There is a branch of investment theory known as technical analysis that uses historical data, and past patterns and relationships in particular, to inform present and future investment decisions. But unless you're a quantitative hedge fund staffed by physicists and mathematicians, it's more akin to astrology than it is to astronomy.
That being said, in a subject that's so ripe for quantification, one can't help delving into this mock science every now and then, just for curiosity's sake. With this in mind, I culled through the past 21 years of data for the Dow Jones Industrial Average (DJINDICES:^DJI) to see what, if anything, we could glean from the performance of stocks in July.
One pattern that emerges is that the average daily point move of the blue-chip index has increased, albeit haphazardly, over the past two decades. Suffice it to say this shouldn't be overly surprising, given that the Dow itself has climbed by roughly a factor of five over the same time period. The spikes in 2002 and 2008, in turn, correspond to the bursting of the dot-com and housing bubbles.
A second pattern, if one could call it that, is that stocks have tended to end July higher rather than lower. Out of the 21 years examined here, it closed up in 14 of those years and down in only seven. The biggest gain was in 2009, as the market recovered from the financial crisis. And the biggest loss was in 2002, on the heels of the dot-com bubble.
Finally, in terms of the proportional magnitude of increases and decreases, the reality is that there really is no pattern. You can see for yourself in the following chart. Sometimes it's up or down a lot. Other times the variation is considerably smaller.
The bottom line here is that looking at past data can be interesting. And in some cases even lucrative -- that is, if you employ mathematical geniuses who can design computer programs that identify and exploit arbitrage opportunities. For the rest of us, however, it's probably best to leave historical examinations at just that. Any present and/or future investment decisions would be better served by an analysis of the underlying companies themselves.