I think most of us will agree in order to be successful trading we need some kind of an edge. A good friend or relative who is willing to give us inside information where he is employed always works well, but most of us have to rely on something more fundamental or technical. I realize there are gifted people out there who have the ability to disseminate large amounts of information and draw accurate conclusions based on fundamental analysis, but I have always used technical analysis to gain my edge.

We use a model that is comprised of multiple trading systems, money management strategies and psychological disciplines, each of which give us an independent edge in that respective area. The combination of the three however is what really adds up to very playable over all edge.

When building an edge one must first consider what will drive the performance. Is it volatility, overbought and oversold counter trend conditions, or trends? Also how will you protect the capital when these conditions are not dominant?

Let’s examine one of the models that we use which is dependent on trends. It was developed in a joint effort between Angus Jackson, Inc. and Traders Tech., Inc. For all intent and purpose it is a trend capturing approach.  The first basic concept that needs to be understood is we need trends to succeed and a way of protecting the capital when markets are not trending.

Dean Hoffman, system developer and president of Traders Tech said “When you think about it, trends can be seen everywhere. Temperatures gradually trend from warm to cold as winter approaches. The demand for gasoline gradually trends higher during the summer driving months. Ground moisture trends from moist to dry when a drought approaches, and interest rates trend from high to low or low to high over time, and so on. All these events, whether seemingly natural or unnatural, can create sustained price trends in the futures market, and it is from these trends that we can try to profit.”

The real difference among traders is how they determine the beginning and end of a trend. A trader may define the starting point of a trend as something as basic as a change in the direction of a moving average. Counter-trend traders, on the other hand, might use this same indicator as a sign that the market is overbought and getting ready to head lower. Both are potentially correct depending on their exits. The real question is how can we quantify these trading approaches and code them into profitable trading systems.

Along with entry and exit methods, a trader must also have a position sizing and money management plan. Even if his entry and exit points are 90% accurate, if a trader risks it all on every trade, at some point the odds are substantially in favor of him losing all his money. By the same token, a system that is only accurate 10% of the time but has proper money management could do well. The bottom line is that traders need to know precisely how much of their account to put at risk in any given trade. It is also necessary to know how many positions to buy or sell whenever there is a signal. An expert system should provide traders with all this information and therefore an edge.

The final piece of the puzzle is proper trading psychology. It does not matter how brilliant a trader’s systems are, if he is unable to take the heat during the inevitable drawdown periods, he will fail. By the same token, if he gets too ecstatic during winning periods, he will also tend to fail. The key is emotional consistency. A trader must have complete confidence in his approach. This is where extensive (and proper) testing can help. Testing can help to build up solid proof that what a trader is doing works over the long run.

The more small edges a trader can build, the greater the probability of success. The small edges built into each system combined with those built into the money management and the psychological discipline will add up to enough total edge to trade with success.

In our model we use 5 different systems simultaneously, that will give a trader an advantage over only trading one. All these systems are essentially trend capturing, yet they also incorporate features that signal counter-trend following.  These different systems also communicate with one another and trade together as one integrated unit. For example, if one system has already invested heavily in Japanese Yen, the other four systems know not to take any more trades in that market. Doing so would not help to diversify but only raise the risk in the same trading idea.

Another unique edge that we have built is called “dynamic portfolio logic.” Unlike most systems that predefine a smaller portfolio to trade, our model trades almost every liquid commodity. The reason is because the systems rank the markets into percentiles on a daily basis. It then narrows the list down to only those few markets that have the highest relative trending potential. The net result of this filter is an edge that shows us where the best opportunities lie. It also guards us against the possibility that the best trends may surface in markets that are not in a static portfolio we may have chosen.

We have also paid close attention to position sizing and money management. It specifically manages how many contracts to enter when a trader gets a trading signal. This is essential because different futures contracts have different volatilities and trading them all in equal numbers would not be properly diversifying. If a contract, for example, tends to have high-volatility, a trader should trade fewer of those than another whose volatility is lower. We use a trader’s account size to determine the proper position size for each trade he makes.

