R-Squared vs. Beta: An Overview

Most stock investors are familiar with the use of beta and alpha correlations to understand how particular securities performed against their peers, but R-squared is a more useful tool for the investor.

  • R-squared (R2) helps determine the practical use and trustworthiness of the beta—and by extension alpha—correlations of securities.
  • Beta is a measure of how closely the price movements of a stock match another stock or sector.

Correlations can show how closely the movement of one investment parallels the movement of an index over time. R-squared is used to determine how reliably they move in the same direction.

You can calculate R-squared using a formula. The numbers also are published online.


R-squared (R2) is a method an investor or analyst can use to see how well securities perform against the benchmark index. As an investor, you want to know how your holding is doing over time compared to others. If, for example, you own Microsoft you want to know if it's performing as well as Apple or HP, or how it's performing compared to a technology index such as the S&P North American Technology Sector Index.

Checking the beta helps. The number is readily available in stock quotes such as the ones on Investopedia.

However, R-squared is a more powerful tool as it measures the differences in the usefulness of such correlations and gives that difference a numeric value.

R-squared defines the practical value of correlations on a percent scale from 0 to 100. A high R-squared number (from 85 to 100) indicates that the performance pattern of the security closely follows that of the chosen index. A low R-squared (anything below 70) indicates that there is little connection between the performance pattern of the security and that of the index.

You can determine R-squared by using a standard formula. Some mutual fund companies report the R-squared of their funds in their advertising literature, but others do not. Yahoo Finance and Morningstar calculate and publish R-squared data as well as beta figures daily.


Beta is a numerical representation of how closely the price movement of a chosen asset is against the movements of other assets. This correlation is measured on a scale from -1 to 1 and shows how the two securities move with one another.

A correlation close to 1 indicates that the two securities rise or fall in a similar pattern. A correlation of 0 indicates that there is no similarity in the behavior of the two securities. A correlation of close to -1 shows that the two securities tend to move in opposite directions from one another, or inversely.

This correlation number is the stock's beta.

Finding two perfectly correlated securities is highly unusual. Readings below 1.0 indicate the security is less volatile than the benchmark, while readings of exactly 1.0 indicate its price should move with the benchmark. Readings greater than 1.0 indicate the asset is more volatile than the benchmark.

On the other hand, the alpha correlation is often viewed as a key performance indicator for stock funds. Alpha is a measure of the risk-adjusted performance of a fund or asset compared to a benchmark index. An alpha of 1.0 indicates that the investment outperformed the index by 1%. An alpha of less than 0 indicates that the investment returned less than the benchmark.

Special Considerations

In general, investments with a high beta reading are seen as relatively risky. Stocks with a high beta will tend to rise more quickly than their benchmarks in bull markets and fall more quickly in bear markets. Over several market cycles, a fund with a high beta may be volatile without producing significant returns.

A high R-squared score of 85% to 100% indicates that the stock or fund predictably moves in close alignment with the benchmark.

Key Takeaways

  • A stock's beta indicates how closely its price moves follow the same pattern as a relevant index over time.
  • A stock's alpha indicates how well it performs compared to a relevant index.
  • R-squared indicates how accurate those performance indicators prove to be over time.