Over the many decades of academic studies and research done by market professionals, nearly everything imaginable has been tested in an attempt to predict the direction of a company s stock price. P/E ratios, dividend yield, revenue growth, book value, earnings growth, etc. have been looked at over and over. The one conclusion that can be made from these studies is that few, if any of them, show a real statistical edge. The one area which we believe remains fertile ground for further research is price behavior.

What is price? It s essentially a culmination of all the known information of a company. Therefore, price is real as it represents what the majority of the market participants know at that given time.

Finding Market Inefficiencies

Shorter-term, we believe that markets (prices) can be very inefficient. We have published some of these inefficiencies over the years and we ll continue to publish them over time. Most of these short-term inefficiencies tend to occur whenever there is either news in a stock, or there is a great deal of fear in the market place. One only has to think about what happened in the summer of 2007 to fully understand these inefficiencies. Solid performing companies saw 20%, 30% and 40% of their value lost in a few days as market participants (primarily institutions) irrationally sold stocks, only to see the prices of many of these stocks quickly bounce back to their previous levels. These types of market inefficiencies can be found on a regular basis in individual stocks, especially on unexpected news-related events, or when market volatility rises.

Longer-term, markets are supposedly more efficient. This is the concept that has been taught by many in the academic world. But, if it was true, you wouldn t expect low volatility stocks to outperform high volatility (and often times high beta stocks) by a better than 2-1 margin since 1995, would you? But they have…

How to Find Safe High Probability Stocks

Is it possible to increase your investment returns using only price as an indicator? We believe the answer is yes, it is. And here s how you do it.

First, the one indicator that best measures the movement of a stock (and based upon the statistics is the best indicator to predict a stocks direction over the next year) is historical volatility.

What is historical volatility? Volatility is the annualized standard deviation of daily returns (don t get scared away…it gets simpler from here). Simply stated, it s the movement of a stock price without regard to direction. Large average daily stock price changes (in percentage terms) mean high volatility, and small average daily price changes mean low volatility. Stated another way, the more a stock moves, the higher its volatility. The less it moves, the lower its volatility.

Companies that are stable and are usually performing in-line with expectations tend to have lower volatility (this is logical, as these companies are more predictable). Companies that have a great deal of uncertainty to them (think about the sub-prime lenders in 2007) tend to have higher volatility as more is unknown.

Do You Invest High-Flying Stocks? Be Careful…

Now, the key thing to learn is that since 1995, high volatility stocks have underperformed the market. In fact, most times you looked at them, the majority have been lower 252 trading days later.

On the other side of the equation, low volatility stocks have outperformed the market. And, most times you looked at them, the majority on average have been higher 252 trading days.

Let s look at the test that confirms this, and then we ll look at the results:

1. We ran these test results from January 1, 1995 through May 31, 2007.
2. The universe of stocks we ran them on had a dynamic volume filter, to assure we were looking at the liquid stocks, and numbered 11,282 different stocks.
3. Survivorship bias has been omitted (meaning all stocks that were public during this period of time were included in our universe).
4. Trading costs were not included.
5. We looked at every stock, every trading day. This means that we took the universe of stocks each trading day, calculated their historical volatility for that day and then looked at their performance 252 trading days (one-year) later.

Each day we ranked the 100-day historical volatility of all the stocks in our universe (we also tested using a 250-day HV and got similar results, further showing the significance of these tests). We then compared the 252-day performance of the stocks which were in the top 20% of volatility (the 20% with the highest volatility) versus the 20% with the lowest volatility.

As you can see, the stocks in the lowest 20% ranked by historical volatility outperformed the stocks with the highest volatility by a better than 2-1 margin 252 days later. And the stocks with the lowest volatility rose 74.5% of the time 252 days later, while the stocks with the highest volatility rose only 42.2% of the time, a significant difference.

And this difference gets even greater if you compare the lowest 10% with the highest 10%. For the lowest 10%, prices rose 77% of the time 252 days later, while the highest 10% rose only 39% of the time (meaning 61% of the time prices were lower 252 days later!).

One caveat to the above; the only time during the test period this didn t seem to hold true was in mid 1998-1999 when the internet bubble was being formed. Money was chasing the high volatility stocks and many of those stocks moved only in one direction. Therefore if you removed the bubble period, the returns for the highest volatility stocks would be even worse. In reality, as poor as the results have been for the most volatile stocks, they d be even worse without the 1998-99 bubble! This is something to think about the next time you see or hear someone recommending some high-flying stock as the next sure thing.

