George Soros, the legendary investor, once described financial markets as “chaos.”

The events of past weeks, in all financial markets, bear witness to the accuracy of this statement. Despite this “chaos,” or maybe because of it, every traded investment vehicle has a price that reflects the underlying value of the assets and the future income stream. This is, over the long term, the basis of the efficient market theory. No matter what the asset, it will eventually gravitate to its most *efficient *price level over the *long term* due to the presence of “perfect information,” where are all factors are accounted for in the buy and sell offers. The phenomenon where it returns to is *previous *pricing level in the *short* *term* is known as “Mean Reversion.”

According to Investopedia, Mean Reversion is, “A theory suggesting that prices and returns eventually move back towards the mean or average. This mean or average can be the historical average of the price or return or another relevant average such as the growth in the economy or the average return of an industry.” For the trader, this means that profits can be made by placing buy and sell orders based on the price of the investment vehicle returning to its previous position, over the short term.

This has been found to hold true for interest rates, as just one example. In a March 2011 study by Jan Willem van den End, of the Economics and Research Division of De Nederlandsche Bank, โStatistical Evidence on the Mean Reversion of Interest Rates,โ it was determined that, based on two hundred years of annual data of the Netherlands, Germany, US and Japan, short-term interest rates and the yield curve “tend to revert to their long-term average value.” The same did not hold true for long-term rates, however, as “long-term rates can persistently deviate from it.” For long-term interest rates, based on the outcomes of smooth transition autoregressive ( STAR ) models, the force for mean reversion was strongest when “rates are far from their equilibrium value.” For this reason, mean reversion for interest rates is included in short-term financial models.

Foreign exchange pricing is also based on mean reversion trading for the short term, too. A paper, “Combining Mean Reversion Strategy and Momentum Trading Strategies in Foreign Exchange Markets,” by Dr. Alina F. Serban, found in 2009 that mean reversion patterns for foreign exchange trading is much like that of the equity markets. Dr. Serban, of West Virginia University, noted that, “I found that the patterns for the positions thus created in the foreign exchange markets is qualitatively similar to that found in the equity markets. Also, it outperforms traditional foreign exchange trading strategies, such as carry trades and moving average rules.

It is no different for Exchanged traded funds (ETFs). The two purest examples are (NYSE: QLD), the Proshares ULTRA QQQ (long the S&P 500), and (NYSE: QID), the Proshares Ultra Short QQQ (short the S&P 500) due to the wide asset base of each, preventing the news from one company distorting the price. Over each of the last five trading days (August 17-22), all very turbulent, the QLD, at some point in the session, rose above the opening price. The same was true for the QID. Over the last five day range (August 17-22, 2011), the QID increased 12.79% while the QLD fell 12.42%, yet, each ETF rose above the opening price every day as it reverted to the mean.

For an individual equity, the movement of the share price for Dell Inc (NASDAQ: DELL) this week serves on a timely example. On Thursday August the 18th, Dell Computer reported earnings which disappointed Wall Street. Closing at $14.20 on the Wednesday the 17th, Dell opened lower on the earnings and traded as low as $13.31 on Thursday the 18th. Even with the market down by 172.93 points on Friday the 19th, Dell hit a high of $14.62, up $1.32 over the low of the previous session, before closing up for the day. There was no news or announcements from Dell to raise its share price, just the stock price reverting to the mean.

The factors leading to a big gain or big loss for a stock are endless. Many times they are minor events that have no impact on the long term value of the company (the very basis of mean reversion). For Dell, it was earnings that were lower than the estimates of the analyst community. For a small cap, this can be even more monumental due to many being thinly traded. When a stock makes a big move, “hot money” immediately jumps in to chase it via program trading. As about 70% of the buying and selling on stock exchanges is now transacted by institutional investors, much of that high frequency trading, it feeds upon itself. Existing buy and sell orders are executed, reinforcing the direction of the price movement. The โcascading effectโ is actuated, where existing buy and sell orders are triggered, reinforcing the price movement.

Mean reversion also provides the foundation of high frequency program trading known as “statistical reversionโ or โstatistical arbitrage.โ This evolved from โpairs trading,โ where stocks are bought long and sold short on the expectation they will mean revert by following each other in price due to an established relationship. Statistical arbitrage involves a portfolio of a hundred or more stocks that are carefully matched by region and sector to eliminate exposure to beta and other risk factors, allowing for the adhering of mean reversion determined pricing relationships to the other stocks.

Larry Connors, author of โHow Markets Really Work,โstudied mean reversion trading for equities. Over a decade long period, Connors documented stocks that were at a 10 day high for the moving average and exiting when it closed below its five day moving average; and then buying at a 10 day low below the 200 day moving average and selling when it closed above the five day moving average. The results, according to Connors: “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. 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. “

Mean reversion is merely a function of perfect pricing information for an investment vehicle, after all. The price of the investment vehicle, be it an ETF or an equity or a foreign currency unit, was at that level for a reason. A short term event, such as missing earnings by a few cents, does not alter the fundamental economic value of the asset as determined by the long term input of perfect information. As Connors notes, “Markets are more efficient long term. There is little statistical evidence to support 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.” Mean reversion allows for traders to profit from the โchaosโ of financial markets due to the perfect information accounted for in the long term price of an investment vehicle that is ignored by the short-term inefficiencies of event-driven buying and selling on exchanges.

**By Jonathan Yates**