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đź’ˇ IDEAS Pairs Trading: Introduction

Pairs trading is a market-neutral trading strategy that matches a long position with a short position in a pair of highly correlated instruments such as two stocks, exchange-traded funds (ETFs), currencies, commodities or options. Pairs traders wait for weakness in the correlation, and then go long on the under-performer while simultaneously going short on the over-performer, closing the positions as the relationship returns to its statistical norm. The strategy’s profit is derived from the difference in price change between the two instruments, rather than from the direction in which each moves. Therefore, a profit can be realized if the long position goes up more than the short, or the short position goes down more than the long (in a perfect situation, the long position will rise and the short position will fall, but this is not a requirement for making a profit). It is possible for pairs traders to profit during a variety of market conditions, including periods when the market goes up, down or sideways, and during periods of either low or high volatility.


Pairs trading’s origin is generally credited to a group of computer scientists, mathematicians and physicists assembled by Wall Street’s Morgan Stanley & Co. in the early to mid-1980s. The team, which included computer scientists Gerry Bamberger and David Shaw, and quant trader Nunzio Tartaglia, was brought together to study arbitrage opportunities in the equities markets, employing advanced statistical modeling and developing an automated trading program to exploit market imbalances.


Central to their research was the development of quantitative methods for identifying pairs of securities whose prices exhibited similar historical price movements, or that were highly correlated. While the team’s resulting black box was traded successfully in 1987 – the group made a reported $50 million profit for Morgan Stanley – the next two years of trading saw poor enough results that in 1989 the group disbanded.


Over the years, pairs trading has gained modest attention among individual, institutional and hedge fund traders as a market-neutral investment strategy. This is largely due to the advent of the Internet and advancements in trading technology. These two factors have helped level the playing field for individual investors, making real-time market data and powerful tools both available and affordable to more than just the institutional traders. True, the large hedge funds and institutional traders still have advantages (for example, robust proprietary systems and economies of scale). However, today’s market participants – whether retails traders or a team of highly-skilled mathematicians in a quant shop – have access to real-time financial market data, direct access trading platforms, advanced computer modeling and the ability to automate complex trading strategies.


Using technology - as well as drawing on fundamentals, probabilities, statistics and technical analysis - pairs traders attempt to identify relationships between two instruments, determine the direction of the relationship and execute trades based on the data presented. Here, we introduce pairs trading, market-neutral investments, arbitrage and provide an example of a pairs trade.
 
I find pairs trading to be one of the more intellectually engaging strategies in the market because it relies so heavily on statistical relationships rather than directional bets. What I like most is the market-neutral aspect — I don’t have to predict whether the overall market will rise or fall, just how two related instruments will behave relative to each other. That removes a huge layer of emotional stress for me, especially in volatile markets. I’m also fascinated by how this strategy grew out of high-level quantitative research and is now accessible to retail traders thanks to modern platforms and data availability. That said, I know it’s not without its challenges — finding genuinely cointegrated pairs, managing execution risk, and dealing with temporary breakdowns in correlation are all issues I’d have to approach with caution. Still, with the right tools and discipline, I think this is a strategy I’d seriously consider exploring further.
 

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