Statistics + Trade

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Visualizing the S&P500

Thanks to Google, we are able to easily visualize data in many interesting ways. We at StatsTrade strongly believe in the power of data visualization (together with dimension reduction techniques, or other mapping methods). And so we wished to take some time and share with you a simple possible visualization.

We took the S&P500 companies, and considered their headquarters information as their “location” information (for example, Google’s headquarters are at Mountain View, California). As for the colors: In the first visualization below, we marked by green the 25% companies (headquarters) with the highest P/E ratio. The 25% with the lowest P/E were marked by red, and the rest (~50%) by yellow.

So there you go. A visualization of the S&P 500 companies headquarters by P/E quartiles, where green = top quartile, red = bottom quartile, yellow = the rest: (click on a point to see its associated data)

And another visualization with respect to YTD (the change in the stock price in 2012, a.k.a. Year-To-Date). Again, green = the top quartile that profited the most in 2012, red = the bottom quartile, and yellow = the rest.

Example observations:

  • Many red dots around Washington. Now, as much as we’d like to argue that Washington-based companies are on the lowest P/E quartile, the truth is that comparing the P/E across companies from various industries doesn’t always make sense. As a matter of fact, the conclusion in this case may be that Washington-based companies tend to be of some very specific industries.
  • In the YTD visualization: Many red dots around San Francisco, in contrast to Los Angeles’ case. It was a better year (so far) for the LA-based companies.
  • Feel free to explore the maps and come up with your own observations! If you came up with something interesting, share it with us and we’ll update this list accordingly!

Disclaimer: The visualizations above are based on data of lesser quality (e.g. Wikipedia) than the one Nate is using, for example… In any case, there may always be inaccuracies, missing values etc., so use it at your own risk.

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