CAPM Beta and Market Return Rate

Joachim Kuczynski, 04 November 2025

This article is about an unusual sight on the CAPM beta. It provides a derivation from linear regression with the method of least squares. Often the definition of CAPM beta seems to be very abstract. But from the perspective of this article`s approach it might be clearer.

We assume a data set of market return rates r_{m,i} and equity return rates r_{E,i}. i indicates the i-th of N data pairs. \overline{r_m} and \overline{r_E} are the arithmetic mean values of r_{m,i} and r_{E,i}. With the method of least squares we can make a linear regression that approximates the relationship with a linear function r_E.

(1)   \begin{equation*}r_E=a+br_m\end{equation*}

The slope b of the linear regression function r_E is given by:

(2)   \begin{equation*}b=\frac{\sum_{i=1}^{N}\left( r_{m,i}-\overline{r_m} \right)\left( r_{E,i}-\overline{r_E} \right)}{\sum_{i=1}^{N}\left( r_{m,i}-\overline{r_m} \right)^2}\end{equation*}

The covariance of r_m and r_E is given by:

(3)   \begin{equation*}\text{cov}(r_E,r_m)=\frac{1}{N-1}\sum_{i=1}^{N}\left( r_{m,i}-\overline{r_m} \right)\left( r_{E,i}-\overline{r_E} \right)\end{equation*}

The variance of r_{m,i} is given by:

(4)   \begin{equation*}\text{var}(r_m)=\frac{1}{N-1}\sum_{i=1}^{N}\left( r_{m,i}-\overline{r_m} \right)^2\end{equation*}

Substituting that in the expression for b leads to:

(5)   \begin{equation*}b=\frac{\text{cov}(r_E,r_m)}{\text{var}(r_m)}\end{equation*}

This is the same as beta (\beta_{r_E}) in the Capital Asset Pricing Model (CAPM). That means that \beta_{r_E} is the slope of the linear approximated relationship of equity return rate r_E and market return rate r_m using the method of least squares. In linear approximation we can state:

(6)   \begin{equation*}b=\beta_{r_E}=\frac{\text{cov}(r_E,r_m)}{\text{var}(r_m)}=\frac{\partial r_E}{\partial r_m}\end{equation*}

In my point of view this is a very important point to know when doing investment analysis and using CAPM. It provides an additional interpretation of \beta_{r_E}. This point of view is valid for all betas in general.

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