The search for alpha is an often heard refrain in investment circles. Unfortunately, many of those seeking alpha—or attempting to deliver alpha—have only a nebulous idea of what alpha is. First, then, let’s attempt to define exactly what this performance measure means. Then we’ll take a stab at how alpha is best used to evaluate particular investors, investment producets, or investment managers.
Wikipedia’s entry is a good place to start and defines alpha as “… a risk-adjusted measure of the so-called active return on an investment… Often the return of a benchmark is substracted in order to consider relative performance, which yields Jensen’s alpha.” When people talk about alpha, they usually mean performance relative to a benchmark, i.e., Jensen’s alpha, and that’s the approach we’ll take here.
The Mathematics of Alpha
At time t for an investment 
The term 
It is important to adjust the returns by subtracting the risk-free rate. There are technical reasons why this is necessary, but the intuitive idea is that the asset’s return over the risk-free rate gives us a picture of the additional return we receive from taking the risks associated with the investment. Failing to subtract the risk-free rate as shown causes significant distortions in 
Thus, the alpha we get is dependent on the benchmark we choose. It is a relative measure that can be estimated statistically but is never observed directly.
More Complex Models for Alpha
In the cases above, we modeled the returns of an investment as a linear equation based on a particular benchmark. A natural question is how appropriate is a given benchmark as the basis for building the return model for a given investment? One response is to use multiple benchmarks, or factors, to achieve a more complete model.
For such a multi-factor model the equation is
where 
The Challenges of Model Development and Estimation
There are significant challenges to building a multi-factor model. Selecting the factors may be done by collecting a series of representative indices or other financial data; however, fitting such factor models must deal with the inevitable correlations which exist across benchmarks. Uncorrelated factors can be derived by projecting the returns of a collection of representative investments onto a lower dimensional subspace; however, this means that the factors are also not observable and are estimated simultaneously with the parameters.
The parameters of the model are almost certainly time-dependent. Thus, the factor model becomes:
Financial returns are heteroskedastic. This demands that we also estimate time-dependent variances for the model data. (See “All Correlations Tend to One…”, http://wp.me/p3uMW7-2v)
These technical challenges can be dealt with, but the task isn’t easy. Certainly, the naïve approach of collecting a few indices and throwing the data into an off-the-shelf regression program isn’t going to cut it. Unfortunately, it is all too often exactly what is done.
Alpha, the “Average” Investor, and Market Efficiency
Consider the case where we are using some broad, capital-weighted market index as our benchmark and the target investment is one whose potential components are the same as those that constitute the benchmark. The capital-weighted gross returns of the portfolios of all such investors is the benchmark. Therefore, the capital-weighted mean of all alphas will be zero and of all betas will be one.
If, however, we look at the net returns, i.e., returns net of costs such as fees, then the capital-weighted mean alpha must be below zero, because net performance includes costs that are absent from the benchmark or factors. Remember, alpha is a relative measure. If the investment management industry magically experienced a universal rise in competence and allocated capital more efficiently, then the mean alpha would still be slightly below zero.
Arguing that the investment management industry is not doing its job because of that fact is like arguing that American education is failing because half of the students are below average. There certainly may be problems in American education, but any argument for or against that conclusion that is based on that sort of observation is vacuous.
Similarly, pointing out that alpha is approximately zero says nothing about whether markets are efficient or not. Market efficiency means something completely different. Efficient or not efficient, alpha has nothing to say about it.
Forecasting Alpha
Forecasting what alpha we can expect from an investment going forward introduces further complications. The obvious but naïve choice of using the “best fit” estimates as a forecast is not a good idea. Again, there are technical issues why this is so, but we’ll rely on a simple intuitive argument despite the fact that it may make a few of my mathematical colleagues cringe.
Consider the observed alpha of an investment. We can think of this observation as being roughly composed of two parts: skill (model) and luck (noise). Even under the strong assumption that our model is in some fashion complete, the forecast return of the investment should only include the skill component because the future expected value of the luck component is zero.
The luck, although unobserved, has an expected value of zero, so it may seem that the best forecast is still just the model fit. However, if we examine the cross section of alphas across investments, then it is likely that further from the average an estimate is, the greater the likely impact of luck on that realization.
A reasonable approach, then, might be to produce a forecast 
The average alpha is near zero. This means that the best forecast is one in which we have shrunk the magnitude of the estimated alpha. Given the other complications we discussed, determining the optimal shrinkage is just one more difficulty we must face.
This says nothing about the fact that things change. Managers are replaced. The competitive landscape evolves. Thus, the relevance of the data we used in our estimation may be in question.
Non-Linear Models, Neural Networks, …
You might remark at this point that a linear model has limitations and a more sophisticated approach would provide more insight. No arguments here. More sophisticated models do nothing, however, to address the concerns discussed above and often exacerbate them. For our purposes here, they change nothing.
Is Estimating Alpha Worthwhile?
So where are we so far? Alpha is a relative, not absolute, measure. There is not “an” alpha; there are a multitude of alphas dependent upon your modeling choices. Alpha is not directly observable; it can only be estimated from the data we have available to us. Those estimates are not only subject to the usual statisitical uncertainties but are also affected by the fact that the behavior of investments change over time. Even if we get all of that right, the mechanics of actually performing the estimation present formidable challenges. Alpha has definite limitations in what it tells us and many read far too much into it. Finally, even when we have done a solid job of estimating historic alpha, there are additional issues we must face in producing a forecast, which is ultimately what we need to do if we wish to use alpha to help us guide our investment decisions.
So, is it worthwhile estimating alpha? Sure if you can be confident that you have something that is remotely usable. If your approach to alpha is to pick a single benchmark, dump data into a spreadsheet, and hit them with an off-the-shelf OLS routine, then the answer still may be “yes”, but at best that’s a highly qualified “yes”. You must understand what you have done and be certain to take that measure with more than a grain of salt. You must also be certain that those who will use ths estimate to make decisions understand its limitations.
In practice, however, the answer is probably “no”, except perhaps as a side-effect of an analysis performed with different objectives.
So if alpha is not the magic number that allows us to evaluate the performance of an investment advisor or manager, then what do we do? More on that in later posts.