Published on
May 27, 2026
How Beta Impacts Risk-Adjusted Returns in CAPM
How beta drives expected returns and portfolio risk: why low-beta can beat high-beta on a risk-adjusted basis, and estimation methods.
Beta measures how much an asset's returns move relative to the market. It’s central to the Capital Asset Pricing Model (CAPM), which calculates expected returns based on risk. High-beta assets are more volatile and amplify market movements, while low-beta assets are less sensitive. CAPM uses this formula:
Expected Return = Risk-Free Rate + Beta × Market Risk Premium.
Key takeaways:
- Beta > 1: Higher risk, higher potential return (e.g., tech stocks).
- Beta < 1: Lower risk, more stable (e.g., utilities).
- Beta = 1: Matches market volatility (e.g., index funds).
Research challenges the idea that higher beta always leads to better returns. Low-beta stocks often outperform on a risk-adjusted basis, defying traditional assumptions. Tools like the Treynor ratio and Jensen's alpha help evaluate whether returns justify risk.
Practical beta estimation methods include regression analysis, adjustments (e.g., Blume), and bottom-up beta for private companies. Accurate beta calculations are vital for portfolio management, financial planning, and valuation.
Beta Values Explained: Risk, Return & Market Sensitivity in CAPM
How Beta Affects Risk-Adjusted Returns
What Are Risk-Adjusted Returns?
Risk-adjusted returns measure how much reward an investment delivers for the level of risk it takes on. Imagine two investments both yielding a 12% annual return. If one involves significantly more volatility to achieve that return, it’s clear they’re not equally efficient. Tools like the Treynor ratio - which divides excess returns by beta - and Jensen's alpha, which calculates how much an asset outperformed its expected return under the CAPM model, help assess performance relative to systematic risk [3][1].
With this concept in mind, we can explore how assets with varying betas behave in response to market movements.
High-Beta vs. Low-Beta Assets
Beta measures an asset's sensitivity to market changes. For example, a beta of 2.0 means the asset tends to move twice as much as the market - up or down. Meanwhile, a low-beta stock, such as one with a beta of 0.5, moves only half as much in either direction [5].
| Asset Beta | Classification | Market Sensitivity | Typical Sectors |
|---|---|---|---|
| β > 1.0 | Aggressive | Amplifies market moves | Technology, high-growth, small-cap |
| β = 1.0 | Neutral | Moves with the market | S&P 500 index funds |
| 0 < β < 1.0 | Defensive | Dampens market moves | Utilities, consumer staples |
| β < 0 | Hedge | Moves opposite the market | Certain inverse ETFs |
As highlighted by Business LibreTexts:
"Investors who are willing to hold stocks that have higher systematic risk should be rewarded more for taking on this market risk." [5]
Yet, in practice, high-beta stocks don’t always deliver the superior returns that theory might suggest.
The Beta Anomaly: What the Research Shows
While beta helps explain how assets respond to market shifts, research has uncovered a surprising twist: low-beta stocks have historically outperformed high-beta stocks on a risk-adjusted basis. This phenomenon, known as the beta anomaly, challenges the assumption that taking on more risk reliably leads to higher returns [2][6].
Frazzini and Pedersen offered one explanation. They found that many investors, unable to use leverage to boost returns from lower-risk assets, flock to high-beta stocks in pursuit of higher gains. This demand inflates prices, reducing future alpha:
"Because constrained investors bid up high-beta assets, high beta is associated with low alpha." [8]
Further adding depth, Campbell and Vuolteenaho dissected beta into two components: "bad beta" (sensitivity to cash flow news) and "good beta" (sensitivity to discount rate news). Their findings revealed that high-beta stocks tend to carry more "good beta", which is linked to a lower risk premium [9]. These insights challenge the basic CAPM framework, suggesting that the relationship between risk and return is more complex than the model assumes.
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Beta, the risk-free rate, and CAPM. Calculate the expected return of a security on Excel.
How to Estimate Beta in Practice
This section dives into practical methods for calculating beta, building on its importance in the Capital Asset Pricing Model (CAPM).
Regression-Based Beta Estimation
Estimating beta accurately is a key step in making informed capital allocation and valuation decisions.
The most common method is Ordinary Least Squares (OLS) regression. Here’s how it works: you plot an asset's excess returns (the stock's return minus the risk-free rate) against the market's excess returns over a historical period. The slope of the resulting line gives you the beta [11]. Typically, the S&P 500 serves as the market proxy, and the 13-week T-bill rate is used as the risk-free rate [14][15].
