Besides the slope (beta), a line also has an intercept and a measure of how well it fits the data.

• Alpha (α): This is the line’s intercept—the point where it crosses the vertical axis. Think of it this way: Beta tells you the return you should expect based on the market’s performance. Alpha tells you if the company’s management is delivering any extra return on top of that. A positive alpha is like a bonus for smart decisions the company made, independent of the overall economic tide.
• R-Squared (R²): This is a “confidence score” for our line, officially called the coefficient of determination. It’s a percentage that tells us how much of a stock’s movement is explained by the market’s movement.

For our General Electric example, the R² is 0.5076. This can be translated into a clear sentence:

“About 51% of GE’s monthly price changes can be explained by the price changes of the overall market (the S&P 500).”

This R² tells us how tightly clustered our data dots are to the line. For GE, a score of 51% means the dots are somewhat scattered, like a loose swarm of bees. A score of 95%, by contrast, would look like iron filings clinging tightly to a magnetic bar.

What this R² score also tells us is how much we should trust our beta. Since only about half of GE’s movement is tied to the market, its beta of 1.06 is a good guide, but not a perfect one. Other company-specific news could easily make the stock move differently than beta predicts.

Now, let’s connect these numbers to how a real investor might use them.