Frequently the right position size is simply zero, and there are four reasons for this:

First, if the trade occurs in a market that is not strong enough in rank to be in the dynamic portfolio. Second, if, given the account size, the risk in the trade is just too high. Third, if there is already enough risk held in that given sector. And finally, if there is already enough risk across all the current positions in the portfolio.

The model does not risk more than 1.5% of a traders account size in any given trade. It will also not risk more than 5% of his account in any given sector. Let’s suppose, for example, that a trader purchases crude oil for his account. If the risk in that trade amounts to 5% or more, the model will not take new trades in any markets that are highly correlated such as unleaded gasoline.

The model will not risk more than 15% of a trader’s entire account at any given time. Meaning, if every trade a trader is in were to hit its stop price simultaneously, the total should represent no more than about 15% of the entire equity of the trader’s account. Once the risk level either reaches or goes past this 15% level, it will reject all new trades and return a position size of zero.

In summary, this was our attempt to build a model of small edges in each category of method, management and psychology, so when combined, we would have a large enough edge to trade with confidence.

The goal was to capture trends in trending periods, while protecting the capital in trendless periods.

Now, as we know, no matter how much precaution a trader takes, money can be lost and therefore any model should be monitored daily for red flags. After all, in a profession of such uncertainty, anything can happen, so we have to prepare for this before putting our money on the line.  Having an edge (or a series of them) is essential.

In the movie “Rounders,” Matt Damon who plays a skilled poker player, says in the opening scene:

“Here’s the thing. If you can’t spot the sucker in your first half hour at the table, then you’re the sucker.”

So don’t get taken. Ask yourself “what is my edge and how will I be protected when my edge is not working.”

If you would like to receive a detailed research report on our model please email me at charles@viewpointsofacommoditytrader.com or call (800) 858-2340 Toll Free.
 
 

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Nick is an engineer by degree and therefore has a great background for the mathematical challenges of “back testing.” In fact, of all the system developers that I have worked with (and there are plenty) I think Nick has one of the best grips on just how challenging it is to build a reliable trading system.

It’s not what you think though. It’s not because he has found ways around the limitations of “the back test,” it’s because he knows you can’t. Therefore he must build in strict money management to address the inevitable surprises of randomness.

Nassim Taleb, in his book The Black Swan, coined the phrases Mediocristan and Extremistan to distinguish between two types of randomness. All randomness is not created equal and must be dealt with, as it is what destroys most trading systems in the “real world”.

In Mediocristan, everything has boundaries and limits that can be easily measured. Things like IQ scores, height, and weight, and how much people smoke would be examples of Mediocristan.

If we were to randomly select 1000 people and calculate the mean weight, we can be reasonably assured that no one sample will dominate the distribution. In other words, you may find the average weight of 1000 men to be 200 lbs, but you won’t find a man weighing in at 200,000 lbs. Even the heaviest man in the sample will not materially affect the mean value of a distribution.

In Extremistan, the variation within distributions is quite different. In Extremistan, one sample can affect the entire distribution.

Let’s say that instead of weighing the 1000 men we find out their incomes. Now, even though the mean income might be $75,000 for 999 men, what if the final entry made 75 million a year? Now we have one event that’s 1000x the mean and also doubles the mean value.

This one sample (event) can change the entire distribution, and in some ways makes it meaningless to depend on. After all, if I am trying to get some idea of what to expect from sampling, and one event can ruin the whole thing, what good is it?

Joan Baez once said “hypothetical questions get hypothetical answers.” When building a trading system, we need to be aware of the limitations of the back test and not buy entirely into the hypothetical suggestions. We not only need to be aware of potential extreme randomness, but we also need ways of protecting ourselves when it happens.

In a recent article about building trading systems Nick said, “Traits that make a good system developer are often the opposite of those that make a good trader. A developer always wants to improve their methods and make them as efficient as possible in regard to risk and reward. A trader understands that there is no efficiency in making money and takes profits when the market gives it to them, while continually managing their risks.”

Most system developers constantly evolve their systems to produce better and better backtests (with hindsight, of course). Our strategies, however, were not developed prior to trading, but only after several successful years of trading and analyzing what consistently works in real-world trading.

In other words, we built our systems around our “real world” trading that was successful as opposed to creating something in the computer that “should work in the real world.”

