By now, I'm sure many of you are tired of hearing about this magical asset management term called “alpha.” In fact, I'm almost at the point of automatically tuning out every time I hear a hedge fund manager (or any active manager) or investor throw around the word.
For an industry that uses the word “alpha” like it's going out of style, I find it amazing the asset management profession has made so little progress when it comes to actually trying to measure it in practice. Given its importance to active management (no alpha = no active management industry) and given all of the verbal airtime “alpha” receives, you would think managers and investors would spend a healthy amount of time trying to actually measure it on a regular basis. Unfortunately, this is not the case.
While I am sure there are many reasons for this, I think the main one has to do with lack of access to cheap, easy to use, easy to understand, consistent, “alpha measuring” infrastructure. For equity-oriented strategies, firms such as Barra and Axioma have been offering “alpha measuring” performance attribution systems for years. Unfortunately, these systems require a non-trivial amount of time and money to set up and maintain, which can act as a barrier for many managers. Furthermore, performance attribution methodologies differ across the various “off-the-shelf” and “in-house” systems, making it difficult for investors to compare manager results.
There is, however, a solution to the above problem. Bloomberg recently released an institutional-quality, holdings-based, factor-based (a la Barra and Axioma), performance attribution system. It has all of the first order, desired qualities, and it has an incremental price tag of zero for Bloomberg users. Because most asset managers already use Bloomberg, it's effectively free and solves the crucial “comparability of results” issue. Additionally, it's easy for the manager to load complete portfolio positions on a daily basis, and the output is easy to digest. Lastly, unlike most attribution analysis used by managers now, the Bloomberg system has the ability to control for the main “smart/alternative/style” betas (e.g. value, momentum, carry, low volatility, etc.) prevalent in today's marketplace, in addition to the more conventional market and industry betas.
A quick demo might best demonstrate its capabilities and have the most impact. Before doing that, however, let's just make sure we're all on the same page with respect to the importance of measuring alpha.
The argument is fairly straightforward. There are many high-quality passive/rules-based money managers able and willing to give investors targeted exposure to various market, industry and “smart” betas at a competitive fee. On the other hand, there are active managers (e.g., long/short equity hedge funds, long-only active equity, etc.) promising to do “security selection” (i.e., alpha generation) above and beyond the embedded betas, for a higher fee. In other words, the passive/rules-based manager represents an opportunity cost to the investor who chooses to go active, and, thus, the active manager needs to beat this opportunity cost on a net-of-fee basis. Otherwise, there's no reason for the active manager to exist. No rational investor would choose an active manager if they could obtain a higher net-of-fee return with the same beta (risk) profile from a passive/rules-based manager.
Back to the demo. I'm going to analyze the February 2015 Form 13F returns of a long/short equity manager called Cloud Gate. These are not the actual returns of Cloud Gate. They represent the returns from mimicking Cloud Gate's publicly available, quarterly 13F filing (as of Dec. 31), which generally only include the long, physical, U.S. equity, listed securities held by the manager (i.e., no short positions, swaps, etc.). Fortunately, Bloomberg automatically loads the 13F holdings of Cloud Gate and many other managers, which makes this demo easy to perform and replicate. However, in practice the following analysis should be performed on the manager's entire portfolio — not the 13F.
Below are the February 2015, Bloomberg, factor-based attribution results for Cloud Gate's long-only 13F portfolio. I chose the S&P 500 (proxied by the S&P 500 ETF SPY) as the benchmark. During this period, Cloud Gate's 13F and the S&P 500 generated a 5.57% and 5.74% return, respectively. Thus, Cloud Gate's long “naïve” alpha (labeled “active” below) was -0.17% — at first glance, a fairly uneventful alpha month. I call it “naïve” because this is the natural first thing to calculate, it's what most people do, and, most importantly, it does not control for all of the various beta (labeled “factor” below) differences between Cloud Gate's 13F and the S&P 500.