Regression to the mean
Extreme results tend to be followed by less extreme ones. It looks like a force pulling things back; it is arithmetic. Watch the same idea turn into trading signals, portfolio rules and model penalties.
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Pattern Essays
Regression to the Mean
The core idea: an extreme outcome is partly luck, and luck does not repeat on demand — so the next result lands closer to the middle.
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Market Psychology
Mean Reversion Psychology
Traders feel it as "what goes up must come down" — and easily mistake arithmetic for a promise.
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Technical Indicators
Relative Strength Index
RSI turns the idea into a signal: overbought and oversold readings are bets on a move back toward the middle.
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Risk & Portfolio
Rebalancing Strategies
Rebalancing leans on it by design, trimming what ran ahead and topping up what fell behind.
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Timeseries Engineering
Stationarity
It only works if there is a stable mean to return to. Stationarity is the check that one exists.
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ML Math
Regularization
Models get the same pull on purpose: regularization shrinks extreme weights toward zero so noise can't stretch them.
Regression to the mean is a property of noisy measurement, not a law of markets. Where the mean itself moves, betting on the return is betting on the wrong thing.
Add up enough small, independent nudges and the same curve appears — in sample averages, in price bands, in risk models. Follow it across four fields, then see where it quietly stops being true.
Every field has the same problem: real structure mixed with randomness. These stops are the tools for pulling them apart — and for checking you have not invented a pattern that was never there.
When a system's output feeds back into its input, small nudges can snowball. Crowds, prices, volatility and even deployed models run on loops like this — and they tend to stay quiet until they don't.
The data you can see is often the data that survived. Patterns found in it can be an artefact of what went missing. These stops show how the trap works, and the habits that keep you out of it.
Recent direction tends to carry on, for a while. The same idea speeds up neural-network training, drives a family of trading indicators, shapes how traders add to winners and fuels the crowd. Follow it, and see where it stops paying.
Many systems change all at once when a line is crossed. A neuron fires, a classifier says yes, a language model drops its unlikely words, a price breaks out, a stop-loss sells. Each stop is a threshold, and each has to answer the same question: where to draw the line.