Visualizing UBS Analysts

Last November, my friend Jesper and I presented at Web 2.0 New York, a talk called Freeing and Visualizing Financial Data. I’ve been meaning to put up some of the visuals we made while looking through what was available.

Banks are required to publish a report each quarter called an “Analyst Transparency Report”. The report lists all the stocks that the bank’s analysts wrote reports about, along with who wrote the report and what their recommendation was. Because the report is just a several-hundred page table, it’s very difficult to get a view of the big picture. It’s even difficult to figure out basic stuff like all the stocks covered by a particular analyst.

To get an idea of what was going on, I took the report and turned it into a large network diagram (longtime readers will start to think that this is my answer to everything )

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(click on the graph to see a high-res version)

The rectangular nodes are analysts and the oval nodes are stocks. The connections between them indicate that the recommendation was buy (green), neutral (black) or sell (red).

You can spend a long time looking at this and noticing patterns that emerge. One of the cool things is that it’s easy to quickly spot companies that appeared together in a report so you can tell that they potentially have related fortunes. Here’s a great example:

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In this image, you can see that this analyst, Kevin Crissey, seems to specialize in the travel industry. He covers, for example both JetBlue (JBLU) and Expedia (EXPE). What’s fantastic about this is that the industry classification for these two companies is completely different. JBLU is classified as a Regional Airline, which EXPE is classified as General Entertainment. Because the clusters in these graphs show relationships between companies that don’t match their hierarchical classifications, they are a great source of information about potential correlations.

Here’s another section of the graph:

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What popped out to me here is that this group of three retail analysts all have recommendations on Zumiez (ZUMZ). Not only that, but two of them have written two reports and change from a neutral to sell rating. This is curious because ZUMZ is only a $600million company, which has three analysts who wrote five reports on it. There are much larger companies which have no coverage or only one analyst. Further investigation might reveal that UBS has a particular reason for all this coverage or that ZUMZ did something to attract a lot of attention.

There are many other interesting groupings and links buried in the graph. I think viewing extremely long tables as a graph like this could help us spot relationships we might not have otherwise seen.