What is Mean Reversion Trading?

Learn about mean reversion trading, including how it capitalizes on extreme price movements, its pros and cons, and its formula and strategies.

What goes up must come down. This simple physics observation also applies to financial markets. As asset prices rise, shareholders sell shares and take profits, which exerts downward pressure on prices. The same process occurs in reverse, as underpriced assets attract buyers, pushing prices up. This tendency for prices to revert to historical averages––called mean reversion––attracts significant interest from traders.

Traders use mean reversion trading strategies to capitalize on market fluctuations. Let’s explore mean reversion, including its use in trading, formula, and popular strategies. We’ll also discuss the pros and cons of mean reversion trading and how you can use Composer’s automated trading systems to create a mean reversion strategy.

What is mean reversion?

Mean reversion is a financial theory that suggests asset prices usually return to their long-term average level. This concept applies to other financial data, such as earnings, book value, and volatility. According to mean reversion theory, extreme price movements can’t continue for extended periods and will eventually revert to the mean or average level given enough time.

The more a mean reversion indicator deviates from its historical average, the greater the probability that future movements will track toward the mean. This theory views extreme fluctuations as outliers rather than the new normal, believing that price, volatility, and earnings data tend to follow historical performance patterns.

How is mean reversion used in trading?

Investors use mean reversion trading to profit on price movements that diverge from the historical average. They build strategies based on a movement’s strength and direction, developing market entry and exit points with the most significant profit potential.

Popular mean reversion trading use cases include:

Technical analysis: Investors use mean reversion indicators to evaluate price movements for potential pullbacks, reversals, and retracements. Common mean reversion indicators include the relative strength index (RSI), moving averages, Bollinger Bands, and price oscillators.
Risk management: During highly volatile markets, traders use mean reversion strategies to mitigate losses. Standard practice includes setting stop-loss orders around the mean and take-profit points when an asset is above its historical average.
Algorithmic trading: Mean reversion strategies lend themselves well to automated trading. Algo traders can design automated strategies that execute orders quickly when an asset deviates from its mean price, meeting certain market conditions and ensuring traders don’t miss out on potentially profitable trades.

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Mean reversion formula

Measuring mean reversion requires calculating an asset’s Z-score. In quantitative trading, the Z-score demonstrates how many standard deviations an asset’s price is away from the historical mean.

First, gather an asset’s historical price data and calculate the mean price during the observed time frame. Calculate the mean by summing all the price points in the time frame and dividing by the number of observed prices.

Mean = Sum of all prices / Number of observed prices

Next, calculate the deviation from the mean for each price point in the dataset.

Deviation = Price - mean

Finally, calculate the standard deviation by taking the square root of the sum of all squared deviations divided by the number of observed prices minus one. Divide the standard deviation from a price point to determine the asset’s Z-score.

Standard deviation = Square root [sum of squared deviations / (number of observations - 1)]

The higher the Z-score, the greater the deviation from the mean. A positive Z-score indicates an asset is overvalued relative to the mean, while a negative Z-score indicates an asset is undervalued. Mean reversion trading strategies use this information to establish entry and exit points, buying when an asset is undervalued and selling when it is overvalued.

Mean reversion strategies

Traders employ various strategies using mean reversion principles, often combining several complementary technical indicators to create the best algorithmic trading strategies. Popular mean reversion strategies include:

Moving average convergence divergence (MACD)

MACD is a trend-following indicator that describes the relationship between an asset’s momentum, direction, and duration. It’s calculated by subtracting the 26-period exponential moving average (EMA) from the 12-period EMA, while a 9-day EMA serves as the signal line for buy or sell orders.

The MACD crossing above the signal line indicates a bullish trend. But if the MACD crosses below the signal line, it indicates a bearish trend.

Bollinger Bands

Bollinger Bands plot two standard deviations––one positive and one negative––above and below a simple moving average (typically 20 days). Traders use Bollinger Bands to compute overbought and oversold positions and estimate mean reversion potential. If the price repeatedly touches the upper band, it can signal an overbought position, while the price continually touching the lower band may indicate it’s oversold.

RSI

The RSI indicator measures the strength and speed of an asset’s recent price changes using a scale of 0 to 100. An RSI score above 70 traditionally indicates an overbought asset, while a score below 30 indicates an oversold one. Traders use this information to detect potential price reversals toward the historical average.

Price oscillators

The stochastic oscillator and Williams %R are popular methods for evaluating mean reversion opportunities.

The stochastic oscillator compares an asset’s closing price to a price range over a specific period. It grades recent prices on a scale of 0–100, with measurements below 20 indicating a position is oversold and measurements above 80 indicating it’s overbought.

Like the stochastic oscillator, Williams %R measures an asset’s closing price to a specific high-low price range––usually 14 days. Williams %R differs from the stochastic oscillator using a -100–0 scale, with -20 suggesting an overbought position and -80 suggesting oversold.

Pairs trading

Pairs trading involves matching two historically correlated assets. When the assets deviate from one another, traders go long on the underperforming asset and short on the overperforming asset. This strategy assumes the assets will revert to the historical mean over time due to their high correlation, securing a profit on the long and short positions.

Analyzing profitability: Pros and cons of mean reversion

Traders employing mean reversion strategies believe that extreme deviations from the historical average present opportunities for profit. However, like any trading strategy, mean reversion comes with its own set of advantages and challenges. Here are a few pros and cons to consider:

Pros of mean reversion

Low risk exposure: Mean reversion analysis limits investment risk by providing clear entry and exit points. Many investors set stop-loss orders around the mean to limit losses and use the mean price when calculating take-profit points.
Potential profit from price movements: As a short-term strategy, mean reversion trading focuses on exploiting intraday price movements for small profits. This approach capitalizes on the inclination for prices to hover around a historical average, presenting numerous opportunities for traders to take gains and compound their earnings.
Identifies overbought/oversold conditions: Mean reversion indicators can identify overbought and overbought conditions. Whether trading an ETF index, stocks, or forex, this information helps you plan your entry and exit points.

Cons of mean reversion

Inaccurate timing predictions: Even when applied correctly, mean reversion indicators can provide false signals. They function less effectively during shorter time frames, while black swan events and unanticipated market news can skew mean reversion predictions.
Vulnerable in prolonged trends: Historically, mean reversion strategies perform better in range-bound markets than trending ones, as trending market price deviations may not regress to the mean for protracted periods. Although mean reversion strategies require volatility, highly volatile market conditions can limit their success.
Monetary and time constraints: Mean reversion strategies leverage high-frequency trading, which can incur significant transaction costs. These strategies require consistent monitoring and adjustment, making them extremely time-consuming.

Trade on mean reversion with Composer

Composer’s algo trading tools simplify mean reversion trading. Design a mean reversion strategy that buys assets valued below their long-term average and sells when they exceed their historical average. Use our no-code editor to design your strategy, or leverage our AI-powered assistant to craft a mean reversion strategy.