Don't Be Fooled By The Fancy Name -- Statistical Arbitrage Is A Simple Way To Profit

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Every profession has its buzzwords to create the illusion that things are more complex than they really are. Everything from the Latin terms used by medical doctors to the chatter of gearheads talking about the latest car engine, simple concepts are often clothed in complicated-sounding terms.

Investing professionals are no different in their use of complicated nomenclature to describe simple things and ideas. 

I know I was intimidated when I first heard the term statistical arbitrage. To me, it sounded like I would need a math Ph.D. or at least an advanced understanding of statistical theory to figure out what it meant. Not being an advanced math person, I was fortunate to have had a trading mentor who patiently explained to me what statistical arbitrage is and how to use it profitably. 

Ever since I was made aware of this unique and profitable trading technique, I have used it in a variety of market conditions to capture profits that would otherwise be unavailable. This method's not for everyone, but if you're an active investor who is looking for additional tricks of the trade, statistical arbitrage may be just the ticket.

What Is Statistical Arbitrage?
Simply put, statistical arbitrage is a fancy term for pair trading, which is the buying or selling of a pair of stocks based on their relationship with each other. 

Often, the stock price of companies in the same sector or type of business follows one another very closely. A pair trader observes the relationship between two stocks and buys or sells whenever the relationship gets out of sync, acting on the assumption that the historical correlation is likely to continue. 

Is it a foolproof method? No, but it does provide another tactic in your investing toolbox. 

It is easier to understand this concept with an illustration. The following chart shows the relationship between Coca-Cola (KO) and Pepsico (PEP), perhaps the most popular stock pair for statistical arbitrage.

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Notice how closely the two stocks follow each other until near the end of May. At this time, Pepsico falls out of sync with Coca-Cola, dropping as Coca-Cola stays steady and starts to climb. Statistical arbitrage traders would purchase Pepsico stock as soon as the divergence is recognized. 

As you can see, the pair quickly moved back into sync, providing a profit opportunity for statistical arbitrage traders. There are multiple ways this can be approached. 

For example, let's say Coca-Cola started rapidly climbing higher than Pepsico. Savvy statistical arbitrage traders would short Coca-Cola shares in anticipation of its price falling back into the historic correlation. 

In addition, the idea is not just limited to two stocks. The same idea can be applied to groups of three or more correlated names. However, special software is often employed to manage multiple-issue statistical arbitrage. 

Here's another well-known pair trade: Wal-Mart (WMT) and Target (TGT)

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As you can see, Target climbed out of the historical correlation range on the chart. Traders invested in the pair would short Target, holding until the historical correlation came back into sync.

It's important to remember that it's not always obvious names that present enough correlation to pair-trade. One example of this is the relationship between Citibank (NYSE: C) and Harley-Davidson (HOG).

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Other than trading on the same exchange, I cannot imagine why two companies that are so diverse would be correlated so closely. The reason could have something to do with the fact that consumers may borrow money to buy Harley-Davidson motorcycles, but that's only a guess. 

Risks to Consider: Although closely correlated stock pairs generally come back into sync with each other after diverging, there is no rule that says this has to happen. Stock pairs can stay out of sync for a substantial period of time, depending on the underlying circumstances. Always use stops and position size properly.

Action to Take --> Begin to chart the common pairs like Coca Cola and Pepsico, General Motors (GM) and Ford (NYSE: F), and other closely related companies. In addition, experiment with finding correlated pairs by simply charting a variety of pairs of stocks. Although I like to use daily charts, tradable correlations can be found in all timeframes. Professional traders often use software, rather than visual charts, to find historical pairs showing a statistical aberration from each other. Some trading platforms have this ability built in, but this type of software is readily available.

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