Nick goes on to say, “Unfortunately, there is no perfect system that works in all market environments. The key in trading is to use the right system in the right market environment, but this is an art based on years of experience and insight. There are, however, some robust methods that can be used to implement commodity trading systems or strategies into the real world and make the crossover from hypothetical to reality as least painstaking and unexpected as possible.”

“We focus on two main priorities in implementing commodity trading systems into the real world. The first is on breaking down the robustness of the performance and the second is on managing risk and exposure, including keeping the maximum drawdown within reason.”

To break down the robustness of a system, we do two types of tests. The first is to obtain a set of robust metrics on the commodity trading system. The second is to test across a large set of data followed by several subsets.

The metrics that we feel are the most reliable are:

1. High positive Mathematical Expectation
2. Large average trade
3. Low standard deviation of daily returns
4. High MAR Ratio
5. Low leverage and exposure
6. Low or positive tracking error
7. High profit /cost ratio

(Note: for any test to be of any value, testing on markets only liquid enough to trade and realistic slippage and commission assumptions must be used.)

In the trading world we have a duel edged sword. On one hand we are unfortunately held hostage to the same extremistan surprises that could blow us up. On the other hand these are the events that can make us wealthy. In trading, it is the same extreme moves that make the effort worthwhile.

It is one of those businesses that one or two big events (trades) can make your whole year. Just like authors or rock stars where a handful of stars generate most of the revenues, a handful of trades do the same thing.

The trick is staying alive long enough to be there when they come.
 
 
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In Part one we talked about how outlier events in the markets play such an important role in our final performance, yet how few are really aware of this fact. These events come out of nowhere, yet the experts concoct explanations for them after the fact, making them explainable and predictable and therefore unlikely to happen again, and besides, everything smoothes out in the long run. Well, these explanations might make us feel better at the time but are hardly useful.

So, what can we do to minimize some of this complex uncertainty?

One thing for sure, we should really take a strong look at “market timing” in the traditional sense. If missing a few good days can wreck your long term performance, timing the market has a whole other dynamic to threaten us. What would happen to our performance if we missed the 10 good days but were in for the worst 10 days? Or, what if we avoided the 10 worst days? I know this much, if a few periods can be so meaningful; it puts quite a strain on the timer.

We should also take a good look at the traditional long term buy and hold strategy like mutual funds, ETF’s and indexing. You may think your mutual fund manager has downside “outlier” protection, but is that true? He can’t afford to be out of the market while his competition is in. He might lose his job. Obviously ETF’s or Indexes have the same problem. So, if you stay exposed to reap the upside rewards you might get clipped, yet if you time the market, you may miss the upside and still be exposed to the downside. See what I mean about hard?

As trite as it sounds, the first thing we need to do is understand completely what we have invested in. Do we really know how it works, what environments it should do well or not? Next, we need to know what amounts to risk and still sleep at night if everything went wrong. I know this sounds rudimentary, but most people don’t give these things enough thought. I think they have a passing knowledge of what they invest into, and assume the advisor knows what to do. Well, I wouldn’t risk it.

I think we need to take on these responsibilities and stop assuming our advisors have. Since it is so difficult for them to predict the good periods, we should concentrate on what we can control, the downside outliers. We most likely won’t be smart enough to see those great periods coming, but we can protect ourselves against a catastrophic loss that we won’t recover from.

It is critical we understand the investment. It is critical to understand the relationship it has to the current financial climate, or otherwise how do we know how it will behave? It is critical to stay in liquid investments and have an “uncle point” where you call it a day. We are all aware of using a stop on a trade, but how many of us use a stop on a mutual fund or a money manager? We assume our advisor is doing that. Well, good luck with that.

Finally, it is critical to consider the impact on our overall net worth if it goes wrong, otherwise how will we behave? Look at the BP situation. You would think BP, of all the oil experts in the world, would have guarded against something as catastrophic as this spill. Now they are simply reacting, trying anything to stop the oil.

Once we follow the advice above, a reasonable application might be to get aggressive with a small % of the overall trading capital. This is a technique that I use all the time. For example, one could invest 15% of his capital into a fund, ETF or futures trading program and risk 100%. If the idea works out, the interest on the 85% cash position plus the aggressive 15% market position should give you a reasonable overall return yet with a definitive 15% downside. Also, since your worst case is identified, you should not be shaken out by volatility or outlier days.