Summary

Trading high volatility stocks may be a good idea if you are looking for short-term gains. But for investing, it appears to be a recipe for underperformance and even potentially for disaster. We can show you many high flying stocks with extreme volatility that collapsed in price over the next twelve months. The safer and more prudent thing to do, is to identify stocks with lower historical volatility readings. A 100-day historical volatility reading under 30 is a good place to start. Since 1995, on average, better than 70% of these stocks have been higher 252 days later. This is a long-term track record that very few (if any) money managers and professionals can come close to replicating.

We ll continue to publish our research on this topic over time. If you would like to see lists of stocks which incorporate the above research, you can find them at www.PowerRatings.net.

You can email us your comments and thoughts to l.connors@cg3.com, and cesar@connorsresearch.com

Larry Connors is CEO and Founder of TradingMarkets.com and PowerRatings.net.

Cesar Alvarez is Managing Director of Connors Research LLC.

The following study was recently forwarded to me and it may be of interest to you if you use Candlesticks to make your trading decisions. I’ve never been a big fan of candlesticks as I have never been able to find an edge using them versus traditional bar charts. In looking at this study, these professors seem to have come to the same conclusion-they’re unable to find any statistical significance when applying them to equities.

As they word it, “Using robust statistical techniques, we find that candlestick trading rules are not profitable when applied to DJIA component stocks over 1/1/1992 – 31/12/2002 period. Neither bullish or bearish candlestick single lines nor patterns provide market timing signals that are any better than what would be expected by chance. Basing ones trading decisions solely on these techniques does not seem sensible but we cannot rule out the possibility that they compliment some other market timing techniques”.

Here’s the link to the study: Market Timing with Candlestick Technical Analysis

Does this mean that candlesticks are useless? Probably not, as there are many successful traders that swear by them. But, at least for now, I have yet to see any studies that show their effectiveness. If you know of any, please feel free to post them here.

Over the past five years or so I’ve declined most speaking invitations due to time constraints except for a few (mostly on television). But the CME forum was one I really wanted to do. Why?

First, because I have a great deal of respect for all of the exchanges as they are the heart of our industry. And second, the CME has done a tremendous job in building a great business, especially over the past few years. One only has to look at their stock appreciation since going public to see what they have built. In fact a few months ago I was visiting a friend in Chicago who has owned seats on a handful of the exchanges. He not only has held onto his CME stock through its rise, he told me he expects to pass it along to his kids 20 years from now. That says a lot considering that this guy’s success has come from holding most positions a few days (at most) for the past two and a half decades.

I want to thank Laura Oatney for inviting me and conducting the forum. Second, if you would like to listen to the presentation and learn the strategies I taught, it will soon be archived here on the CME site.

Click here to sign up.

The VIX is obviously very stretched as I post this. And historically, extreme VIX stretches have many times (not always…many times) preceded market turns to the upside.

Here’s an indicator to keep an eye on in the future:

1. Since 1995, whenever the VIX moved “intra-day” 20% or more above its 10-day moving average, the market has closed higher 81% of the time using a 5-day moving average as the exit for the SPX (meaning exiting when the SPX closes above its 5-day MA).

2. Whenever the VIX has “closed” 20% or more above its 10-day MA, it has closed higher 85% of the time using the same exit as mentioned above.

Both these results are based upon the SPX being above the 200-day MA on the signal day.

Extreme spikes in the VIX like we’re seeing today are fairly rare and have occurred only a few times a year since 1995.

The article-“Sometimes The Worst Are First” flies in the face of those professionals who continuously preach to the masses to buy good companies (of course they have little-to-no statistical evidence as to why one should buy those stocks). From 1983-2006, Fortune’s annual list of “Most Admired Companies” has under performed their list of “Least Admired Companies”. The most admired companies returned 15.4%; the least admired returned 17.8%, 6.6% above the S&P 500.

The study was done by Meir Statman (great last name), a professor of behavioral finance at Santa Clara University along with another professor and Ken Fisher of Fisher Investments. Here’s the study for your knowledge and enjoyment:

http://lsb.scu.edu/finance/faculty/statman/articles/fortune030207.pdf

I’ve been trading the markets for nearly three decades. Actually I’ve been in the markets now nearly 40 years…for my 8 year old birthday my grandfather gave me stock in Exxon and Laclede Gas. At 7 years old it was baseball — a glove and a bat for my birthday. At 8 years old it was stocks.

And ever since that time, both still compete for my attention every day.

