The Mean Reversion Strategy aND stock pair trading
This article will take a closer look at the mean reversion strategy, different types of mean reversion strategies such as pairs trading or statistical arbitrage, and will explain why those are one of the most commonly used trading strategies in the industry. We will cover stock pair trading and how you can profit from it.
MEAN REVERSION STRATEGY
Mean reversion strategies are one of the most commonly used trading strategies in the industry. Their fundamental assumption is that prices (or their ratio) will revert to the average price over time.
This omnipresent phenomenon is called regression, and it is a natural law you can regularly see in your daily life. In the case of trading, if the market is in a significant and fast downtrend in one day, we could estimate that the market won’t be in such a considerable downtrend tomorrow. In other words, we expect the next day to move close to the average. A popular alternative to just looking at one security is to look at multiple different securities and their correlation. This type of mean reversion strategy is known as pairs trading.
Trading on stock exchanges worldwide is one of the most profitable and “sexiest” enterprises one can come across. In business terms, relatively low capital is needed for a start.
Every professional trader knows that risk management is the foundation of long-term prosperity. Banks and investment funds worldwide pay generously for their teams of experts that has only one task that is probably even more important than returns: to minimize risks, minimize risks, and, once more, to minimize risks.
A stable strategy that makes a few percent per year is incomparably better for a professional than aggressive scalping that can make 100% in one month but can also ruin your account within a few minutes.
How it works?
A myriad of instruments can be traded on exchanges worldwide, from currency pairs to stock, options, to futures contracts. Each instrument has its characteristics, risks, and profit potential. Equities are the most easily understandable for a beginning trader: when I buy stock, I buy a share in the given company. When things go well, the value of the stock goes up. When things turn for the worse, the value of the stock (company) goes down.
There is one well-known mean reversion strategy that takes upsides and downsides moves into account: Stock pair trading – statistical arbitrage.
Banks have looked for reliable and stable exchange trading strategies since the beginning of their existence. Dozens or even hundreds of more or less successful methods have been devised over the years. Some strategies have only worked on a short-term basis; others made money for decades. A strategy that has worked on an extended basis is stock pair trading, also known as statistical arbitrage.
Statistical arbitrage was described in theory in the 1950s by the Australian investor and hedge fund manager Alfred Winslow Jones, but it did not get used in practice until the introduction of powerful computers that made it possible to process enormous volumes of data in real-time and search for profitable equity pairs.
The strategy boom came in the 1980s thanks to Gerry Bamberger, Nunzio Tartaglia, and their expert team from Morgan Stanley. In 1987, Morgan Stanley announced that it made 50 million dollars on Tartaglio’s automated systems. That was an incredible profit for that era. Although the group was dissolved in 1989, stock pairs have remained one of the bank’s strategy pillars. Over time, as details of the strategy reached the general public, stock pairs became one of the most popular neutral strategies. Today, they constitute the foundation of trading portfolios of professional traders, banks, and funds worldwide.
The principle of stock pair trading
The concept of pair trading is surprisingly simple: we find two shares whose price ratio has been stable on a sustained basis and speculate whether it will continue to remain stable. In other words: if the price ratio (stable in the long-term) sways outside of the usual range, we speculate on it returning to normal after some time.
Stock pair trading is known as the stock spread, or as the statistical arbitrage – this is because this kind of trading is based on statistical analysis. Stock pair trading is one of the investing strategies used by professional traders, banks, and investment funds. Stock pair trading is a strategy based on trading two different stocks at the same time. The goal is to profit from the price difference between those two stocks. Two stocks are being traded during the stock pair trading. Both stocks have very similar behavior (they are highly correlated).
Let’s take a look at an example:
We have two companies on US stock exchange (Figure 1): SMLP Summit Midstream Partners LP (NYSE); ENLC EnLink Midstream LLC (NYSE). Both stocks come from the same sector (Natural GasDistribution). One stock is bought (Long position), and the second one is sold (Short position). Stock pair trading is based on the price difference between two correlated stocks. We can speculate on the price convergence of correlated stocks or their divergence. If we can find a stock pair whose price ratio is stable in the long-term, we can speculate (with a high probability of success) on returning those prices to normal.
Price chart of highly correlated stocks
When trading stock pairs, we do not speculate on the fluctuation in the price of one stock, but on the relative fluctuation of the prices of two stocks concerning one another – the return of a short-term sway in their price ratio back to the long-term normal.
HOW IT WORKS IN PRACTICE?
In practice, a pair transaction works as follows: at one time, we buy the undervalued stock title and sell the overvalued. If the assumption of the reunification of their prices is confirmed, we collect the profit. It is irrelevant whether the profit is generated by the growth of the undervalued stock title, a drop in the overvalued one, or any other combination of price fluctuations. What matters is that we collect our profit when the difference in prices is reduced.
For greater clarity, we show a figure that demonstrates the principle of trading on the difference in stock prices in simplified terms. The difference in stock prices, in this case, is expressed in simplified terms as a simple difference in the prices of stock the titles used (there are several ways of expressing that difference).
Again, we are using SMLP and ENLC stock for our example:
The principle of pair trading
Point 1: The difference between stock prices is very small
Point 2: The difference between prices is unusually high – you can expect a return to the long-term average
Point 3: The prices became closer again
Pair trading is simple in principle, but it requires very high gross calculation capacity. At a same time, one must monitor dozens or hundreds of stock pairs, assess their trading model, and execute the relevant orders precisely and flawlessly in the event of a signal for entry/exit.
The edge of stock pairs
Tree pillars can make the edge:
- limited risk of loss,
- speculation on the return of the ratio of prices to its long-time average, without the influence of external factors,
- broad portfolio diversification thanks to the many trading opportunities in the stock pairs universe.
No trading strategy generates profits solely. Loss is an integral part of stock trading, and stock pairs are no exception. A loss occurs when the difference between prices does not revert to normal within the set time. Rules for leaving a position must be defined to deal with this adverse development. Simultaneously, a very effective method is a time stop-loss – closing a position no later than at the end of a day defined in advance.
Based on an analysis of an extensive set of data, it is possible to state that stock pairs manifest a stable winning ratio of around 65%.
By nature, a stock pair is hedged against market fluctuations. It is a market-neutral strategy.
If we combine several stock pairs in a portfolio, the total number of transactions will increase, and the equity could become significantly smoother because of trading many uncorrelated pairs. Of course, this approach needs complete automation, as it is very time demanding.