The Great Beta Hoax: Not an Accurate Measure of Risk After All
Every investor is concerned with risk at some level. Arguably investors in retirement are and should be concerned with risk the most. However, not every investor looks at or defines risk in the same way. In truth and fact, there is a wide gap between how various segments in the financial community define and view the complex subject risk.
For example, proponents of academic finance tend to have a very narrow view of the concept of risk. Academic finance seems to favor defining risk as volatility. Since much of their work is derived by conducting statistical analysis on large databases with a strong focus on historical price movements, they tend to prefer statistical expressions of risk such as beta.
In layman terms, academic finance defines beta as a measure of a stock’s volatility in relation to the overall market and/or a benchmark. Therefore, the statistical measure “beta” fits very nicely into their statistical models such as the capital asset pricing model (CAPM). This is a model that allegedly calculates the expected return of an asset based on its beta versus expected market returns.
More traditional definitions of risk are favored by old-school, business owner oriented, fundamental investing proponents. To the fundamentalist, risk is more about more practical matters such as the loss of purchasing power, or more directly the outright loss of capital.
Definitions of Beta
Academics in finance love to utilize and present fancy and complex mathematical formulas applied to comprehensive statistical analysis in order to present and support their theories on investing. Admittedly, it is quite impressive and even cerebral looking to most of us laymen lacking the complex mathematical skills to interpret what we are seeing. However, just because something looks impressive and even complex, doesn’t necessarily mean it’s smart or even true.
The following are some basic definitions of beta that in themselves illustrate what I consider as a penchant for taking the simple and making it complex:
Investopedia offers the following definition: of beta:
“Beta a measure of the volatility, or systematic risk, of a security or a portfolio in comparison to the market as a whole. Beta is used in the capital asset pricing model (CAPM), a model that calculates the expected return of an asset based on its beta and expected market returns.”
“In finance, the beta (β) of an investment is a measure of the risk arising from exposure to general market movements as opposed to idiosyncratic factors. The market portfolio of all investable assets has a beta of exactly 1. A beta below 1 can indicate either an investment with lower volatility than the market, or a volatile investment whose price movements are not highly correlated with the market.
Beta is used in finance as a measure of investment portfolio risk. Beta in this context is calculated as the covariance of the portfolio's returns with its benchmark's returns, divided by the variance of the benchmark's returns. A beta of 1.5 means that for every 1% change in the value of the benchmark, the portfolio's value changes by 1.5%”
Why I Am Writing This Piece?
Importantly, if you really carefully consider the above definitions of beta, it should be clear that they are full of theory, but not necessarily full of fact. A beta attached to an individual company suggests that the company stock price will move in direct proportion as the market moves according to the relationship depicted by the beta. For example, if a company has a beta of 2, this suggests that it will move up or down twice as much as the market over a given period of time. Unfortunately, at least for the academics, the stock price of an individual stock doesn’t always behave precisely as theory suggests.
Nevertheless, if a company does in fact have a high beta, many investors will automatically assume that it is a high risk stock. This became vividly clear to me as a result of comments made on my most recent article on Johnson Controls (JCI). It started with one individual suggesting that, and I quote: “according to Yahoo Finance, the beta for JCI is over 1.5, and the yield is only 2.1%. How can a poor retiree live on 2.1% with such a high risk stock?”
You see, part of the problem is that this individual opined that JCI is a “high risk” stock simply because it has a high beta. Frankly, I don’t believe this person is alone, as many additional people chimed in expressing their displeasure regarding investing in a high beta stock. On the other hand, other readers commented and offered support for the research recommendation as well as their disdain for beta.
From my own perspective, the bigger problem is that based on fundamentals, JCI is in truth a very high quality company and certainly worthy of consideration for retiree’s portfolios. Especially for those retirees that are in need of achieving a higher total return on a part of their portfolio that many higher-yielding dividend growth stocks do not offer.
What I believe that all boils down to is that I will concede beta to be a moderately relevant measure of price volatility. However, I do not concede that volatility is necessarily risk. In fact, at the right valuation high beta could be more of an indicator of extraordinary opportunity than high risk. At extremely high valuations a high beta might imply significant risk. However, high valuation represents high risk, even on a low beta stock.
I am an ardent supporter and believer in value investing. Therefore, I simply cannot accept the idea that beta can be a measure of risk when a company’s valuation is sound or low. Investing in a great business when its valuation is low represents opportunity, not risk. When I do come across great businesses at sound valuation, if it did in fact have a high beta, I would consider that a major plus, not a negative.
High Profile Critics of Beta
Since the individual that initiated the discussion on beta in my article loves it when I include Warren Buffett quotes in my articles, I will accommodate by starting with comments made by Warren Buffett in his 1993 Chairman’s Letter to shareholders:
“The strategy we've adopted precludes our following standard diversification dogma. Many pundits would therefore say the strategy must be riskier than that employed by more conventional investors. We disagree. We believe that a policy of portfolio concentration may well decrease risk if it raises, as it should, both the intensity with which an investor thinks about a business and the comfort-level he must feel with its economic characteristics before buying into it. In stating this opinion, we define risk, using dictionary terms, as "the possibility of loss or injury."
