Regression toward the mean simply says that, following an extreme random event, the next random event is likely to be less extreme. In no sense does the future event ‘compensate for’ or ‘even out’ the previous event, though this is assumed in the gambler’s fallacy. Regression toward the mean was first described by Victorian polymath Francis Galton. He found that offspring of tall parents tended to be shorter. Also, offspring of shorter parents tended to be taller. Galton stated that processes that did not follow regression towards the mean would quickly go out of control. In finance, the term ‘mean reversion’ has a different meaning. Jeremy Siegel at Wharton uses it to describe a financial time series in which ‘returns can be very unstable in the short run but very stable in the long run,’ in seasonal businesses for example.
The effect can be exploited for general inference and estimation: the hottest place in the country today is more likely to be cooler tomorrow than hotter, as compared to today; the best performing mutual fund over the last three years is more likely to see relative performance decline than improve over the next three years; the most successful Hollywood actor of this year is likely to have less gross than more gross for his or her next movie; the baseball player with the greatest batting average by the All-Star break is more likely to have a lower average than a higher average over the second half of the season, etc.
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The Daily Omnivore 