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Visualizing the World of Mutual Funds

March 21, 2010

I had the opportunity to work on some non-music related projects recently, including a project that involves analyzing mutual funds.

Investing money in the stock market is pretty easy. However, making good investments takes considerably more skill, especially in the current economy.

Mutual funds are one way of managing the complexity of the market. Rather than choosing a number of stocks, and then determining how many shares to buy of each, an individual can purchase shares of a mutual fund.  While mutual funds sell shares like other stocks, they don’t actually produce goods and services like a conventional publicly traded company would.  Rather, they take the money they receive from the sales of their own shares, and then go and buy other stock with it.  Typically mutual funds focus on investments that have certain features, such as stocks that have a large market capital, or stocks that are poised to grow significantly in the next few years.  In this sense, they seek to emulate certain basic investment styles that many investors want to follow.  In some sense, mutual funds can be thought of as “savvy” investors. Understanding relationships that they share could go a long way to understanding how the investment community understands opportunities in the stock market.

The availability of mutual funds still presents a good deal of complexity for the average investor. There could potentially be dozens of funds that might match their chosen needs.   Are those funds fairly equivalent, or do the funds differ wildly in what they hold?  There is still a lot of research to be done in most cases.

MorningStar has a rough approximation of how basic stocks relate. Their Market Barometer indicates gains and losses over different market sectors.  These sectors include companies that have small to large capital, as well as companies that are geared for growth (risky) or value (conservative).  ( I seem to have picked a bad day to get my example from, as all sectors are down.  Typically, there would be a bit of green somewhere).

I thought it might be interesting to try and see how the mutual funds related to one another from the bottom up.  That is, how well do they follow the market barometer grid according to what they actually invest in? Are the small cap funds always more related to mid caps than large caps?  Are growth funds always more related to core than value funds?

It’s possible to see what the mutual funds hold on a quarterly basis.  All funds are required to submit their activity to the SEC, which offers these records publicly through EDGAR (Electronic Data-Gathering, Analysis, and Retrieval system).  It’s possible to extract information from EDGAR, and combine it with historic stock pricing information to get a fairly good representation of what the portfolios own at regular periods.

Understanding how funds relate to one another can help to assess a few important issues of risk:

  1. There is strength in numbers : Typically, if a number of different funds follow a given portfolio allocation, then it’s a sign that a number of very savvy investors believe that this given configuration is a good bet.
  2. There may be opportunity in outliers : Simply following what everyone else is doing will never allow you to outperform your peers.  If funds want to set themselves apart from their peers, they will need to change their allocation ratios, or add new stocks that their peers do not own.
  3. There is usually noise in outliers : In some cases, a given fund may behave very differently from their peers, but perhaps they are not distinguishing themselves from their peers in a useful way.  For instance, they may advertise themselves as following a small cap growth investment style, but have a portfolio allocation more similar to a mid cap investment style.  In other cases, they may not report their holdings correctly to the government, leading to errors in how their portfolio is related.  In both cases, their behavior could be a negative indicator for opportunity.  In the first case, they’re not strongly related to their peers, but they are strongly related to other irrelevant funds.  In the second case, they are not organized enough to submit properly formatted documentation to the government, indicating that they may lack in some administrative areas.

The core component of my visualization technique is an advanced multidimensional scaling method, and it’s a bit too complicated to go into the details here.  However, it’s essentially possible to turn thousands of portfolios into points in two dimensional space, where the portfolios are arranged to preserve their similarity as a geometric distance (similar portfolios are close together, dissimilar portfolios are far apart).

The interesting thing about the resulting scaled representation is how similar it is to the market barometer layout.  In particular, the large cap funds (orange to red) arrange themselves in order from growth, to core, to value.  The grid itself is slightly rotated and reflected, but this is an irrelevant  artifact of the dimensionality reduction technique (rotating/reflecting the points does not affect their distances in two dimensional space).  The mid caps and small caps are separated somewhat from each other, but not as strongly.  They also do not arrange themselves clearly from growth to value like the large caps.  This is to be expected since there is far more diversity of stock options for these fund styles to choose from (there are more small cap stocks than large cap stocks).

I can select individual funds from a small region of the plot, and verify that their holdings are more or less the same.  For instance, the above plot shows three funds taken from the same location on the plot.  While they reflect three different styles (an S&P 500 index fund, a large cap value, and a large cap core fund), they have very similar portfolios.  The bar plots indicate the value of their individual stocks, and the top stocks are labeled with their stock symbols.

The final element I added was to animate this visualization over time to reflect how the funds changed their relationships with others by buying and selling stocks.  It’s interesting to see one or two outliers that identify a hot stock suddenly turn into a small cluster of funds.  It’s also interesting to see other outliers hover and move over different regions of the visualization as they emulate a different style.  There are also plenty of funds that are outliers because they are missing information, or had improperly formatted data.  I was able to remove most of these using the visualization itself to identify them.

You can see the visualization in the movie at the beginning, but it is much clearer if you see it in high definition here.

The upper left legend shows the colors used for the fund type acronyms.  LCC = Large Cap Core, MCG = Mid Cap Growth, SCV = Small Cap Value, etc.  Also, SP5I is an S&P 500 index fund.
The upper right legend shows the coloring scale for the “halo” (the colored border of each fund).  The green to red scale is an indication of the change in value from the previous month.
The bottom legend is an indication of the total portfolio value in dollars, scaled logarithmically, and expressed as the plot point circle area.

From → Mapping

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