Alpha (α) and beta (β) are two crucial coefficients that are used for measurement of success of a particular portfolio. Beta represents the volatility of a particular asset (or the whole portfolio) versus the volatility of the benchmark.
In this article, we'll explain what beta is and give a few simple examples to demonstrate how it can be used.
What is Beta?
The beta coefficient in investing and finance is a metric that shows the volatility of a particular traded market security (often a stock) in comparison to the volatility of the benchmark. Price volatility is a metric that expresses the assertiveness of price fluctuations: low volatility means that the price doesn't change much, and high volatility indicates that an asset's price often changed by a lot.
Beta doesn't just measure the volatility of one stock independently, it compares it with the volatility of the benchmark, which usually is an index like S&P 500, but it can be something else in a particular sector. To determine what benchmark to use in each case, analysts calculate an R-squared value that should be high to indicate a close relation.
Let's say that we want to measure beta for a stock that is a part of the S&P index.
This time we'll pick Electronic Arts, a video game company, with a ticker symbol NASDAQ: EA.
These days we don't even have to assume the volatility or calculate it manually because many trading platforms and websites like Yahoo Finance provide this data for free. Currently, Electronic Arts’ beta (measured monthly for 3 years) is equal to 0.92, but what does this number mean?
In our case of the Electronic Arts stock, 1 would mean the perfect correlation of volatility 1 to 1 with the volatility of the S&P index. It means that this particular asset in a portfolio has the same push of price correlations as the benchmark. If the S&P changes by 10% per year on average, the price of the Electronic Arts stock with beta 1 is supposed to change by 10% too.
If this stock had a beta of 0.5 it'd mean that it's much less volatile and a 20% change in S&P would cause just a 10% change of the EA. Beta of 2 would mean a volatile (i.e. risky) stock that'd change by 20% while the S&P changed by only 10%. Those are usually banks, mining companies and other industries that market participants see as risky.
Volatility works similarly to the elasticity concept in the general economics theory.
In simple terms, beta shows how risky a particular investment is due to internal factors related only to this investment compared to the whole market. You can watch a short video on YouTube by MoneyWeek and Tim Bennett to see a nice visual explanation of what beta is.
A Negative Beta
In some rare cases, it's even possible to have a stock with a negative beta (-1.1, -0.34, etc.). Such a beta would mean that the stock moves in the opposite direction compare to the market and it might be a good idea to use it as a hedging instrument. For example, according to the suredividend.com website, Fox Corp. (FOXA, Fox Broadcasting company, Fox Corporation) had a beta of -0.12, which is quite interesting.
How to Use Beta?
Some people would use beta to predict stock returns relative to the market.
Here is an example of how such a thought process usually goes. If a stock has a beta of 1.4, and the S&P 500 index goes up 10%, it can be expected that this stock would move up by 1.4 * 10%, which is 14%.
During a bull trend it might be better to pick stocks with high beta as they can show higher than average returns, and during a bear trend when the market is falling, it's often better to purchase some safe stocks with a low beta, which would allow an investor to avoid significant losses.
Also, beta can be used for the whole portfolio in regards to its smart diversification.
This is a different and complicated topic of the modern portfolio theory and the efficient frontier that we might cover later in a separate article. This would include formulas and some calculations of variance, covariance, and returns.
Systematic and Unsystematic Risks
Risk can be divided into two categories:
- Systematic risk can't be reduced by diversification.
- Unsystematic risk can be reduced by diversification.
A good example of the systematic risk is the financial crisis in 2008. In this case diversification of an investment portfolio wouldn't be very helpful as everything was falling, except a small number of unique and specific assets with negative betas or things like bitcoin.
Unsystematic risk is a risk associated with a particular asset caused by various internal factors: management strategy, financial issues of this company, regulations, location, innovations in the industry, etc.
Generally speaking, the main idea of the modern portfolio theory is to perform smart diversification by using beta to reduce unsystematic risks.
Criticism and Limitations of Beta
The theory of beta, that you can figure out the optimal and perfect structure of the portfolio, sounds too good to be true, and it does have some weaknesses. The first of them is that the data used for beta calculations is, obviously, historic. This means that we are looking backward, the predictions are based on the past prices, thus they can't always be correct because the history doesn't always repeat itself. To be fair, in science and serious researches we don't have any other methods for predictions rather than to analyze the data from the historical point of view.
Secondly, beta is usually calculated for 3 years, but why use 3 years, why not to use 10 or maybe just one? In most cases, a longer history is better than a short history, thus those stocks that had their IPOs recently aren't quite fitting the whole theory, yet they can be good too. It might be better to use other analysis methods for those new companies, such as the fundamental annual report analysis, etc.
The capital asset pricing model (CAPM) and beta as a big part of it wasn't seriously called into question until in the 1990s two economists Fama and French (the same Fama who supported the CAPM in 1973) presented evidence of inconsistencies in the model. Their work makes two important points that the relationship between average return and beta was weak from 1941 to 1990, and almost nonexistent from 1963 to 1990. Another point they made is that the average return on a security is negatively related to both the firm’s price to earnings ratio and the firm’s market-to-book ratio (M/B), which is quite damaging to the whole concept if proven right. This point was found in the Corporate Finance book (12th edition) by Ross, Westerfield, Jaffe, and Jordan, McGraw-Hill Education, 2019 (Chapter 10: Return and Risk, Is Beta Dead?).
Lastly, beta often doesn't tell you much about the particular sector/industry where this company operates, because a very general index is usually taken as the benchmark. Stocks might behave very differently depending on the sector, that is why it's smart to use other metrics like the quick ratio, the current ratio, the return on assets, and others to compare them not only with the general benchmark and the whole economy, but also with similar sized companies in the sector.