Obviously the numbers can be manipulated to suit individual appetites for risk. Risk a 5% position in three ideas for an overall 15% exposure or a 5% position in six positions if you want to risk 30%.

This is not to be confused with a trade risk. In a managed futures program or mutual fund the individual risk on each trade should be much less.

I’m saying to risk the 15% on the success of the idea. If you control the unexpected downside surprises of your ideas you will eventually hit some upside outliers. If not, and all your ideas are just plain wrong, I suggest visiting with an Algiers voodoo doctor to remove the hex, or simply finding another line of work.
 
 
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Malcolm Gladwell in his book Outliers says an “outlier is a scientific term to describe things or phenomena that lie outside the normal experience”. In Florida for example, you can expect most days during the summer to be somewhere between hot and Africa hot. What if however, in the middle of August, the temperature dropped to forty degrees? That would be an outlier, but more importantly, we would be hard pressed explaining why it happened. We seem to have a good understanding of what is normal, but know a great deal less about the ”outlier”, in spite of the hind-sight explanations.

What role do outliers play in the markets? Are the returns in a buy and hold strategy in stocks reasonably distributed?  Do we really achieve steady returns over the long term like the experts say? I don’t think so.

As they say, the devil is in the details, and a closer look into long term returns paints quite a different picture than what we’ve been sold. It turns out that long term returns are anything but smooth and steady, or reasonably distributed. In fact, most of the upside (and downside) performance we see over the long term comes from a few outliers. 

In their book Dance With Chance, authors Makridakis, Robin and Hogarth, researched the standard deviation of returns for the DJIA from 1900-2007 with some surprising results. The time period reflects a rather large sample of just shy of 30,000 days with more “outlier” days than one would suspect. An outlier day is defined as three or more standard deviations from the mean (average) return for a day. This is a mathematical formula that determines when returns are outside the experience of “normal” returns.

Academically speaking, if returns were “normally distributed” there should have been about ninety days where the daily returns were more than three standard deviations from the mean. In the real world however, there were 429 days. That is almost five times as many as modeled. Even more surprising, there were 91 days where the returns (or lack of them) exceeded five standard deviations from the mean. According to the “The Normal Distribution” there should be essentially none at that extreme. If this were happening in my Florida weather example, we would have some very upset tourists shivering on the beach.

This characteristic of extreme measurements drives the academics crazy when modeling. As the Authors say “Typically, the attitude of financial forecasters and statisticians alike consider the 429 days outside the three standard deviation limits as “outliers” (somehow  external to the system) and the 91 days as outside five standard deviations as non-existent. Ignoring them allows analysts to use the nice, comforting properties of the normal distribution for making calculations”

Well, this may be O.K. if you measuring something more stable like heights and weights or smokers, but ignoring “outliers” in our field can lead to financial suicide. The October 1987 massacre for example was twenty three standard deviations below the mean (a drop of 22% in one day). If you’re not aware that something like this can happen, you’re likely to have a heart attack some day. The research proves that outliers have an enormous impact on long term performance and a few days here and there can mean everything.

 Javier Estrada in his paper Black Swans, Market timing and the Dow elaborates more on this concept when he pointed out that during this same period “missing the 10 best days in the stock market resulted in portfolios 65% less valuable than a passive investment, whereas missing the worst 10 days resulted in portfolios 206% more valuable than a passive investment”. In other words, if a person was lucky enough to avoid those 10 days he ended up with three times the money in his account at the end of the rainbow. 

So, what can we do with this information? Obviously, this is the tricky part. You have to be in the market to get the upside outlier periods but at the risk of the downside periods. You know, I’m beginning to think this investing thing is hard. There’s always something to worry about.

Check back soon for Part II where we explore some concepts that help us cope better with these uncertainties.
 
 
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Traders are entrepreneurs and must minimize their weaknesses and capitalize upon their strengths. Their success lies entirely in their own abilities. I am a believer in tapping any resource that can help you maximize your potential. A good trading coach can help you identify the everyday challenges a trader faces, and a plan to meet those challenges. Lessons can range from psychology, expectations and performance to help in identifying trading opportunities.

