Boston Snow Indicator

It’s December. As a New Englander, I look forward to the holidays and the cold, snowy weather that comes with them. With all the buzz about Santa Claus and our “Dreaming of a white Christmas”, I started looking into data sets containing historical snow accumulation in the US. The idea was to come up with something like this or, even better, like this, but it seems I was beat to the punch. I decided to limit scope to Boston snow data.

After doing so, the ol’ Boston Snow Indicator popped up in my head. The reason being that, in a previous life, I worked as an investment advisor and heard all sorts of speculative ways to forecast market performance. As a finance professional, I was in a rat race to beat the market in any [legal] way possible because any sort of competitive edge may result in more clients investing with me. More clients means more money, more money means more happiness, more happiness means… you get the point? I was young. By now you may have noticed this profession never panned out for me and I’ve redirected myself to a much less heinous professional track. I digress.

What the Boston Snow Indicator is, according to the definition on, “A market theory that states that a white Christmas in Boston will result in rising stock prices for the following year. For example, in Christmas of 1995, Boston received snow and the following year, the S&P 500 increased by more than 20%.” Essentially, if it snows a bunch in December, especially on Christmas, we’re going to have a bullish year, so dump your money (and your clients money) in the market!

In my search for any sort of data around Boston snow accumulation and market index performance over time, I stumbled upon 2 websites that would give me a good start. I ended up combining the data from the two websites into one file, plugging it into Tableau, my weapon of choice for data visualization.

I have just a few basic components I want to work with, then a couple other parameters and calculated fields I created to make things interesting:
Dimensions: Year
Measures: Dow Jones Industrial Average (DJIA) percent gain or loss, NASDAQ percent gain or loss, S&P percent gain or loss, Snowfall

I first decided to chart out snowfall over time, using the lollipop chart method to spice up the viz with snowflakes. Then I plotted out the indeces over time to compare and see if any trends clearly popped out. Nothing major, so next I decided to create the scatterplot, box plot, and heat map to get a better feel for distribution. I felt like I had a good enough story to tell at that point, so I threw it all on a dashboard and started playing with the custom parameters, custom coloring, and quick filters to come up with the following:

Now, as explains it, “As you may have guessed, there is no logical correlation between whether there is snow in Boston on Christmas and the performance of the stock market. Any incidence of a white Christmas in Boston and bullish market performance in the following year are purely coincidental. This may be why this indicator is also referred to as the “BS indicator”.

I think I accomplished what I set out to do – come up with a cool way to take 2 very boring data sets, blend them together, and visualize them in a way that helps answer a question.

Oh, and the super neat little font I used in the blog title was created using

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