What is the 'Calendar Effect'

The calendar effect is a collection of assorted theories that assert that certain days, months or times of year are subject to above-average price changes in market indexes and can, therefore, signal good or bad times to invest. Some theories that fall under the calendar effect include the Monday effect, the October effect, the Halloween effect and the January effect.

BREAKING DOWN 'Calendar Effect'

Most of the evidence for the calendar effect is anecdotal, although there is a slight statistical case to be made for some of them, which is more than enough to encourage some investors to place their faith in them. Academics and practitioners alike realize most of the observable correlations in calendar effects are spurious in nature — but these interesting paradoxes make for good headlines all the same.

Proponents of the October effect, one of the most popular theories, argue that October is when some of the greatest crashes in stock market history, including 1929's Black Tuesday and Thursday and the 1987 stock market crash, occurred. While statistical evidence doesn't support the phenomenon that stocks trade lower in October, the psychological expectations of the October effect still exist.

Other Examples of the Calendar Effect

Another example for sports fans comes from the Super Bowl. The Super Bowl Indicator is a superstition purporting the stock market's performance in a given year can be predicted based on the outcome of the Super Bowl of that year. Another pseudo-macroeconomic concept, it states that if a team from the American Football Conference (AFC) wins, there will be a bear market (or down market). But if a National Football Conference (NFC) team wins, we'll have a bull market (up market). As of January 2017, the indicator has been correct 40 out of 50 times, as measured by the S&P 500 Index – a success rate of 80%. Not bad, however, since a particular football league winning a Super Bowl and the U.S. Stock market have no real connection, this is just a coincidence or spurious correlation.

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