Month-of-year Seasonality

What is Month-of-year Seasonality?

Month-of-year seasonality is the tendency for average returns to differ by calendar month, measured by laying years of monthly returns into a matrix (years down the rows, months across the columns) and summarizing each column with an average and a hit rate. The equity folklore lives here: September's historically weak average in major US indexes, 'sell in May', the Santa Claus rally, the January effect in small caps. Some of these are visible in long samples; all of them are averages with wide dispersion around them.

Its value is as a base rate. A monthly average over twenty years rests on twenty observations, outlier years can dominate a column, and widely publicized effects have historically weakened after publication. That is why seasonality tooling pairs each monthly average with a consistency score, and why the output is a tilt on top of price analysis rather than a standalone signal.

How traders use it

• Tilting expectations: sizing more conservatively or demanding better setups in a symbol's historically weak months, without letting the calendar overrule live price action.
• Building the matrix per instrument: commodities follow harvest and demand calendars, and crypto's four-year halving folklore belongs with long-horizon calendar cycles; equity folklore does not transfer, so each market earns its own return matrix.
• Auditing strategies: if a backtest's edge concentrates in one or two calendar months across the sample, that is concentration risk worth knowing before committing capital.