Below I have a short guest post from Jim Maley, who is a thirty year veteran trading the markets and also helps coach traders.

Jim provides a personal road map if you will, a practical set of tools and insights that can be worth their weight in Gold.

If you would like to run anything by Jim (free of charge) he can be reached at james@stockmarkettutor.com

His website is JMStockMarketTutor.com

“The Emperor’s New Clothes”

   By James Maley

“The Emperor’s New Clothes” (View Related Post Here) is a tale by Hans Christian Andersen about two weavers who promise the Emperor a brand new suit of clothes. However, there is one catch. If you are either unfit for your position or incompetent you cannot see the clothes.

The weavers are swindlers. So, they dress the Emperor in mime and tell the Emperor how good he looks. The Emperor who cannot see the clothes himself, pretends that he can in order not to be viewed as either unfit or stupid.

When a child cries out “the Emperor has no clothes.” The Emperor ignores him and continues along in his procession.

This fable reminds me of the current rally in the stock market.  Investors keep pouring money into the stock market because they are made to feel stupid by the prognosticators and talking heads even though history says it is probably not a good time to buy stocks.

Historically, if you look at normalized price to earnings ratios the current one of 19.1 is right up there with some of biggies:

                          December  1973           18.3

                          August        1987           18.6

                          Prior to 1929 crash     20.1

As you already know these were not very good times to buy stocks.

The latest investor intelligence figures (View Related Post Here) states that there are just 17.4% bearish advisors. The 15 year low are 15.6% advisors being bearish. This means that the majority of stock market letter writers etc. who are polled by Investors Intelligence are telling their clients to buy stocks now.

This indicator has been around for years and is a reverse indicator. By that I mean at extreme reading like now, the advisors are usually wrong. For example, another sentiment indicator “The Daily Sentiment Indicator” is currently at 90+% bulls. It was at 3% bulls at the low in March 2009.

Finally, we have rallied about 60% from the March 2009 low and over 15% from the low last February (just 21/2 months ago).

To borrow a phrase from John Hussman we are overvalued, over-believed and over-bought.

JMStockMarketTutor.com
 

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Unfortunately the “do it yourself” approach is no guaranteed success either.  The problem lies in the ability for the investor to maximize on his do-it-yourself approach. You can have the fastest execution at the lowest cost available and still consistently lose money. You can back test until your blue in the face only to find the “real world” upsetting your back tested strategy. 

I hear traders all the time use the term “statistically significant”, when in fact it is nothing more than an illusion of certainty. There is uncertainty in all statistical suggestions, but some are more like intellectual fraud. These TV advertised platforms are particularly dangerous in their limitations. 

There are countless obstacles to overcome when building dependable quantified trading strategies, but one limitation in quantified trading will never be resolved, and must be addressed through proper risk management strategies. 

Let’s focus on that one. 

In his book The Black Swan, author Nassim NicholasTaleb coins the phrases Mediocristan and Extremistan to distinguish between two types of randomness. All randomness is not created equal if you will.

 In Mediocristan, everything has boundaries and limits that can be easily measured. Things like IQ scores, height, and weight, and how much people smoke would be examples of Mediocristan. If we were to randomly select 1000 people and calculate the mean weight, we can be reasonably assured that no one sample will dominate the distribution. In other words, you may find the average weight of 1000 men to be 200 lbs, but you won’t find a man weighing in at 200,000 lbs. Even the heaviest man in the sample will not materially affect the mean value of a distribution.

In Extremistan, the variation within distributions is quite different. In Extremistan, one sample can affect the entire distribution. Let’s say that instead of weighing the 1000 men we find out their incomes.  Now, even though the mean income might be $75,000 for 999 men, what if the final entry made 75 million a year? Well, that would mean we can legitimately see a single event that’s 1000x the mean (there are people who make 75 mill), and also double the mean value.

This one sample (event) can change the entire distribution and in fact in some ways make it meaningless to depend on. After all, if I am trying to get some idea of what to expect from sampling, and one event can ruin the whole thing, what good is it?

