Perhaps they are calculating the values using different periods of time for comparison. Most likely is that the services are using different benchmarks to represent the market against which the company is compared, e. Regardless of the explanation as to why the values differ between the services, the key point to take away is that you can't accurately compare the beta values of different companies if you retrieve the beta values from different financial services, so use the same source for each company.
It looks like you're using Internet Explorer 11 or older. Apply market research to generate audience insights. Measure content performance. Develop and improve products. List of Partners vendors. It is used in the capital asset pricing model CAPM to estimate the return of an asset. Beta, specifically, is the slope coefficient obtained through regression analysis of the stock return against the market return.
The following regression equation is employed to estimate the beta of the company:. Such a regression analysis can be conducted for listed companies because historical stock-return data is used.
But what about private companies? Due to the lack of market data on the stock prices of private companies, it is not possible to estimate stock beta. Therefore, other methods are required to estimate their beta. In this approach, we first need to find the average beta of the publicly-traded companies that generate income from similar operations as the private company.
This will be a proxy for the industry average levered beta. Second, we need to unlever the average beta using the average debt-to-equity ratio for these comparable companies. Assume we want to estimate the beta of an illustrative energy services company with a target debt-to-equity ratio of 0. The equity-weighted average beta of the four companies is 1. This is close to the arithmetic average of about 1. The chosen method to find the average beta may depend on the specifics of the data and size range of the comparable companies.
For instance, if there are one very large company and three very small companies, then a weighted average method will be biased toward the beta of the large company. The next step is unlevering the average beta. The agricultural equipment business, however, was in a slump, largely because many low-moisture areas had received unusually abundant rainfall. Exhibit VI Performance of Subsidiary vs. Industry June —December The heads of both Alaska Gas and Lockwood had forecast their results rather well.
If they believed its prospects were dismal, AKI executives would obviously want to answer the following questions:.
A summary appears in Exhibit VII. The group judged Alaska Gas to be in a superior position with respect to its facilities and resources but also in a deteriorating position because of the declining growth rate of its Anchorage market. Since the commission set the rates the company could charge, its future returns were difficult to predict. Lockwood had products with strong competitive positions, but they were in mature and declining markets. Management expected low crop prices to continue, and low prices would lead to a drop in new and replacement purchases of farm equipment.
Moreover, technological innovation was altering the irrigation equipment business, and the barriers to entry were few. The AKI staff turned this analysis into estimates of the future risk and return for each subsidiary. Analysts figured cash flows and, by using a discounted cash flow approach, converted them to estimated returns. Thus far, a rather traditional analysis. Each circle represents a subsidiary, and the circle size the proportion of corporate capital employed. Not unexpectedly, the Alaska Interstate managers decided that Alaska Gas would be less risky 2.
This exhibit also shows the difference between the first assessment these managers made, in April , and the one they made in December The arrows show the movement from the April forecast to the circle that represents the December forecast. While far more subjective as a measure of future risk than a mechanistic approach like the industry proxy betas, this approach included the best judgment of the most knowledgeable people.
The process of obtaining the information in a systematic way forced these managers to make their evaluations with care. Exhibit IX shows the anticipated changes in risk and return by The circles are those made in the December assessment, and the arrows point to the expected position in While planning little new investment in either Lockwood or Alaska Gas, the AKI officers expected better returns from Lockwood when an assumed pent-up demand for irrigation equipment made itself felt.
On the whole, the managers expected the entire AKI portfolio to become more profitable, at a slight increase in risk. That increase in risk would come not from Lockwood or Alaska Gas but from the new offshore gas venture. It had, of course, in the short run quite the opposite effect. Making explicit judgments about risk and return, however, puts a spotlight on the assumptions behind them and inhibits overenthusiasm. Second, the use of probabilistic analysis could have helped pinpoint the magnitude of the systematic and the unsystematic risk.
These changes are important; the first ensures that the data will be comparable from manager to manager, and the second defines the sources of risk that require managers to follow different strategies. The evidence indicated that Alaska Gas was not particularly sensitive to macroeconomic events and was unlikely to be exposed to much risk from industry or company-specific sources. Lockwood appeared to be quite different. With a beta of 1. So Lockwood was a risky venture. Great opportunities as well as dangers lie with such businesses.
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