What Is Mean Reversion?

What Is Mean Reversion?

Let me explain mean reversion to you directly: it's a financial theory that asset prices will eventually return to their historical average, and I use it to guide my trading and investment decisions based on significant deviations from this mean.

You see this concept applied in various financial data like prices, earnings, and book values. If an asset's current price is below its past average, I consider it a buy. If it's above, I expect it to drop. That's how I time my strategies.

Key Takeaways

Mean reversion in finance means phenomena like asset prices and return volatility revert to long-term averages. This theory drives strategies from stock trading to options pricing. In mean reversion trading, I capitalize on extreme price changes, assuming they'll revert. Tools I rely on include moving averages, RSI, Bollinger Bands, and the stochastic oscillator.

Understanding Mean Reversion

Mean reversion suggests asset prices return to their long-term mean. I base this on the idea that prices and returns gravitate toward averages over time. The bigger the deviation, the more likely the price moves back.

This leads to strategies where I buy or sell securities that have strayed from historical averages. But remember, a change might signal altered prospects for a company, reducing reversion chances. It's not just prices—interest rates or P/E ratios can revert too.

Use by Traders

I employ mean reversion to profit from prices far from historical means, assuming they'll revert. Here's how I do it: I use statistical analysis like Z-scores to gauge deviations—a score above 1.5 or below -1.5 signals opportunities.

In pairs trading, I spot correlated assets and trade when their ratio deviates: long the undervalued, short the overvalued. For volatility, I buy options when it's high, expecting reversion. I set stop-loss and take-profit around the mean for risk management. In algorithmic trading, I build models to predict movements.

Consider time horizon and market conditions—short-term uses intraday data, long-term uses yearly. It works best in range-bound markets, not trending ones.

Calculating Mean Reversion

To calculate mean reversion, I gather historical price data and compute the average: Mean = Sum of prices / Number of observations.

Then, deviation for each point: Deviation = Price - Mean. Next, standard deviation: Square root of (Sum of squared deviations / (Observations - 1)).

Finally, Z-score: Deviation / Standard deviation. A high positive Z-score means overvalued, low negative means undervalued—thresholds like 1.5 or 2 guide me.

Mean Reversion and Technical Analysis

Mean reversion underpins technical analysis for spotting overbought or oversold conditions. I use moving averages to find mean prices—above signals overbought, below oversold.

Bollinger Bands have a middle average with deviation bands; prices revert to the middle. RSI from 0-100 shows overbought above 70, oversold below 30. Stochastic oscillator flags overbought above 80, oversold below 20. MACD signals deviations via crossovers.

Day Trading and Mean Reversion

In day trading, I buy and sell within the day, using mean reversion for short-term fluctuations. I apply intraday moving averages for mean prices and expect reversions on deviations.

RSI and stochastic help spot overbought/oversold intraday. Bollinger Band squeezes indicate potential moves back to the mean. I even use algorithms for high-frequency mean reversion trades.

Swing Trading and Mean Reversion

Swing trading holds positions days to weeks, and I use mean reversion for reversals. I prefer longer moving averages than in day trading; crossovers signal potential reversals.

RSI and MACD identify conditions for reversion. Fibonacci retracements at 38.2%, 50%, 61.8% show reversion levels. Candlestick patterns like doji or engulfing confirm opportunities.

Forex Trading Using Mean Reversion

In forex, I target currency pairs reverting to historical means. Moving averages identify average rates; deviations prompt trades.

RSI and stochastic signal overbought/oversold. Pivot points based on prior highs/lows/closes mark support/resistance for reversions. I trade divergences in correlated pairs: long underperformer, short outperformer.

A Hypothetical Example of Mean Reversion

Take Company XYZ stock with a 200-day average of $50. After earnings, it jumps to $70. Standard deviation is $5, so Z-score is (70-50)/5 = 4, indicating overvalued. I short it, and it falls back to $52.

Benefits and Limitations of Mean Reversion

Mean reversion gives a structured approach, versatile across assets and times, with risk management via stops around the mean. It's profitable in range-bound markets and reliable with multiple indicators.

But it's weak in trending markets, incurs high transaction costs from frequent trades, prone to false signals in noise, disrupted by events, and non-directional unlike trend-following.

What Is a Mean Reversion Strategy?

It's a trading approach where I bet on assets reverting to historical means by identifying over/undervalued ones.

What Is the Best Time Frame for Mean Reversion?

It depends on your objectives, risk, and asset—varies from intraday to long-term.

What Is the Best Asset To Trade Using Mean Reversion?

Depends on conditions, expertise, risk—stocks, forex, commodities, ETFs, fixed income work well.

What Is the Difference Between Trend-Following and Mean Reversion?

Trend-following profits from directional moves; mean reversion from deviations back to averages.

The Bottom Line

Mean reversion says prices revert to historical means, backing strategies across assets. I use indicators like moving averages, RSI, Bollinger Bands for opportunities. It's great in sideways markets but needs risk management due to costs and frequency.