500. The first thing it did was determine if the composite efficiency was greater than 8. If it was greater than 8, it then purchased the top 10% of them in terms of smoothness. That was it – our automated screening process. But did it work?

Exit

The exit in our model was very simple -- we used a 25% trailing stop. Thus, if the price dropped 25% from our entry, we were out. However, whenever the stock made a new closing high, the stop was adjusted, now being 25% away from that new high. It was always raised and never lowered. Thus, the stop became both a reason to abort a trade and our profit taking exit. I used the 25% trailing stop because it seems to be an excellent substitute for buy and hold.

Position Sizing

We began each position with a 1% risk based upon the Total Cash available. Since our portfolio started out with $100,000, each position would risk $1000 and be a total investment of $4000. When our total cash changed, our 1% risk would always reflect that change.

Commissions and Slippage

Some of our positions were huge. For example, if you were to risk $1000 with a 25% stop on a split adjusted MSFT starting out at say 16 cents, then our initial risk would be 4 cents. We’d be buying 25,000 shares. The cost of buying those shares, especially in the mid 1980s would be tremendous. However, we wouldn’t really be buying a split adjusted stock. Instead, we’d probably be buying a $25 stock. As a result, we elected to include a 1% cost per trade (1% in and 1% out) for commissions and slippage. 

Results

The results of this study made us look like geniuses. These results are summarized in Table 1. We are able to turn $100K into $265 million in 25 years.

Table 1: Summary Results
Initial Balance 100,000   $ Won 330,803,321
Net Win Loss 265,541,946   $ Lost 65,261,375
Ending Equity 265,641,946   Incentive + Fees 0
ROI 265542%   Other Credits 0
Compound Annual ROI 37.83%   Commission/slippage netted 0
Max Drawdown % 38.65%   Other Debits 0
Max Drawdown % Date 19871204      
Longest Drawdown in years 1.49   Long Wins 382
Longest Drawdown Start Date 20020410   Long Losses 292
Longest Drawdown End Date 20031006   Short Wins 0
MAR Ratio 0.98   Short Losses 0
Sharpe Ratio 1.93   Long $ Won 330,803,321
Return Retracement Ratio 5.5   Long $ Lost 65,261,375
Sterling Ratio 0.54   Short $ Won 0
Std. Dev. Daily % Returns 1.21%   Short $ Lost 0
Value at Risk (99% confidence) 3.33%   Largest Winning Trade 22,969,429
Average Expectation Value 33.27   Largest Losing Trade 2,209,232
Expectation 176.28%   Transactions netted at open 0
Kelly 0.45   Average Winning Trade 865,977
Sum of Up % / Sum of Down % 1.36   Average Losing Trade 223,498
Percent New Highs 16.74%   Max Consecutive Wins 16
      Max Consecutive Losses 13
Trades 674   Days Winning 3,465
Trades Rejected 701   Days Losing 2,729
Wins 382   Average Days in Winning Trade 378
Losses 292   Average Days in Losing Trade 80
Percent Wins 56.68%      
Avg $Win to Avg $Loss 3.87   Number of Margin Calls 0
      $ Largest Margin Call 0
Start Date 19801001      
End Date 20050422   Size Adjustments 0
Max Items Held 16,487,507   Size Adjusted Items 0
Total Items Traded 117,196,789      
Total Slippage + Commission 9,000,400   Process time (H:M:S) 0:28:15

Notice that we made a compounded annual return of 38.8%. How many people did that from 1980 through 2005? Our worst drawdown was 38.7%, which occurred in 1987. It made 674 trades and rejected 701 trades (because we were fully invested). 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, so you can’t say that low costs influenced our results.

The Sharpe ratio of this system is nearly two and the System Quality Number is close to Holy Grail range.

Figure 1 shows the distribution of yearly returns. Notice that we only have two years of negative returns, 2002 and the last year of trading. Two losing years out of 25 is an exceptional, especially with no screening for bear markets. And during nine of the 25 years we made over 50%.

Yearly Returns from Our Efficiency System.

So what is your reaction to these results? Do you need more information or do you want to jump on this and trade it? Remember, I’ve already told you that I believe the efficiency model is an excellent long term trading system.

Perhaps you’d like more information. After all, the maximum drawdown is nearly as big as our compounded annual return. Let’s look at the drawdowns produced by this system. 

These are shown below.

Drawdowns in our Efficiency System

Notice that most drawdowns did not go over 15%, but twice we exceeded 35% and we exceeded 25% quite a few times. Was that due to our risk? Well, the table below plots the risk against the equity curve.

Risk Versus the Equity Curve

Our maximum initial risk should only be 25%, but since one position could get very large and have a significant risk, you’ll notice that the total risk sometimes gets bigger than 40% (e.g., in 2002-3).  Nevertheless, risk is still about what we’d expect it to be in this system.

So would you trade it?  All you have to do is buy stocks that are going up in a straight line and put a 25% trailing stop on them, risking 1% of your portfolio with each.  You should be able to do that, but will you?  Why or why not?

Problems with the Study

Many of you might be salivating to trade this system.  Based upon these results I could probably sell it to others.  However, I see a number of problems with the study, which we’ll be addressing in future articles with additional studies.  Let me just list the problems here to see how many you might have caught.  (Oh, and if you see some I didn’t mention, please let me know. Email van@iitm.com).

1)      Why are the worst performing years 2003 and 2005?  We had a bear market in 1980 through 1982.  We had the crash in 1987.  We had a nasty market in 1990-1.  What happened with those and why didn’t we have drawdowns in those years?  Some of the problems below might explain that.