For reliable results, the CFA Institute suggests using 60 monthly observations (equivalent to 5 years of data), with a minimum of 30 paired data points [13]. However, OLS has its drawbacks - it’s backward-looking and often comes with high standard errors [10]. To avoid distortions, ensure the date series for returns is perfectly aligned [13].
Adjusted and Bottom-Up Beta Methods
Raw OLS betas tend to move toward 1.0 over time. To address this, analysts often apply adjustments:
- Blume Adjustment: This method blends 2/3 of the historical OLS beta with 1/3 of 1.0 (the market average) [11].
- Vasicek Adjustment: This approach uses a Bayesian framework, weighting the historical beta against the market average, with adjustments based on the variability of the stock’s beta [11].
For private companies, which lack a stock price history, the bottom-up beta method is widely used. This involves finding comparable public firms, unlevering their betas to remove the effects of debt, averaging these unlevered betas, and then re-levering them using the private firm’s debt-to-equity ratio with the Hamada equation [10]. Aswath Damodaran of NYU Stern notes:
"Any sample size greater than one is an improvement on a regression beta. However, the more firms that you have in your sample, the greater the potential savings in error." [10]
Using data from just 16 comparable firms can reduce the standard error of a beta estimate by around 75% compared to a single-stock regression [10]. This method is especially useful when a company has recently changed its business structure - such as through an acquisition - because historical regression data would no longer reflect the current situation [10].
These techniques help refine beta estimates, making them more reliable for analyzing risk and expected returns.
Tools for Estimating Beta
Excel is a straightforward tool for beta calculation. You can use the COVARIANCE.S and VAR.S functions or run a full regression through the Data Analysis Toolpak [13]. For more advanced analysis, Python and R offer libraries specifically designed for beta estimation [14][15].
When dealing with illiquid or infrequently traded assets - like small-cap stocks or private equity - OLS can underestimate beta due to stale pricing. In such cases, the Scholes-Williams or Dimson estimators adjust for non-synchronous trading, providing a more accurate beta [11][12].
These methods and tools are essential for building portfolios that balance investment risks with expected returns effectively.
Applying Beta to Portfolio and Financial Decisions
Portfolio Beta and Risk-Adjusted Performance
Once you've calculated reliable beta estimates, the next step is applying them in portfolio management. A portfolio's beta is essentially the weighted average of the betas of its holdings [7]. For example, if 60% of your portfolio is invested in a stock with a beta of 1.4 and the remaining 40% in a stock with a beta of 0.6, the portfolio beta comes out to 1.08 - indicating it’s slightly more volatile than the market. It's worth noting that the benefits of diversification tend to level off after about 30–40 stocks. Beyond this point, beta becomes the main tool for managing risk-adjusted performance.
Beta also plays a key role in position sizing. For instance, to achieve a specific market exposure, you’ll need to allocate more capital to lower-beta assets. This makes beta especially relevant when planning for growth-stage financial strategies.
Beta in Growth-Stage Financial Planning
The role of beta extends beyond portfolio management into the strategic financial decisions of growth-stage companies. For these businesses, beta is critical in capital budgeting and valuation processes. The beta-derived cost of equity determines the hurdle rate, which is the minimum acceptable return for a project to be considered viable [4]. Misjudging beta can lead to poor decisions - either approving unprofitable projects or passing on opportunities that could add value.
An often-overlooked point is that growth opportunities usually involve higher systematic risk compared to existing assets. Research by Bernardo, Chowdhry, and Goyal demonstrates that this trend holds across nearly all industries dating back to 1977 [17]. Using a beta based solely on a company's current operations can understate the actual cost of capital for new growth initiatives.
Even a small adjustment in beta can have a big impact. For example, altering the discount rate by just 0.66% could shift a discounted cash flow (DCF) valuation by 6%. For a company generating $100 million in steady-state free cash flow, this translates to a difference of roughly $62 million [16]. Companies with large cash reserves should also account for the fact that cash (with a beta of zero) skews overall beta calculations. By stripping out cash and recalculating the "pure-play" operating beta, you can then relever it to reflect the company’s actual capital structure [10].
How Phoenix Strategy Group Uses Beta in Financial Advisory
Phoenix Strategy Group integrates beta analysis into every aspect of its advisory services, leveraging the CAPM framework to provide actionable insights. To ensure more reliable inputs for financial models, the firm employs adjusted beta methods such as Blume or Stotz adjustments [16].
