Alpha and beta are two of the most common words used in investing conversations. Interestingly, each has a technical finance definition as well as a more common meaning. However, it can be difficult to distinguish between alpha and beta in real life. Furthermore, several famous investors believe that a framework that relies of things like alpha and beta are flawed at best and dangerous at worst.
The definition of beta (β)
At it’s simplest, beta can be defined as the volatility of an asset relative to a benchmark. The benchmark could be an of index an entire asset class, a specific sector, or something else entirely. For example, a US large-cap stock that is 20% more volatile than the S&P 500 index has a beta of 1.2. Or if the volatility is 20% less than the index, it’s beta is .8. Beta is a measure of relative volatility.
The common meaning of beta
When used colloquially, the word beta usually has a different meaning than it’s technical definition. Beta often refers to the risk and return characteristics of a benchmark. Some examples:
- US large-cap beta may refer to the Dow Jones Industrial Average or the S&P 500.
- Investment grade bond beta is often synonymous with the Bloomberg Barclays Aggregate Index.
- Tech beta may simply refer to a tech-sector ETF.
- International beta could be the MSCI EAFE Index or MSCI ACWI ex-USA Index.
I often refer to index ETFs as cheap, liquid beta. Some may compare large-cap beta to small-cap beta or consumer staple beta to consumer discretionary beta, when discussing risk and returns. Below are some examples of how someone might refer to beta:
- Utility sector beta is sensitive to interest rates.
- An index fund may be called beta exposure.
- An investor holds investment grade bonds and reports a -1% return while the Bloomberg Barclays Agg return is -3%. Some might say that the beta return was -3% (and alpha was positive 2%).
- The Russell 1000 index is large-cap beta, while the Russell 2000 index is small-cap beta, and the Russell 3000 is total market beta (read our explainer on the differences between the Russell 1000, 2000, and 3000).
The definition of alpha (α)
Within the Capital Asset Pricing Model (CAPM), beta (as technically defined) is used to forecast returns. Under CAPM, higher beta leads to higher returns and lower beta results in lower returns. In other words, an asset with more volatility should have higher returns and an asset with lower volatility should have lower returns. Reality rarely unfolds as modeled, so another CAPM equation was created to account for the difference between reality and modeled outcomes. It adds a variable called alpha to account for the difference between an asset’s actual return and it’s expected return (which CAPM predicts to be the benchmark return, adjusted for volatility). Below are a couple of examples on calculating alpha:
- An investor invests in large-cap domestic stocks and reports a 10% return, while the Russell 1000 returns 7%. The investor has produced 3% alpha.
- An investor invests in global equities and reports a 9% return, while the MSCI All Country World Index returns 13%. The investor has (negative) alpha of -4%.
Distinguishing alpha from beta-rotation
One of the challenges for alpha is that it is difficult to prove. in fact, most alpha can be explained away. Consider the following three scenarios:
- If an investor beats the market with a portfolio of anything besides an index, observers argue that the investor took more risk or different risks. They will explain that higher returns are not from alpha, but from different factors, higher beta, or what I call beta combinations.
- If an investor beats the market by rotating sectors or adjusting duration, people will say, “That not alpha, that’s just beta rotation.”
- If an investor buys assets at the lows or sells at the highs, academics will explain that the resulting outperformance is due to timing the market and exposure to systematic risk.
Generating alpha through any of the above activities can be explained away generally as beta rotation, but that does not negate the skill required and/or benefits gained. The much more important question is whether the actions and benefits can be consistently repeated to generate a material risk-adjusted return (net of expenses and taxes). Thus, finding that marginal return is much more difficult than labeling it as alpha or beta.
A side note that this is partially why hedge fund hurdle rates exist, to compensate managers for alpha above some defined level.
“Beware of geeks bearing gifts”
As usual, Buffett says it best. In his 2008 shareholder letter in the midst of the Global Financial Crisis, he wrote:
“Investors should be skeptical of history-based models. Constructed by a nerdy-sounding priesthood using esoteric terms such as beta, gamma, sigma and the like, these models tend to look impressive. Too often, though, investors forget to examine the assumptions behind the symbols. Our advice: Beware of geeks bearing formulas.”
Warren Buffett, 2008 Berkshire Hathaway shareholder letter
A few years later, he expanded on the idea and closed with joking nod to antiquity.
“…they had advanced degrees, and they look very alert, and they came with these — they came with these things that said gamma and alpha and sigma and all that. And all I can say is beware of geeks, you know, bearing formulas.”
Warren Buffett, CNBC interview
Summary of alpha & beta in investing
- Beta is an asset’s volatility, relative to another asset or benchmark.
- Alpha is an asset’s return above or below it’s benchmark, after controlling for differences in volatility.
- Technical definitions notwithstanding, the term beta is often shorthand for a benchmark or may refer to the benchmark’s risk and return characteristics.
- It is often difficult to distinguish between alpha and beta-rotation or beta-combinations.
- Investors should be knowledgeable about beta and alpha, but I do not believe they should become too reliant on these measures. To learn more about the the challenges of risk measurement, investors should read Taleb’s story about the Thanksgiving turkey.
