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Raw beta is the historical regression estimate of a stock’s systematic risk. Adjusted beta modifies this estimate toward 1 to account for the documented tendency of beta to mean-revert. For forward-looking business valuation, adjusted beta can provide a useful estimate, but the appropriate choice depends on the valuation methodology and data.
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 factor. 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 factor as a price-determining element of the expected return on equities is also an expected value.
Traditionally, beta factors are determined empirically on the basis of historical share returns. This creates a methodological challenge: the valuation requires a forward-looking estimate, while the available beta is generally derived from historical market data.
In valuation practice, beta factors are determined using historical capital market data. This is not a problem from a methodological point of view, but it raises the question of the extent to which historical data can serve as 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 move downward, while betas below 1 tend to move upward. Blume’s research identified this regression tendency toward the market mean of one, which subsequently became an important basis for beta-adjustment approaches (Gangemi, Brooks, & Faff, 1999).
The implication is not that every individual company’s beta will necessarily move toward 1. Rather, the empirical relationship provides a statistical basis for adjusting historical beta estimates when the objective is to estimate future systematic risk.
The mean-reversion property indicates that historical betas may have limited suitability as purely forward-looking estimates. This issue can be addressed by using an adjusted beta rather than the raw beta.
The so-called Blume adjustment is one of the most widely used approaches. It approximates the tendency of beta factors to move toward 1.0 using the following relationship:
Adjusted beta = α0 +α1 × raw beta
where: α0 = 1/3 and α1 = 2/3
For example, if the raw beta is 1.20, the adjusted beta is:
1/3 + 2/3 × 1.20 = 1.13
The adjustment therefore pulls the historical beta toward the market beta of 1.0. The raw beta receives a weighting of two-thirds, while one-third is assigned to the market beta of 1.0. Damodaran also illustrates this conventional 2/3–1/3 adjustment in his corporate-finance material (Damodaran, 2011)
The rationale is straightforward: an extreme historical beta is assumed to contain information about the company’s current systematic risk, but some of that extremity may not persist into the future.
The choice should not be made mechanically.
The adjustment should nevertheless be documented. A valuer should be able to explain why an adjustment was applied, which methodology was used and whether the underlying assumptions are appropriate for the company and valuation date.
Research on beta estimation in business valuation also shows that the 1/3–2/3 adjustment has been used by major financial-data providers, although its suitability for valuation should be considered rather than assumed. Other adjustment approaches exist. For example, Vasicek adjustment incorporates an additional statistical shrinkage mechanism and can therefore produce results that differ from the simple Blume approach. The existence of alternative methods reinforces the point that “adjusted beta” is not a single universally defined number (Echterling & Eierle, 2015).
The choice between raw beta and adjusted beta is relevant for the determination of systematic risk in the CAPM. Raw beta is based directly on historical market data, while adjusted beta incorporates the empirical tendency of beta estimates to move toward 1.
The Blume adjustment provides a simple and widely used way to incorporate this mean-reversion effect. However, adjusted beta should not automatically be regarded as superior to raw beta. The appropriate beta depends on the purpose of the analysis, the quality and representativeness of the underlying data, the valuation methodology and the assumptions about future systematic risk.
For business valuation, the key is therefore not simply to choose between “raw” and “adjusted” beta, but to use a beta methodology that is consistent, transparent and appropriate for the valuation date and objective.
smartZebra’s beta-factor data and valuation tools support the consistent determination and application of beta factors in business valuation.
Updated at 12 August 2026
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.
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.
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.
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.
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.
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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