Given this, grasping what mean reversion is will be a prelude and key to understanding reversion to mean trading. At the heart of mean reversion is the assumption that, over the long term, be it shorter- or longer-term, asset prices and historical returns eventually revert back to their averages or means, given the market forces in play, such as investor behaviour, the economic cycle and fundamental valuations.
It’s saying that, when prices stray a long way from their average – that is, the midpoint between their highest and lowest values over many years – then an investor should expect a return to that average. This is because the differences will be rectified – the price of a rocket ship that’s trading way above its historical average will come down, those that are trading way below their historical average will go up. After all, if you’re buying a share at a price way above the historical average, it means that its high price will only be sustainable if lots of buyers, in turn, also want to pay that price. And you can bet, as soon as they think the price is too high, a sell order will start ticking its way up the ladder. Conversely, a share price trading way below its historical average will almost certainly attract buyers wanting value. They will buy it, pushing the price up.
After all, knowing about mean reversion can help traders bet on when a price will correct, and alert them when it might be time to make a change.
It has deep roots in the early development of finance and statistics. Charles Dow, the founder of Dow Jones, for example, would have been familiar with the basic rules of correlation and regression towards a mean, as these ideas had been developed half a century earlier by statisticians such as Karl Pearson. In the financial era that followed, the development of the Efficient Market Hypothesis by economists such as Eugene Fama in the 1960s suggested that finance prices included all the information available about an asset.
In the late 20th century, institutions began to discern rhythmic patterns by which assets would move between an average price level and an outlier. The rise of automated quantitative trading strategies in the 1980s made mean reversion-style approaches particularly popular as computers finally made it easy to analyse price history. Some of the most basic quantitative trading strategies today rely on mean reversion, as they aim to take advantage of ‘blips’ within otherwise steady trends.
Successfully spotting mean reversion trades will depend on recognising a set of important metrics that signal possible price corrections to historical averages. The level of volatility plays a critical role – assets that have been trading at extreme levels often return to the mean once the volatility subsides. Traders also need to look at momentum indicators such as the relative strength index (RSI) that can warn of overbought or oversold conditions. If the RSI is above 70, for example, it can be an indication that overbought assets will reverse. If the RSI of a security is below 30, it might imply that price might bounce for the oversold asset.
Second, mean reversion can be measured using moving averages. Asset prices that are too far away from their moving average (and especially the longer-term ones) have a greater chance of reverting to that moving average. Third, traders can examine the old price time series, looking for patterns when it would be helpful to see whether assets that have often reverted to the mean are more reliable when it comes to receiving good and timely trading signals. Using these indicators, traders can arrive at more accurate mean reversion situations in the market to execute strategies based on them.
Successful application of mean reversion trading strategy necessitates a delicate blend of technical expertise and risk-management discipline. Most commonly, traders begin with the identification of an asset whose price cycles around a mean (long-term average). They might start by examining a price chart, along with statistical tools such as standard deviation and Bollinger Bands, to ascertain whether an asset is overbought or oversold.
Once these potential trades have been identified, the next step would be to define entry and exit levels by reference to historical price data. For example, we might look to enter long positions (going long) when prices have fallen below a particular level relative to their moving averages, and conversely short them (going short) according to the same set of rules. We might also want to place ‘stop-loss’ orders – to cut losses if the market moves unexpectedly against us.
You also need to monitor the bigger picture – economic indicators can affect asset prices and potentially harm strategies that rely on mean reversion, for example. Integrating technical analysis with good risk management maximises the odds that a trader can make money from these temporary price anomalies while protecting their capital.
Although the theoretical basis of reversion-to-mean trading is strong, there are a number of risks and challenges that needs to be taken or addressed when implementing such a strategy. The most obvious issue seems to be the logic of reversion to a mean – namely, prices will return to the historical average. Market conditions can change fairly dramatically over time as a result of economic developments, new regulations or unexpected geopolitical events. All of which can produce protracted trends that are markedly different from the historical average. Going against these prevailing winds can be detrimental to trading such strategies.
And then there is the issue of timing. If you don’t know exactly when a security is mean-reverting and when it’s not, and you’re wrong once or twice when you decide to trade (ie, you enter a long position when in fact prices move against you), you have the prospect of losing large sums.
Second, your technical indicators give you false signals, especially if the market is volatile (as for example in May) making the direction of price changes difficult to interpret. Third, long-term clients may shake off your discipline, for example by ringing your neck when it hurts (a drawdown). In short, traders, beware.
Luckily, there’s missing the point The best real-world examples of mean reversion trading come from the stock of XYZ Corporation, which took a beating when it missed earnings expectations by a country mile. Traders who looked at its history of price moves observed that similar drops had historically been followed by recoveries back to the average price over the space of weeks. If they entered positions right after such sell-offs, sentiment had clearly turned, and their bets on the recovery played out for them.
Another good example was a currency pair, EUR/USD, which investors buying Euro soared against the dollar, driven in part by geopolitical factors. Experienced traders notice the price gap from the 200-day moving average, and they went long as soon as tensions subsided. A few days later, with prices correlating back to the mean and volatility decreasing, they managed to book their profits.
These examples further show how, by understanding historical price behaviour and market psychology, traders could employ mean reversion strategies that create profitable opportunities for benefiting off temporary mispricings while trading risk through smart entries and exits.