2)      We have the S&P 500 data problem.  We are basically trading a limited universe that includes the very best performing stocks during that time period.  And those stocks were pre-selected.  I believe that the efficiency method is good enough to find the S&P 500 that will exist 20 years from now, but that still doesn’t negate the selection problem with these data.  To solve it, we’ll be looking at a database of stocks that includes the real S&P 500, including those stocks which no longer exist.  Finding such a database is very difficult, but we have succeeded in creating it!.

3)      Perhaps any trend-following algorithm would produce similar results with this database.  We’ll soon test a 180 day breakout system to see how it compares.  At least with that system, I’ll know that the stock is doing what I expect it to be doing when we buy it, making a new 180 day high.

4)      Our smoothing algorithm is flawed.  Remember that we are basically ranking stocks according to the standard deviation of daily changes in price.  The smaller the standard deviation the greater the ranking, which is quite interesting because it tends to favor the low-priced split adjusted stocks in our database.  Thus, it is biasing us to buy the very stocks that will go up the most.

5)      Third, one of my Super Traders looked over a list of the trades made during the study.  He concluded that the stocks being purchased were anything but efficient.  Thus, our algorithm that we automated doesn’t seem to be doing what we wanted it to do.

6)      Fourth, we will have the most cash during the short bear markets of this 25 year period.  Thus we should be situated to accelerate out of each drawdown.  Now that’s good, since most of the period under consideration was a secular BULL market.  But today we’re in a secular bear market.  What if we have a 3-4 year down period?  We’d be buying stocks as the market continues to go down.  We could have a 25% loss on top of a 25% loss on top of a 25% loss.  That would be a disaster.  Can we filter out the bear market periods and make this perform better?

During the next few months I’ll be presenting one or two additional studies each month to address these issues.  But the important lesson for you was if you saw the flaws in our backtesting.  Or did the result make you just want to jump on the system and trade it?  These types of flaws occur all the time and that’s one of the things I’d like to point out.

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

About Van Tharp: Trading coach, and author Dr. Van K. Tharp, is widely recognized for his best-selling book Trade Your Way to Financial Fre-edom 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

Bubbles, Bubbles Everywhere

by D.R. Barton, Jr.

On the front page of the Tuesday Wall Street Journal, there was an article about a resumption of the tech bubble watch: things like the “silly names index” and fifth graders starting multi-million dollar websites.

There’s also been much talk about a bubble in the Chinese stock market.  Teachers and cab drivers quitting their jobs to trade stocks.  Triple digit gains.  An Initial Public Offering (IPO) for China Digital TV (NYSE symbol STV) was priced at $16, opened at $34, and is currently trading above $50.

Indeed it does seem like 1999 in many ways.

But wait – this is not going to be one of those “get ready for gloom and doom” articles (though gloom and doom is a distinct possibility).

Quite the opposite! With most booms and bubbles, there is a significant period of “irrational exuberance” before the bubble bursts.  And that “run-up” phase is where we seems to be right now.

Political pressure to keep the party going means that the Federal Reserve (and indeed, central banks around the world) is “persuaded” to act.  And they comply by pouring liquidity into the market in the form of both currency infusions and interest rate cuts. 

You can really throw a great party with tens of billions of new cash every week or two.

We are in the third longest bull market since 1900.  Only the 1920s and the 1990s had longer bulls.  How long can this bull run continue? How about months or even years…

Remember that in 1997 and 1998, analysts were already writing about the imminent demise of that bull market.  And the tough part is that macro economic indicators are traditionally very poor timing indicators.  There are signs out there that are hard to ignore.

The credit crunch is far from over.  The biggest chunk of sub-prime adjustable rate mort-gages have yet to hit their major adjustment cycle (this will hit hard because of the political implications of people “losing their homes” – even if they bought far more home than they could afford in the first place).  The dollar devaluation has yet to be fully felt.  And the list could go on and on.

So what are traders and investors to do?

Unless (and maybe even if) you make your trades based on macro economic indicators, we have to wait for price to tell us that things have changed.

Since March of 2003, we have not had a 10% pullback in the S&P 500 cash index on a closing basis (though on 8/16/2007) we did have an intraday price that was 10% below the previous highs).  So we are in a bull run and should invest that way until we see a 10% pullback (yellow flag), 15% pullback (bright yellow flag) 20 – 25% pullback (red flag).

Shorter term traders, can (and will!!) continue to buy and sell stretch points.  And I’ll continue to write about those stretch points… But until the market PRICE tells us differently, we are in a bull, and should continue to manage longer term trades in a manner consistent with the bull.

Great Trading!

D. R.

P.S.  Please note – the bull market that I’m talking about here is not be confused with “secular market cycles (or supercycles).  Secular cycles are normally measured in 5 – 25 year time frames, and this is the type of very long term “secular” market that Van has discussed in his macro economic articles.

About D. R. Barton: D.R. will be presenting his upcoming “Professional E-Mini Futures Tactics” workshop, November 10-12.

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 drbarton@iitm.com.

Melita's Inspirational Corner

An Update On Melita

Last week our CEO Melita Hunt shared her lung cancer diagnosis with you. 

She underwent surgery yesterday, October 9th, and is recovering well from that procedure. We expect that she will be released from the hospital by Friday, October, 12th. Also by Friday we expect the tissue sample results will be available and her doctors will know with more certainty what her final diagnosis and treatment options are.

She asked me to thank everyone for their well wishes and particularly for the jokes, which really brightened her day. Her spirits continue to be high and her optimistic view of life continues to touch everyone around her.  

Her blog, www.myleftlung.com, is up and she welcomes comments, or you can email mel@iitm.com.

Feedback

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