In mergers and acquisitions (M&A), Phoenix Strategy Group applies the Hamada equation to unlever and relever beta, allowing for meaningful comparisons between acquisition targets with varying capital structures. This approach helps distinguish between operating risks and leverage effects [16][18]. This is particularly important when a target company has recently shifted its business mix, as historical regression data may no longer reflect its current operations [10].
Whether it’s cash flow forecasting or investor reporting, beta-driven discount rates enable growth-stage companies to present valuations that can withstand scrutiny. This is especially critical during fundraising or due diligence ahead of a potential exit.
Key Takeaways on Beta and Risk-Adjusted Returns
Beta is a measure of how much an asset's returns move in relation to the overall market, helping investors understand its systematic risk. Since the Capital Asset Pricing Model (CAPM) only compensates for systematic risk - ignoring company-specific risks that can be diversified away - getting an accurate beta is crucial for valuation and investment decisions [3].
Interestingly, about 70% of a portfolio's returns can be explained by its beta [1]. Even small changes in beta can have a big impact: for instance, a 0.1 shift in beta can raise the cost of equity by 0.5%–0.6%, while a 1% change in the discount rate can alter terminal value by as much as 20%–30% [20][21].
"Beta is the single variable that determines how much equity risk premium a stock should earn, making it one of the most consequential inputs in cost-of-equity and DCF valuation." - Dr. Andrew Stotz, CEO, A. Stotz Investment Research [16]
Here are three practical ways to refine how beta is used:
- Use Adjusted Beta: Over time, historical beta tends to move closer to 1.0. Adjustments like the Blume or Stotz correction can provide a more accurate, forward-looking beta [16].
- Account for Debt Levels: When comparing companies with different financial structures, unlever the beta to remove the effects of debt, then relever it using the Hamada equation [19].
- Factor in Growth-Stage Risk: Companies with significant growth opportunities often carry higher systematic risk. A single blended beta may understate the cost of capital for these firms [17].
The Security Market Line (SML) serves as a useful diagnostic tool. Assets above the line are undervalued given their risk, while those below it fail to offer enough return for their risk level. When paired with metrics like Jensen's alpha and the Treynor ratio, beta becomes an insightful way to evaluate whether a portfolio is delivering strong risk-adjusted returns or merely tracking market trends [3]. These takeaways highlight beta's importance in CAPM, making it a cornerstone for both valuation and risk management.
FAQs
Why do low-beta stocks sometimes outperform high-beta stocks?
Low-beta stocks tend to outperform high-beta stocks because certain investors are restricted by borrowing limits, making it harder for them to take on additional risk. These limitations often result in high-beta stocks being overvalued, which lowers their expected returns when adjusted for risk. Investors who are less constrained can take advantage of this imbalance by leveraging low-beta stocks to achieve stronger returns. Phoenix Strategy Group supports growth-stage companies in navigating these types of market conditions by offering customized financial and strategic advisory services.
Which beta method should I use for valuation (OLS, adjusted, or bottom-up)?
When it comes to valuation using the Capital Asset Pricing Model (CAPM), the bottom-up beta stands out as the most reliable option. Why? Because it breaks risk assessment into smaller, more precise pieces. By evaluating risk at the level of individual business segments and then factoring in the company's financial leverage, this method delivers a clearer and more dependable estimate.
On the other hand, while OLS beta (Ordinary Least Squares beta) is straightforward to calculate, it often suffers from high standard errors, making its results less dependable. Similarly, adjusted beta, which incorporates the concept of mean reversion, still relies heavily on historical data. This reliance can be problematic since past performance doesn’t always mirror future risks.
In short, the bottom-up beta offers a more tailored and accurate approach, avoiding much of the noise and uncertainty tied to other methods.
How do I unlever and relever beta for different debt levels?
To account for varying debt levels when calculating beta, follow these steps:
-
Unlever the equity beta using the formula:
Unlevered Beta = Levered Beta / [1 + (1 - Tax Rate) * (Debt / Equity)] -
Re-lever the beta to reflect your target debt level:
Levered Beta = Unlevered Beta * [1 + (1 - Tax Rate) * (Debt / Equity)] -
For greater precision, factor in the debt beta with this adjusted formula:
Levered Beta = Unlevered Beta * [1 + (1 - Tax Rate) * (Debt / Equity)] - [Beta of Debt * (1 - Tax Rate) * (Debt / Equity)]
These calculations allow for a more tailored approach, considering the influence of debt on a company's risk profile.