I’ve also been seriously researching the markets for the past two decades. I’ve been lucky enough to have gotten to know and become friends with some great traders. And I’m also fortunate to have a team of market researchers who are a heck of a lot smarter than I am.

I’ve written a handful of books over the years. “Street Smarts” is probably the one I’m best known for. But “How Markets Really Work” is the one I’m proudest of because it’s the statistical source from which all our trading has evolved. My philosophy of the markets has not changed much over the years…it’s made up of only three simple pieces. They are…

1. Markets tend to revert to their mean on a short term basis. Once you figure that out…the game gets a bit easier.
   
2. Markets are efficient long-term. There is little statistical evidence to prove otherwise. But markets can be very inefficient short-term. There’s ample statistical evidence to prove this. And that’s where the best opportunities are today.
   
3. Risk control is underestimated, under-utilized, emotionally driven, and is likely the least understood aspect of professional trading and the market place. Get this part down, and you’ll likely have a very long and prosperous career over the years to come.

I’ll add one more piece to this list, especially for those of you who like to watch a certain TV station and read well known newspapers every day…“Opinions are fine. Statistics are better.”

How I Trade

The main theme behind my research and our trading is “reversion to the mean”. To us this is the holy grail of trading. This is not only our opinion; it’s backed statistically with literally thousands of tests we’ve run over the years.

Let me qualify this a bit. Reversion to the mean can be interpreted many different ways and on its most literal level, it carries little weight when one takes the belief that every stock reverts to its mean in every time frame. There’s nothing further from the truth.

But, in specific, recurring situations, reversion to the mean is the key to identifying market behavior. And we as traders only care about one thing-short-term market behavior. Longer-term predictions are tougher, and at least on the surface appears to be a game that few can beat. For every Warren Buffett in the world, there are (and have been) literally thousands of very smart Ivy League MBA’s, CFA’s, market analysts, etc, whose long-term performance has only been within a few points of the market averages. One would think that the entire exercise these people go through would be considered meaningless. But the fundamental analysis industry is much too big and established, and there’s no chance that this game will come to an end in our lifetime. I’m fairly certain that if each of the 9 year olds on the baseball team I coach bought 100 shares of the SPY today… they’d outperform 50% (or more) of the money managers in this country over the next five years. Not only am I fairly certain of this… I’d guarantee it (and I’m not in the habit of guaranteeing many things).

The edges lie elsewhere, and based upon what the statistics show over and over again, it’s in a reversion to the mean in short-term stock prices.

What is reversion to the mean? It’s simply the concept that prices move back to levels they previously were trading at. Again though, there’s not a great deal of evidence that it exists in longer-term pricing of securities. But shorter term-at least looking back more than a decade-it certainly has existed.

As I mentioned earlier, we can literally show you thousands of tests to prove this point. But to keep things simple, here’s one simple example.

• A stock is above its 200-day moving average. Today it trades at its lowest price in ten days. If markets are efficient, the future price of these securities should be random. There should be about a 50-50 chance of them rising or falling in the near term. But, in reality that has not been the truth.

Let’s look at the results from 1/1/95 to 6/30/06 of buying a stock that made a 10-day high (above its 200-day moving average) and exiting when it closes below its 5-day moving average; versus buying a stock that made a 10-day low (also above its 200-day moving average) and exiting when it closes above its 5-day moving average.

Here are the results:

	Avg % Gain/Loss	% of Winners	# of Trades
10-Day Highs	+0.21%	37.56%	352,389
10-Day Lows	+0.39%	64.93%	236,059

Two things stand out. First, the average returns for the stocks that made 10-day lows is nearly double that of stocks that made 10-day highs (see chart 1). Even more eye opening is the percentage of winning trades. Buying 10-day lows was correct nearly 65% of the time, while buying 10-day highs was correct only 38% of the time (see chart 2). And these numbers are not based upon a few hand-picked trades; they are based upon hundreds of thousands of trades.

Chart 1

Chart 2

Is this by chance? Not likely, especially when you’re looking at hundreds of thousand of trades over decades worth of time.

These statistics start to tell a story. And it’s a story that the long-term fundamental people, who have been doing this much longer, and with far more resources than us traders have had, can never tell, at least statistically. Prices on a short-term basis are predictable at certain identifiable times. Looking at 10-day highs and lows is just one very, very, simple example. Over time in this blog, I’ll go into this much deeper with you with both research and strategies. But for today, it’s a good place for us to start.

If you’d like to see more research (and statistically backed indicators from our research), you can find it on the TradingMarkets website.

Larry