Is Your Fund Manager A Quant Or A Monkey With A Dart?

What? It's a fair question.
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Investors of all stripes need to understand attribution analysis now more than ever. New data sources and tools for measuring luck versus skill in investing are improving the ability of investors to pick advisors. More on these statistical techniques and how you can use them in a moment, but first let’s take a step back.

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Can your fund manager outperform a monkey throwing darts at a page of stock listings? That question is that age old issue which still plagues many investors. The move towards indexing is driven in large part by concern among investors that they aren’t getting much value for their money by employing a financial advisor.

Against that backdrop, smart investors and fund managers have a common goal – come up with tests that clearly identify the effective managers from the ineffective ones. The seminal study done in this space is On Persistence in Mutual Fund Performance by Mark Carhart in 1997.

Essentially Carhart showed that the large majority of mutual fund managers did not generate alpha versus a Smart Beta investing strategy (Passive Smart Beta investing strategies have been shown by hundreds of studies to outperform the broader market over time). These techniques are useful not just in mutual funds but in hedge funds, and a variety of other settings as well. Subsequent researchers have shown the same conclusions largely fit across the investment spectrum.

Carhart also showed that “skill” in investment fund managers is concentrated in the tails of the return distributions – in other words, the bottom 5% managers who are really bad at their job remain bad at their job, and the top 5% of managers who are good at their job remain good at their job. The key here is to measure performance appropriately.

To measure that performance, Carhart used what is today called the Carhart Four Factor model. That model identifies passive stock characteristics that can be used to “beat” the market over time. Managers (can most easily beat the market by focusing on stocks with these characteristics – but normal investors can achieve that same performance on their own at lower cost once they understand Carhart’s model.

There are a small number of managers that do have true skill in the Carhart model – they can generate returns that outperform the Four Factor model (and its contemporary cousin Smart Beta) through skill, specialized knowledge (e.g. political intelligence), or specialized resources (e.g. algorithms like those used by RenTech).

For investors to isolate and identify these skilled managers, we need good data. Good data lets investors compare apples to apples performance between fund managers. In addition, investors can pull background data from free publicly available sources like the data library of Ken French at Dartmouth.

To run an attribution analysis that will let you examine if your fund manager has skill, follow these steps:

  1. First, get a spreadsheet package capable of performing multiple regression – Excel with the Analysis Toolpak installed will typically suffice
  2. Next, you need to download from the regression series from Professor French's site. (Note: this is a zipped text file, which you'll have to decompress and import into csv/xls/xlsx format.)
  3. Third, you need to extract the monthly returns for your money manager. This is the tricky part. Ideally, you’ll either have been given the data by the fund manager. If not, Morningstar Pro is the next best option, but even then the extraction process is not completely painless. You have to output the monthly returns from the Advanced Analytics section. Then, once you’ve opened this in Excel you need to use the copy/edit/paste special/transpose sequence to get it into vertical format. And if you only have the plain-vanilla package, you’ll have to extract the monthly returns point-by-point with the graph function. Lay this series next to the T-bill returns, and subtract the latter from the former. You now have a column that represents the difference between the two, which is the "risk-adjusted return" (RAR). As an example, I’ve chosen the Dodge and Cox Stock Fund. Column B contains the raw returns; column C, the T-bill returns; column D, the RAR (column B minus column C); and columns E, F, and G, the three regression series. The six rows of the spreadsheet should look something like this:
  4. Last, multiply regress the RAR versus the Mkt, SmB, and HmL series. I recommend including the labels in the regression menu.

Your output will set you well on the way to examining your fund manager’s performance with a critical eye and finding out if he or she really does have skill. More on interpreting these regressions in next week’s column.

Mike McDonald is a PhD in finance and a university professor in the subject at Fairfield University in Connecticut. He also runs a consulting company doing work on quantitative investing, big data, and machine learning for a variety of financial firms, asset managers, institutional investors, and government regulators. Prior to getting his PhD, Mike worked for a major Wall Street bank and one of the top hedge funds. Comments, questions, and concerns are always welcome – email Mike at M.McDonald@MorningInvestmentsCT.com or visit his firm’s website at www.MorningInvestmentsCT.com

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