In the 1880s Galton studied the heights of parents and their children. He found that exceptionally tall parents tended to have children who were tall but less extreme — they 'regressed' toward the average. This was not a biological force but a statistical phenomenon: extreme observations are partly due to chance, and chance does not repeat. Galton called this 'regression toward mediocrity.'
Galton's insight led him to develop the concept of the regression line — a straight line that best summarises the relationship between two variables. He introduced the correlation coefficient (later formalised by Pearson) to measure the strength of linear association. These tools remain the workhorses of empirical research in every quantitative field.
Regression to the mean is the statistical foundation of mean-reversion trading strategies. Companies with extreme valuations, extreme performance, or extreme sentiment tend to revert toward their historical baseline. Understanding when this reversion is statistical (tradeable) versus fundamental (not tradeable) is the core challenge of quantitative value and statistical arbitrage strategies.
Mean-reversion signals are a core component of our statistical arbitrage and quantitative value strategies. Galton's discovery tells us that extreme readings are partly noise — and that systematic exploitation of this reversion is mathematically grounded, not speculative.