Academics, however, like to define investment "risk" differently, averring that it is the relative volatility of a stock or portfolio of stocks - that is, their volatility as compared to that of a large universe of stocks. Employing databases and statistical skills, these academics compute with precision the "beta" of a stock - its relative volatility in the past - and then build arcane investment and capital-allocation theories around this calculation. In their hunger for a single statistic to measure risk, however, they forget a fundamental principle: It is better to be approximately right than precisely wrong.
For owners of a business - and that's the way we think of shareholders - the academics' definition of risk is far off the mark, so much so that it produces absurdities. For example, under beta-based theory, a stock that has dropped very sharply compared to the market - as had Washington Post when we bought it in 1973 - becomes "riskier" at the lower price than it was at the higher price. Would that description have then made any sense to someone who was offered the entire company at a vastly-reduced price?”
Another high profile billionaire investor that has as much disdain for beta as I do is Seth Klarman. Here’s a comment he made in his 1991 book Margin of Safety:
"I find it preposterous that a single number reflecting past price fluctuations could be thought to completely describe the risk in a security. Beta views risk solely from the perspective of market prices, failing to take into consideration specific business fundamentals or economic developments.
The price level is also ignored, as if IBM selling at 50 dollars per share would not be a lower-risk investment than the same IBM at 100 dollars per share. Beta fails to allow for the influence that investors themselves can exert on the riskiness of their holdings through such efforts as proxy contests, shareholder resolutions, communications with management, or the ultimate purchase of sufficient stock to gain corporate control and with it direct access to underlying value.
Beta also assumes that the upside potential and downside risk of any investment are essentially equal, being simply a function of that investment's volatility compared with that of the market as a whole. This too is inconsistent with the world as we know it. The reality is that past security price volatility does not reliably predict future investment performance (or even future volatility) and therefore is a poor measure of risk."
In spite of what I offer above, there are many investors that do, and will continue to, accept beta as an excellent indicator or measurement of risk. However, I often wonder if those investors recognize and understand a couple of significantly important attributes about beta and its calculation. For example, beta is a rearview mirror metric, and as such, does not necessarily predict how volatile a stock might be in the future. History supports this contention because a long-term analysis of the beta applied to various stocks reveals that a company’s beta is very dynamic and subject to significant change. In other words, a company’s beta can go from very high to very low as time marches on.
An Interesting Analysis of Beta and Risk
According to the definitions of beta presented above, beta is used in the capital asset pricing model that in theory calculates the expected return of an asset based on its beta and expected market returns. Therefore, when a company has a low beta it should indicate that it is a low risk investment and should generate good returns, at least in line with the market or its benchmark. In contrast, when betas are high this would indicate higher risk than what we would expect from the market.
In summary, a company with a high beta is theoretically risky, and a company with a low beta is less risky. However, when I conducted an analysis of several high profile companies based on beta via the FUN Graphs (fundamental underlying numbers) feature of FAST Graphs, I found some interesting results. Interestingly, it seemed that each company generated the best returns when their beta was high, and their worst returns when their beta was low. Consequently, this analysis leads me to question the value of beta as a risk measurement even more. Below are the results I found.
To get a free more detailed perspective on the fundamental merits of beta follow this direct link to a video on my site mistervaluation.com and watch and listen to me analyze beta out loud via the FAST Graphs fundamentals analyzer software tool.
Johnson Controls (JCI)
A comment made on my article on Johnson Controls was the inspiration for this article and analysis of beta. Therefore, the first company I chose to analyze based on beta was appropriately Johnson Controls.
The following FUN Graph depicts Johnson Controls’ beta from 1995 to 2014. The first thing that should be noticed on the beta FUN Graph, and this will apply to all the future graphs, is the extreme variance in each company’s beta overtime. In other words, beta is a very dynamic metric, and just because a company has a high beta today doesn’t necessarily mean it will have a high beta in the future. As previously stated, beta is a rearview mirror metric.
In each example starting with Johnson Controls, I circled the periods of time when beta was low in dark blue indicating low risk, and circled periods of time when beta was high in red indicating high risk. If beta is a great indicator of risk, we might assume that the best returns came when beta was low, and the worst returns came when beta was high. However, in all fairness, I should point out that the argument could be made that higher risk should support higher returns.
On the other hand, one of the primary purposes of this article is to point out that investors should not avoid investing in a great business, especially when valuation is sound, just because it has a high beta. However, my experience, to include the comments made in my Johnson Controls’ article, indicate that investors are prone to do just that.
Utilizing the scrolling feature of FAST Graphs™, I ran Johnson Controls’ earnings and price correlated graph during the period when its beta was low, theoretically indicating low risk. However, this was also obviously a time when the company’s fundamentals were under pressure due to the Great Recession. Consequently, it was no surprise to see that shareholders of Johnson Controls suffered annualized losses of almost 23%, even though the beta was low. Clearly, fundamentals trump statistics.
