Mean reversion in trading theorizes that prices tend to return to average levels, and extreme price moves are hard to sustain for extended periods. Traders who partake in mean reversion trading have developed many methods for capitalizing on the theory. In all cases, they are betting that an extreme level whether it be volatility, price, growth, or a technical indicator will return to the average.

Example of mean reversion

While an instrument’s price tends to revert to the average over time, this does not always mean that the price will drop back to the mean, or that the price will rise to the mean. The mean is also moving, so if the price stalls and doesn’t move much, the mean price has time to catch up. That too counts as mean reversion.

Mean Reversion Strategies

Mean reversion strategies attempt to capture profits as the price of an asset returns to more normal levels or the average. When considering using a mean reversion strategy in your trading, remember that a price rising away from the mean doesn’t necessarily indicate that the price will fall. The mean could also simply move up to meet the price. That would also constitute reversion to the mean because the price is back in line with its average. While reversion to the mean occurs regularly, prices rarely stay exactly at the mean for long. Below are some popular examples of mean reversion strategies.

Mean reversion in pairs trading

Pairs trading​ involves finding two highly correlated assets. The prices of these assets tend to move together. When the prices deviate from one another; for example, one drops when the other doesn’t; this creates a potential mean reversion trade. This is also sometimes referred to as statistical arbitrage​.

A trader could buy the underperforming pair and short the stronger pair. This creates the opportunity to profit if the pairs start to align again. A pair’s trade requires buying and selling at the same time. It does not matter to a pairs trader which asset rises or falls; they are simply betting that the two prices will converge back to each other and start moving in alignment again.

Advantages and key points are

• Mean reversion in finance suggests that various relevant phenomena such as asset prices and volatility of returns eventually revert to their long-term average levels.
• The mean reversion theory has led to many investment strategies, from stock trading techniques to options pricing models.
• Mean reversion trading tries to capitalize on extreme changes in the price of a particular security, assuming that it will revert to its previous state.
• Some technical analysis means reversion tools include its moving averages, the relative strength index (RSI), Bollinger bands, and the stochastic oscillator that helps in trading as well.

Mean reversion is a financial theory that suggests asset prices will eventually return to their long-term mean or average strategy level. This concept is grounded in the belief that asset prices and historical returns will gravitate toward a long-term average over time through some time limits and different criteria as well. The greater the deviation from this means, the higher the probability that the asset’s price will move closer to it in the future which helps as well.

How can we know and understand if a market suits a mean reversion trading strategy?

Technical analysis

To identify markets suitable for a mean reversion strategy, traders can observe the behavior of Simple Moving Averages (SMA) with periods of 20 and 30. When both SMAs move sideways and roughly in the middle of the recent price range, it indicates a ranging market. Traders recognize this as an opportunity for mean reversion strategies. They can buy near the lower end and sell near the upper end of the range, anticipating price reversals to the mean. However, caution is essential as ranging markets can transition into trends, requiring strategy adjustments as market conditions evolve.

Statistical analysis

A trader can follow a concise process to determine if a market is statistically prone to mean reversion behavior and suitable for a mean reversion trading strategy. First, apply the Augmented Dickey-Fuller (ADF) test to the market’s price or asset data.

Focus on the test statistic and p-value: a highly negative test statistic and a low p-value suggest stronger mean reversion tendencies. You can use for instance Chat GPT 4 to perform this test provided you have the price data for the market you are looking to analyze.

Conclusion

To help you on your journey toward crafting your own mean reversion trading strategy, this article combines statistical theory with practical trading tools like Bollinger Bands. Traders must understand that success in this endeavor goes beyond mere theory; it requires a holistic approach that includes market selection, technical and statistical tools, as well as disciplined risk management, and psychological control. Remember that a successful mean reversion trading strategy goes beyond the ‘set-and-forget’ approach. It requires continuous adaptation and a profound understanding of market intricacies.