In this case, it misleads me into thinking that the mean income of the 1000 men is $150,000 per year, twice as much as the 999 men actually make. So, if I had a product that was dependent on the fact that the 1000 men made $150,000 with little variation, I would be quite surprised when my product only sold to the rich guy.

Now, let’s say the income examples above are trades. That extreme entry could be the event that sends us to the financial graveyard. We are at risk of pulling a sample that excludes the extreme event when back testing, fooling us into thinking that the distribution we have is dependable enough to trade. Then we are likely to build an approach based on this sample and these assumptions. When we finally are confident enough to trade the strategy, we are unaware of the extreme, and therefore unprepared and eventually surprised.

In the trading world we have a duel edged sword. On one hand we are unfortunately held hostage to the same extremistan surprises that could blow us up. On the other hand these are the events that can make us wealthy. In trading, it is the same extreme moves that make the effort worthwhile. It is one of those businesses that one or two big events (trades) can make your whole year. Just like authors or rock stars where a handful of stars generate most of the revenues, a handful of trades do the same thing. The trick is staying alive long enough to be there when the come.

Quantified trading systems and strategies, like randomness, are not created equal, and therefore should be scrutinized to see how they will handle the adverse extremes. Constant risk monitoring is the key.
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I thought I would put out the performance of 40 different markets (compliments of Financial Visualizations) including the stock indexes and interest rate markets for comparison. I find it amusing how some markets that are really outperforming (or underperforming for that matter) are hardly ever mentioned, but could provide great trading opportunities.

Here are the relative performances of those 40 markets in two time frames. The first chart is the performance year to date. The second is the performance over the last 30 days.

I will update these figures on a monthly basis.

NEW – Weekly Blast from the Past!!

Now, I would assume that Ben Bernanke has a research staff, and access to information, that would dwarf our capabilities, not to mention he can call anyone he wants on the phone and pick their brain.

So, in light of that, how in the world could he have been so wrong with such a high degree of confidence?

This is just another example of the “expert” arrogance. The old “I’m right so why should I have a plan B” way of thinking. On a brighter note, it is more evidence of a few things I know to be true.

First, it is extremely difficult to predict and forecast large systems such as the financial markets or the economy, no matter how many facts you think you have.

Second, if with all his resources he can be this wrong, that doesn’t bode well for the rest of us who might be going about the markets in a similar fashion.

Third, it’s OK to have an opinion (forecast) if you firmly believe, but it’s not OK to bet the farm and have no real plan to manage the situation if you’re wrong.
 

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Those that have knowledge don’t predict. Those that predict don’t have knowledge – LAO TZU 

What criteria did you use to choose your latest investment? A better question would be, are you sure that you even understand the criteria? 

For example, let’s say that you chose a mutual fund based on your advisors presentation of how well managed the fund is, and the fact that it has done so well over the last x amount of years. He then assures you it will do well into the future based on the past facts. On the surface this seems reasonable until one considers a few undeniable facts. One, the advisor really has no idea of the future, and two, the performance could be random.  

In fact, history proves that the average mutual fund over the last 25 years has grossly underperformed the market averages. Keep in mind that the advisor is in the business of showing you products that compete with the averages, and is not paid to guide you into something as simple as “buy the S&P.” Also, the fund managers themselves continually compete with the averages, selling their “expert” forecasting as better mousetrap. 

In their book Dance With Chance, Authors Spyros Makridakis, Robin Hograth & Anil Gaba point out a rather astounding piece of research. I think most people have heard that the fund managers don’t out perform the averages in the long run, but I’m not sure that they know to what degree. 

According to the authors, if a person had invested $50,000 into the market average (S&P), 25 years ago they would have 1,061,527 today. The average Index fund (those that track the S&P) would have sent you back 1,015,541. Pretty close. However the same $50,000 in the average mutual fund would have sent you back $541,735. Not so close. 

The significance of this is that the S&P is not a person and therefore can’t claim any expertise. It is essentially the growth of 500 companies. On the other hand the fund managers do claim the expertise, but can’t really seem to deliver it.  It’s amazing how people continue to fall for this, but I suppose the lure of large returns is just too strong to resist. 

When one considers the survivorship bias for those that actually did outperform the averages, the picture becomes even dimmer. In other words, many of the funds that have outperformed the averages still cannot be attributed to skill. Some for sure, but not all.  

