The stock market is on an absolute hot streak right now.
Not only is it sitting at an all-time high, it also hasn’t seen a notable pullback in at least 179 days.
Furthermore, it’s closed positively for 18 straight Tuesdays.
However, Art Cashin warns that we should be careful about patterns we may or may not see in the markets.
Cashin, a member of Mensa and UBS’s man on the New York Stock Exchange floor, shares a tale of how correlation was once confused for causality.
“As the debate about what may, or may not, have caused the highly unusual string of eighteen straight “up Tuesdays”, it is worth reviewing the human affinity for seeking and finding patterns around us,” he writes.
From this morning’s Cashin’s Comment:
Sometimes our search for patterns and causes can lead us astray, occasionally with a humorous result. Here’s a tale of such a search from the London Economist back in early 2004:
To be fair, statistics can be deceptive, especially when explaining human behaviour, which is necessarily complicated, and to which iron laws do not apply. Moreover, even if a relationship exists, the wrong conclusions can be drawn. In medieval Holland, it was noted that there was a correlation between the number of storks living on the roof of a house and number of children born within it. The relationship was so striking that, according to the rules of maths that govern such things, you could say with great confidence that the results were very unlikely to be merely random. Such a relationship is said to be “statistically significant”. But the Dutch folklore of the time that storks somehow increased human fertility was clearly wrong.
Intriguingly, although the stork barometer of babydom was dismissed hundreds of years ago, the stork association with new babies lives on. Error dies hard, especially in the stock market.
We hope to return to man’s innate search for patterns and potential causality – as well as the Tuesday phenomenon – in coming days.
Cashin notes that we’re hardwired to recognised patterns. However, he can’t reiterate enough that we should be careful not to mistake correlation for causation.