What is the 'September Effect'

The September Effect refers to the historical weakness in stock market returns for the month of September. There is a statistical case for the September Effect depending on the time period used, but much of the theory is anecdotal. It is generally believed that investors come back from summer vacation in September ready to lock in gains as well as tax losses before the end of the year. There is also a complimentary belief that individual investors liquidate stocks going into September in order to offset schooling costs for children. Like many other calendar effects, the September Effect is generally thought of as a historical quirk in the data rather than any causal relationship.  

BREAKING DOWN 'September Effect'

The September Effect is real in the sense that analyzing the market data – most often the DJIA - shows that September is the only calendar month with a negative return over the last 100 plus years. That said, the effect is not overwhelming and, more importantly, is not predictive in any useful sense. If you bet against September over the last 100 years, you would have made an overall profit. If you made that bet only in 2014, you lost money.    

The October Effect and September Effect

Like the October Effect before it, the September Effect is a market anomaly rather than causal relationship. In fact, October’s 100 year data set is positive despite being the month of the 1907 Panic, Black Tuesday, Thursday and Monday in 1929, and Black Monday in 1987. September saw as much market turmoil as October, hosting the original Black Friday in 1869 and two huge single day dips in the DJIA in 2001 after 9/11 and in 2008 as the Subprime Crisis really ramped up. So September has been historically unlucky in having black swan events happen during its run.    

The Dangers of Statistics

The September Effect and the attention it generates every fall is a useful reminder that statistical analysis can be a dangerous thing. This point was brought home best by David Leinweber, who showed that 99% of the annual movements in the S&P 500 from 1983 to 1993 were explained by overfitting a combination of U.S. cheese production, butter production in Bangladesh and the sheep population in both countries. So forget watching the calendar and start watching the butter production numbers from Bangladesh. Or maybe just focus on the quality of the stocks, not the time of year.

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