The beta coefficient measures the sensitivity of a security or portfolio to market movements. It is a statistic that is calculated using regression analysis and used to estimate the risk of a security or portfolio. The beta coefficient measures a security’s or portfolio’s volatility in relation to the market. A beta of one means that the security or portfolio moves in lockstep with the market.
Beta is employed within the capital quality rating model (CAPM), that describes the connection between systematic risk and expected come back for assets (usually stocks). CAPM is wide used as a technique for rating risky securities and for generating estimates of the expected returns of assets, considering each the chance of these assets and also the price of capital.
The beta coefficient can be interpreted as follows:
- β =1 exactly as volatile as the market, it indicates that its price activity is strongly correlated with the market. Adding a stock with a beta of 1.0 to a portfolio does not increase risk, but it also does not increase the likelihood that the portfolio will provide an excess return.
- β >1 more volatile than the marke, indicates that the security’s price is theoretically more volatile than the market. This means that including the stock in a portfolio raises the portfolio’s risk while potentially increasing its expected return.
- β <1>0 less volatile than the market, the security is theoretically less volatile than the market. A portfolio containing this stock is less risky than one that does not contain the stock. Because utility stocks move more slowly than market averages, they frequently have low betas.
- β =0 uncorrelated to the market
- β <0 negatively correlated to the market. Some stocks have negative betas. A beta of -1.0 indicates that the stock is 1:1 inversely correlated to the market benchmark. This stock could be viewed as the inverse, mirror image of the benchmark’s trends. Negative betas are intended for put options and inverse ETFs.
Therefore, a good beta will depend on your objectives and risk tolerance. A beta of 1.0 would be perfect if you wanted to replicate the broader market in your portfolio, perhaps through an index ETF. A lower beta can be more suitable for you if you are a cautious investor who wants to preserve principal. Betas greater than 1.0 will often yield above-average returns in a bull market, but they will also result in greater losses in a bear market.
From a statistical point of view, the beta represents the slope of the line through regression of the data points. In finance, each of these data points represents the returns of a share compared to those of the market as a whole. The beta efficiently describes the activity of a security’s returns in response to market fluctuations.
The formula to calculate the Beta coefficient is:
Beta (β) = Covariance (Ri, Rm) / Variance (Rm)
Where:
- Ri = a stock’s return
- Rm = overall market’s return
- Covariance = ups & downs of stock returns versus ups & downs of market returns
- Variance = the difference between market returns and its average
The beta calculation is used to help investors understand if an action is going in the same direction as the rest of the market. It also gives an overview of the volatility – or risk – of a stock in relation to the rest of the market. In order for the beta to provide useful information, the market used as the benchmark should be equity-related. Calculating the beta of a bond ETF using the S&P 500 as the benchmark, for example, would not provide much useful insight to an investor because bonds and stocks are too dissimilar.
To ensure that a specific stock is compared to the appropriate benchmark, it should have a high R-squared value relative to the benchmark. R-squared is a statistical measure that shows the percentage of historical price movements of a value that may be explained by the movements of the reference index. When beta is used to determine the level of systematic risk, a security with a high squared R value, relative to its benchmark, may show a more relevant benchmark.
A gold exchange-traded fund (ETF), for example, such as the SPDR Gold Shares (GLD), is linked to the performance of gold bullion. 1 As a result, a gold ETF has a low beta and R-squared relationship with the S&P 500.
One way an equity investor thinks about risk is by dividing it into two categories. The first category is referred to as systematic risk, that is, the risk of a decline in the overall market. The 2008 financial crisis is an example of a systematic risk event; no diversification would have prevented investors from loosing value in their stock portfolios. Systematic risk is also known as un-diversifiable risk.
Unsystematic risk, otherwise known as diversifiable risk, is the uncertainty associated with an individual stock or industry. Unsystematic risk can be seen in the surprise announcement in 2015 that the company Lumber Liquidators (LL) had been selling hardwood flooring with dangerous levels of formaldehyde for example. It was a risk unique to that company. Non-system risk can be partially mitigated by diversification.
Beta theory assumes that stock returns are usually statistically distributed. However, capital markets are subject to huge surprises. In fact, returns aren’t always normally distributed. As a result, what the beta of an action could predict about the future movement of an action is not always true.
A stock with a very low beta may have smaller price swings but be in a long-term downtrend. As a result, adding a downtrending stock with a low beta reduces risk in a portfolio only if the investor defines risk solely in terms of volatility (rather than as the potential for losses). In practice, a low beta stock that is in a downtrend is unlikely to improve the performance of a portfolio.
Similarly, a high beta stock that is volatile in a mostly upward direction will raise the risk of a portfolio while also potentially adding gains. Before assuming that a stock will add or remove risk from a portfolio, investors should evaluate it from other perspectives, such as fundamental or technical factors.
While beta can provide some useful information when evaluating a stock, it does have some limitations. When using the CAPM, beta is useful for determining a security’s short-term risk and analyzing volatility to arrive at equity costs. However, because beta is calculated using historical data points, it becomes less useful for investors attempting to forecast a stock’s future movements. Beta is also less useful for long-term investments because a stock’s volatility can vary significantly from year to year, depending on the company’s growth stage and other factors. Furthermore, the beta measure for a specific stock tends to fluctuate over time, making it unreliable as a stable measure.
Although beta offers some insight into risk, many experts concur that it is not effective as a risk indicator on its own. Beta does not offer any predictions for the future; it just examines a stock’s past performance in comparison to the S&P 500. Additionally, it ignores a company’s foundational factors, including its earnings and expansion potential.
