### What Is R-Squared?

R-squared (R^{2}) is a statistical measure that represents the proportion of the variance for a dependent variable that’s explained by an independent variable or variables in a regression model. Whereas correlation explains the strength of the relationship between an independent and dependent variable, R-squared explains to what extent the variance of one variable explains the variance of the second variable. So, if the R^{2} of a model is 0.50, then approximately half of the observed variation can be explained by the model’s inputs.

In investing, R-squared is generally interpreted as the percentage of a fund or security’s movements that can be explained by movements in a benchmark index. For example, an R-squared for a fixed-income security versus a bond index identifies the security’s proportion of price movement that is predictable based on a price movement of the index. The same can be applied to a stock versus the S&P 500 index, or any other relevant index.

### The Formula for R-Squared Is

$begin{aligned} &text{R}^2 = 1 – frac{ text{Explained Variation} }{ text{Total Variation} } \ end{aligned}$R2=1−Total VariationExplained Variation

### Key Takeaways

- R-Squared is a statistical measure of fit that indicates how much variation of a dependent variable is explained by the independent variable(s) in a regression model.
- In investing, R-squared is generally interpreted as the percentage of a fund or security’s movements that can be explained by movements in a benchmark index.
- An R-squared of 100% means that all movements of a security (or other dependent variable) are completely explained by movements in the index (or the independent variable(s) you are interested in).

### Calculating R-Squared

The actual calculation of R-squared requires several steps. This includes taking the data points (observations) of dependent and independent variables and finding the line of best fit, often from a regression model. From there you would calculate predicted values, subtract actual values and square the results. This yields a list of errors squared, which is then summed and equals the explained variance.

To calculate the total variance, you would subtract the average actual value from the predicted values, square the results and sum them. From there, divide the first sum of errors (explained variance) by the second sum (total variance), subtract the result from one, and you have the R-squared.

### What Does R-Squared Tell You?

R-squared values range from 0 to 1 and are commonly stated as percentages from 0% to 100%. An R-squared of 100% means that all movements of a security (or another dependent variable) are completely explained by movements in the index (or the independent variable(s) you are interested in).

In investing, a high R-squared, between 85% and 100%, indicates the stock or fund’s performance moves relatively in line with the index. A fund with a low R-squared, at 70% or less, indicates the security does not generally follow the movements of the index. A higher R-squared value will indicate a more useful beta figure. For example, if a stock or fund has an R-squared value of close to 100%, but has a beta below 1, it is most likely offering higher risk-adjusted returns.

### The Difference Between R-Squared and Adjusted R-Squared

R-Squared only works as intended in a simple linear regression model with one explanatory variable. With a multiple regression made up of several independent variables, the R-Squared must be adjusted. The adjusted R-squared compares the descriptive power of regression models that include diverse numbers of predictors. Every predictor added to a model increases R-squared and never decreases it. Thus, a model with more terms may seem to have a better fit just for the fact that it has more terms, while the adjusted R-squared compensates for the addition of variables and only increases if the new term enhances the model above what would be obtained by probability and decreases when a predictor enhances the model less than what is predicted by chance. In an overfitting condition, an incorrectly high value of R-squared, which leads to a decreased ability to predict, is obtained. This is not the case with the adjusted R-squared.

While standard R-squared can be used to compare the goodness of two or model different models, adjusted R-squared is not a good metric for comparing nonlinear models or multiple linear regressions.

### The Difference Between R-Squared and Beta

Beta and R-squared are two related, but different, measures of correlation but beta is a measure of relative riskiness. A mutual fund with a high R-squared correlates highly with a benchmark. If the beta is also high, it may produce higher returns than the benchmark, particularly in bull markets. R-squared measures how closely each change in the price of an asset is correlated to a benchmark. Beta measures how large those price changes are in relation to a benchmark. Used together, R-squared and beta give investors a thorough picture of the performance of asset managers. A beta of exactly 1.0 means that the risk (volatility) of the asset is identical to that of its benchmark. Essentially, R-squared is a statistical analysis technique for the practical use and trustworthiness of betas of securities.

### Limitations of R-Squared

R-squared will give you an estimate of the relationship between movements of a dependent variable based on an independent variable’s movements. It doesn’t tell you whether your chosen model is good or bad, nor will it tell you whether the data and predictions are biased. A high or low R-square isn’t necessarily good or bad, as it doesn’t convey the reliability of the model, nor whether you’ve chosen the right regression. You can get a low R-squared for a good model, or a high R-square for a poorly fitted model, and vice versa.