In contrast, I conducted the same exercise covering the period 2010 to 2015 when Johnson Controls’ beta was high (see the FUN Graph above). I found it interesting that during this high risk beta period of time Johnson Controls’ shareholders earned annual rates of return in excess of 13% per annum. Once again, fundamentals trump statistics.
The following long-term earnings and price correlated graph on Johnson Controls can be analyzed in conjunction with the FUN Graph depicting Johnson Controls’ beta above. A careful analysis should indicate that fundamentals are what drive returns, and more importantly, valuation is a better indicator of risk than a statistic such as beta.
Apple Inc (AAPL)
I conducted the same exercise on Apple as I did with Johnson Controls above. I found this particular analysis especially interesting for a couple of reasons. First of all, I found it fascinating that over the period 1999 through 2001 high tech stock Apple actually had a beta that was half that of the market. Frankly, it was surprising to see Apple with such a low beta.
Additionally, it was also fascinating to me to see another example of how dynamic the beta statistic really is. Today’s high or low beta is not necessarily indicative of what tomorrow’s beta might be. However, I’ve not seen anything suggesting a reliable method of determining the future beta of a company.
Again, using the scrolling functionality of FAST Graphs™ I discover that Apple generated negative returns for its shareholders over the long period when its beta was uncharacteristically low. Once again, fundamentals trump statistics. In other words, as a fundamental investor, I prefer assessing risk based on sound fundamental analysis, and tend to eschew statistics.
For the period 2004 to 2007, Apple had a very high beta ranging from 1.48 to 1.81, which in theory indicated it to be a very high risk stock over those timeframes. Once again, the principle that higher risk should generate high returns seems to hold true. Apple shareholders enjoyed annual rates of return exceeding 100% when their beta “risk” was high.
The following long-term earnings and price correlated graph on Apple can be analyzed in conjunction with the FUN Graph depicting Apple’s beta above. A careful analysis should indicate that fundamentals are what drive returns, and more importantly, valuation is a better indicator of risk than a statistic such as beta. Since Apple has such a high growth history, this graph is presented in logarithmic form.
Johnson & Johnson (JNJ)
Johnson & Johnson carries the highest credit rating possible (AAA) and is considered one of the highest quality blue-chip dividend growth stocks available. Nevertheless, an analysis of its historical beta indicates how dynamic the metric “beta” can be. To save the reader from excessive verbiage, I have conducted the same exercise with this blue chip as I did with the previous examples. Therefore, I will let the graphs speak for themselves.
The following long-term earnings and price correlated graph on Johnson & Johnson can be analyzed in conjunction with the FUN Graph depicting Johnson & Johnson’s beta above. A careful analysis should indicate that fundamentals are what drive returns, and more importantly, valuation is a better indicator of risk than a statistic such as beta.
Southern Company (SO)
Since utilities are known to have low betas, I offer the following beta FUN Graph analysis on Southern Company. What was most fascinating about reviewing this company’s historical beta was the discovery that Southern Company actually had a negative beta over the timeframe 2002 to 2008.
Yes, companies can have a negative beta, and in theory, a negative beta would indicate an expected rate of return that was less than the risk free rate of return. However, as we will soon see, Southern Company has historically generated consistent returns driven by fundamentals, regardless of variations in its beta.
When Southern Company’s beta was high, the company generated returns for shareholders of 9.55% per annum. Importantly, those returns were driven by the fundamentals – earnings and dividends. Beta had little impact.
In contrast, when Southern Company’s beta was negative, it also generated rates of return of 9.38% per annum. Once again, the return was driven by earnings and dividends, and beta had little effect.
The following long-term earnings and price correlated graph on Southern Company can be analyzed in conjunction with the FUN Graph depicting Southern Company’s beta above. A careful analysis should indicate that fundamentals are what drive returns, and more importantly, valuation is a better indicator of risk than a statistic such as beta.
Summary and Conclusions
Statistical measurements such as beta are widely-followed by many investors. When a company has a high beta, many investors will avoid the stock believing that the risk is high, without evaluating the fundamentals. However, I believe that is generally a mistake. Fundamental valuations are significantly more relevant and predictive measurements of risk than any mere statistic could ever be.
Perhaps I might acquiesce that if you come across a highly overvalued company, it might be logical to check its beta in order to get a better perspective on the risk of investing in it. In contrast, if you come across an extremely undervalued company, it might also be logical to check the beta. However, in the undervalued situation, beta might be more of an indicator of a great opportunity than a great risk.
At the end of the day, beta is a rearview mirror statistic that is based solely on an analysis of its price history. To the prudent fundamental oriented value investor, statistics can never substitute for serious analysis and due diligence. Comprehensive research based on fundamentals will serve investors far better in the long run.
(c) FAST Graphs