What is Alpha in Investing?
Alpha (α) coefficient in investing is used for measurement of the success of a particular portfolio. Along with beta, the alpha coefficient helps portfolio managers to determine how certain picked assets performed against the market average.
In this article, we’ll explain how to use alpha and why is it important for investors.
What Is Alpha in Investing?
The simple definition of alpha is that it’s a value that shows by how much a certain portfolio (or a security within the portfolio) overperformed S&P 500 or another suitable market index during a given period.
Sometimes alpha is called “excess return” or “abnormal rate of return”.
For example, if the S&P 500 index had +30% growth during the last year, and a different portfolio had a +35% growth rate during the same time, the alpha, in this case, would be equal to 5%. This is by how much the portfolio manager overperformed the market with his specific portfolio.
You can calculate alpha by using this simple formula:
$$ A = R - BR $$
- A: Alpha
- R: Actual Return of a Portfolio
- BR: Benchmark Investment Return
Obviously, alpha can be a negative value too if a portfolio manager failed to show a better than market results.
In capital assets pricing model (CAPM) alpha’s formula is a bit different and more complex as it takes into account beta as well.
Purpose of Alpha
When many reputable investors and market analysts, including Warren Buffett, claiming that a passive long-term approach to investing, when you can just buy and hold an index fund, is a wise way to go, other people are trying to do something better than just purchasing an index. Maybe the structure of the index is “wrong” and there is a more efficient set of assets that can show a higher rate of return relative to a benchmark at the end of the day? Those active and more aggressive portfolio manages use alpha to show that their unique set of investments performed better, thus they can be trusted with your money.
Alpha, Beta, and Risk
Alpha is often used together with another important metric - beta (β), which measures risk and volatility. A beta of 1 means that there is a positive correlation between an asset (i.e. a stock, an ETF, or a portfolio) versus a benchmark. A negative beta of -1 means that there is a correlation 1 to 1, but it’s in the opposite direction. A beta of 0 implies no correlation between the assets at all. A beta of 2 implies that the tracked asset has twice the volatility of a benchmark, therefore it’s a risky investment. If a beta equals to 0.5, it means that the asset has half of a benchmark’s volatility, so it’s quite safe. The concept of beta is somewhat similar to the concept of elasticity if you are familiar with this economic term.
Alpha and beta may seem similar to some extent and confusing, but an easy way to understand their differences is to remember that beta shows volatility (correlation of volatility) and alpha shows return (correlation of returns).
As volatility basically means risk in investing, beta is considered to be an important risk metric, but it works better together with alpha. So, how are they related and how do they work together?
Alpha and Beta Example
To see how alpha and beta are used let’s look at a hypothetical investment with the following characteristics:
- Annual benchmark’s (for example S&P 500) return: +15%
- Annual return of the given portfolio: +20%
- Alpha: 5% (20% - 15%)
- Beta: 2
This example was borrowed (and then modified) from Ken Faulkenberry’s website.
The question is: is it a smart investment?
Well, a portfolio manager showed a better than market results and the portfolio he built performed well, but…
As you can see portfolio’s beta is 2, which means that it has twice more volatility than the benchmark, but is its return twice better?
It’s only 33% better, but this portfolio is 100% riskier, so it’s not a good investment after all.
In a short-term perspective, this new portfolio showed a nice return, but what would happen to it in a long run? With such high volatility, its return will eventually fall below the market average and no previous returns would compensate for that.
In this case, the beta has to be as low as 1.33 to make a new portfolio somewhat equal to the benchmark. If this new portfolio with the alpha of 5 had a beta of 1 or lower than it would be safe to say that it’s a better option than the benchmark.
Criticism of Alpha
This theory of alpha and beta, and how they are used to figure out the effectiveness of an investment, seems to be quite reliable and straight forward at a first glance. However, the concept of alpha occasionally receives some criticism.
For instance, Tim Bennett in his video mentioned a few common downsides of using alpha. He says that portfolio managers are often trying “to sell” alpha, meaning that by promising a high alpha they are justifying their high fees, but they are going to get those fees regardless of the real long-term performance, yet alpha is usually tracked by them only short-term. If the alpha is proven for a sensible period of time of 5-10 years then it probably can be trusted to some extent, but for a shorter period of 1-3 years a high alpha can be even random, maybe portfolio managers just got lucky and in a few years ahead their strategy will fail?
If some advisers and portfolio managers, like Motley Fool, can show an alpha of 237 for 17 years (324 - 87), 14% annually, than they probably can be trusted to some extent, but this story is far from being common and a very limited number of portfolio managers can demonstrate a high alpha for such a long period of time.
Many sources, including Investopedia, present the concerning fact:
“Empirical evidence comparing historical returns of active mutual funds relative to their passive benchmarks indicates that fewer than 10% of all active funds can earn a positive alpha over a 10-plus year time period, and this percentage falls once taxes and fees are taken into consideration.”
So, there is hope that a genius can beat the market, especially in a short-term perspective, but facts are supporting the view of those who are skeptical of the alpha concept at all and they argue that alpha often shouldn’t be even presented as a valid metric.
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