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January effect

The historical tendency for smaller companies to outperform in January, traditionally attributed to tax-loss selling reversing.

The original explanation was mechanical: investors sold losing positions in December to realise tax losses, depressing small-cap prices, then bought back in January. That is a genuine mechanism with a plausible effect on prices.

The effect has weakened substantially since it was documented in the 1970s and 1980s. That is the expected fate of a published anomaly with a known cause: participants front-run it until the edge is gone.

It remains a useful case study in how anomalies behave. A pattern with a real mechanism can still disappear once enough capital knows about it, which is a reason to be sceptical of any widely publicised seasonal edge.

Related: seasonality, sell-in-may, turn-of-the-month-effect, efficient-market-hypothesis, overfitting

See it drawn

Original diagrams for the ideas on this page. Illustrative, not real market data.

The spread of outcomes behind an expectancyA histogram of forty trades: a tall block of small losses on the left, a low spread of larger wins on the right, and a line marking the average outcome.NUMBER OF TRADES051024 LOSSES, AVG −$20016 WINS, AVG +$600EXPECTANCY +$120−$400−$200$0+$200+$400+$600+$800PROFIT OR LOSS PER TRADEexpectancy = (40% × $600) − (60% × $200) = +$120 per trade
Expectancy: the average trade. Forty trades sorted by outcome: 24 small losses and 16 larger wins. Weighting each side by how often it happens gives the average result per trade, marked here by the dashed line at +$120.

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