To make my point simple, consider a scenario where 1000 manages have a 50% chance of beating the S&P in a particular year. After one year there would be 500 that did. By the fifth year there would be 31 that have beaten the S&P five years in a row. Pretty impressive, but still cannot be attributed to skill.  

After 10 years there will be one manager, from sheer luck, that will have beat the S&P for ten years in a row. The last man standing, the survivor so to speak. Very impressive, but still luck. Past and future performance simply are not related, therefore chasing past success should not be the primary reason to invest.   

What is even more astounding is the “I’ll do it myself crowd.” The average investor investing on his own, according to the research, ended up with $271,371. In other words if the average investor just stayed with the S&P he would have had 4x the amount of money. This could be the difference between financial freedom and “I owe, I owe, it’s off to work I go.” 

I guess this is one walk of life where many of us wish we were just average. 

John Kenneth Galbraith once said “when it comes to the stock market there are two kinds of investors. Those who don’t know where the market is going, and those that don’t know that they don’t know where the market is going.” 

I guess people will draw different conclusions from the above statistics, but I agree with the author’s conclusion. DON’T TRY TO BEAT THE AVERAGES. If you are a stock market investor buy the S&P (or another index or ETF), the numbers speak for themselves.  

If you like gold mining companies buy a mining index (ETF) and don’t try to find the company that will outperform the index (many mining companies). It appears that our odds of choosing the fund, or stock, that will consistently beat the indexed averages is practically impossible. 

Also BE PATIENT the authors say. People just don’t seem to exercise the patience that is required to do well as a investor. Don’t give into the temptation to try and sell the highs and buy back later, or quit your trend following system because it had a sluggish year. The average investor will fail at this. Play the probabilities of long term growth and trends. 

Finally be RISK AWARE, the area that I think is the most important. This does not mean risk-adverse. It means realize that what you don’t know is always greater than what you do know….. and manage your risk accordingly.   

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Here is an interesting video of Trader Salem Abraham and Howard Lederer, legendary poker player. They discuss the similarities in managing a good poker hand and a good trade, and how folding bad hands in both cases is the key to success. 

Howard Lederer is known as “the Poker Professor” because of his demeanor, analytical style and long history of wins. He is known for his focused, calm demeanor at the poker table; he has stated that he has applied Zen wisdom to his poker playing. He has won two World Series of Poker bracelets and two World Poker Tour titles. In 2008, he won AUD1.25 million by winning the $100,000 No Limit Holdem Challenge, billed as the world’s most expensive tournament buy-in, at the Aussie Millions. As of 2009, his total live tournament winnings exceed $5,200,000. 

Here’s another post and video with Howard Lederer.

Salem Abraham is the founder of Abraham Trading Company (ATC). Abraham’s trading methodology is a 100% systematic, long-term, trend-following approach, implementing filtering techniques that avoid trends with adverse risk/reward characteristics.

Abraham says “While the goal is to capture long-term trends, the system only enters the market during periods when the risk/reward of a trade is heavily in the trade’s favor. If unacceptable risk characteristics exist, the system will even avoid trends that have a positive profit expectation. The end result is a trading method that has historically provided exceptional returns that are uncorrelated to traditional stock and bond investments.” 

“Over the last few years the markets have become choppier, and many traders’ returns are suffering. In ATC’s continuing research efforts, a technique was discovered that helps to identify these choppy markets and keep the system out during these periods. ATC modified its system with a filter, added markets to the portfolio, and made other changes that will continue to help performance in non-trending markets, while simultaneously not penalizing returns during trending periods. All the models tested are robust with very few degrees of freedom/parameters.” 

“A myriad of proprietary statistical techniques are looked at that include analyzing multiple time period subsets, market-by-market as well as sector analysis and correlation, risk/reward analyses, parameter degradation studies, slippage analysis, and drawdown analysis.” 

“Experience helps in developing a successful trading system. The most common and most dangerous error made in system development is curve fitting. One thing we have learned over the last 15 years is that curve fitting cannot be understood by theory alone. There are many statistical traps that can only be learned by trading systems real-time.” Read related post

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