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In the context of the Capital Asset Pricing Model (CAPM), beta represents the risk measure for systematic risk.

In the context of the Capital Asset Pricing Model (CAPM), beta represents the risk measure for systematic risk. This risk is the portion of the fluctuation in an equity return that cannot be eliminated even within a fully diversified equity portfolio and must therefore be borne by the investor.

Risk-averse investors expect to be compensated for this part of the risk in the form of a risk premium. The price of risk is known as the equity risk premium, and the amount of risk is measured by the beta factors. The price of risk multiplied by the amount of risk gives the total risk premium of the equity return.

The parameters in the CAPM are forward-looking expected values. This applies in particular to the expected covariances of equity returns, which are decisive for the formation of a fully diversified equity portfolio. The beta factors as a price-determining element of the expected return on equities is also an expected value. Traditionally, the beta factors are determined empirically on the basis of historical share returns.

In valuation practice, the beta factors are determined using historical capital market data. This is not a problem from a methodological point of view, but raises the question of the extent to which historical data can be a good indicator of the future risk profile of an investment.

Empirically determined betas statistically show a so-called mean reversion property towards a value of 1. Historical betas greater than 1 tend to fall, while betas less than 1 tend to rise.

The mean reversion property indicates that historical betas are only suitable for forward-looking business valuations to a limited extent. However, this problem can be minimized by using the adjusted beta instead of the raw beta.

The so-called Blume adjustment (M. Blume 1971) is the most frequently used adjustment. The temporal instability and return tendency of the beta factors towards 1.0 is approximated in the Blume adjustment by the following equation:

The adjusted beta is therefore determined here by a mean reversion process in which the historically measured beta is incorporated with a coefficient of 2/3. Although there are also more complex adjustment algorithms (e.g. O. Vasicek, 1973), the relatively easy-to-use and sufficiently valid Blume adjustment has become established in valuation practice.

The choice between raw beta and adjusted beta is crucial for risk assessment in the CAPM. While raw beta is based on historical data, adjusted beta takes into account the tendency towards mean reversion and thus provides a more reliable estimate for future risks. The Blume adjustment is a practical method to take this tendency into account and is therefore frequently used in valuation practice. SmartZebra's tools and expertise support the accurate determination and application of these beta factors efficiently and reliably.

What is the difference between raw beta and adjusted beta?

Raw beta is based on historical stock returns, while adjusted beta adjusts this data to account for mean-reversion bias and provide a more reliable estimate of future risk.

Why is the mean reversion property important for the evaluation?

The mean reversion property shows that historical beta values tend to approach a value of 1 over time, meaning that they are less extreme in the long term than measured in the short term.

What is the Blume Adjustment?

The Blume adjustment is a method of adjusting raw beta by including it in the adjusted beta calculation with a coefficient of 2/3 to account for the mean reversion property.

When should Adjusted Beta be used instead of Raw Beta?

Adjusted beta should be used when a more reliable estimate of future risk is needed, especially when historical data is not representative of the future risk situation.

Are there other methods for adjusting beta factors?

Yes, in addition to the Blume adjustment, there are also more complex methods such as the Vasicek adjustment, but the Blume adjustment has established itself in practice due to its simplicity and reliability.

How can SmartZebra help determine beta factors?

SmartZebra provides tools and data that simplify the complex process of beta determination, enable accurate analysis and ensure compliance requirements are met.

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