2016's week 1 return among the 50 worst since 1950

The following plot shows the 50 worst weekly returns of the S&P 500 since 1950 (data from Yahoo! Finance):

The first week of 2016, highlighted in the upper right, is bad -- but not the worst. The worst is the 18.2 percent loss for the week ending October 10. If you recall that amusing period, within a week, Warren Buffett wrote "Buy American, I Am," http://www.nytimes.com/2008/10/17/opinion/17buffett.html, where he advocated being greedy during periods when others are fearful:

"Be fearful when others are greedy, and be greedy when others are fearful. And most certainly, fear is now widespread, gripping even seasoned investors."

Fear is not quite as widespread now as it was then but this thumping has likely taken quite a few investors back to the days of Fall 2008's halcyon panic. Undoubtedly, the market looks awful now -- and the carnage has started to become indiscriminate. A number of decent stocks have been hit, based entirely (most likely) on margin calls, stocks that will nevertheless report excellent numbers in the quarters ahead.

This table shows that the first week of 2016 -- with a weekly return of -5.96% -- is 32nd on the list of worst weekly returns since 1950:

Based on the full set of data, the chance of getting such a bad week (a weekly return of roughly -5% or worse) is roughly 15 out of 1,000 or roughly 1 out of 70 or roughly one week every 16 months. The market did not encounter any such weeks during 2012, 2013, and 2014. In 2015, it encountered one - the week ending 08/21/2015. That week, which saw a loss of -5.77%, is 36th on the list.

Perhaps we are due. Buckle up? Typically, these things do tend to occur in clusters:

This pattern shows clustering -- a tendency for movements to occur close to each other. Certainly, the movements are not random, in the sense that they occur somewhat regularly but without any discerning pattern. This is very different from movements about the mean or 0 -- which do show randomness. For instance, in an earlier post, we saw that randomness is characteristic of monthly returns about 0. Sharp falls have a separate nature unto themselves. They occur relatively rarely and they tend to cluster. Of course, predicting the first is next to impossible. Once you have the first, however, you can be reasonably sure of one or two others upcoming. Maybe.

In my book, Investing in Dividend Growth Stocks, I emphasize stability of returns for long-term investors precisely because I am not a fan of extreme movements for the typical long-term holder of any stock (you have no choice if you hold the market, which I am also not a huge fan of, but that story is for another day). Moreover, it is not difficult to show that if you have stability in your returns you tend to have a higher compounded return (read: wealth), all else equal. When you invest in unstable stocks and many markets you have to be certain not to get continually hammered as your long-term returns will invariably suffer. With generally stable markets such as the U.S. market, this "suffering" is not nearly as great as it is with many other markets. Essentially, this has to do with excess volatility acting like a leech, bleeding off your compounded return, hence wealth. From my book:

"If you had to choose between (a) two years of returns at 10 percent a year and (b) a first-year return of 0 percent a year followed by a second-year return of 20 percent a year, which would you choose? Although the average of both choices is 10 percent a year (the average of two tens is 10 and the average of 0 and 20 is 10), the stable growth of the first choice results in more wealth. With the first choice, for every $100 that you invest, you end up with $121; with the second choice, for every $100 that you invest, you end up with $120. Yes, all of $1 less; but what counts is the general principle: For a given average return, where the average is computed simply as the sum of the annual returns divided by the number of years, stable growth is always more valuable than fluctuating growth."

For more, see pages 76-81 of my book. If you do not have the book, you may be able to view the pages on Amazon if you use Amazon's look-inside feature for the book, here.