Answer: OLS minimizes the sum of squared differences between the actual and estimated stock returns.

## Explanation In Ordinary Least Squares (OLS) regression, the objective is to find the line of best fit that minimizes the sum of the squared residuals (errors). ### Key Points: - **Residuals** are the differences between the actual observed values and the predicted values from the regression model - **Squared residuals** are used to eliminate negative signs and give more weight to larger errors - The **sum of squared residuals** (SSR) is what OLS minimizes ### Mathematical Representation: For a regression model: \[ y_i = \beta_0 + \beta_1 x_i + \epsilon_i \] Where: - \( y_i \) = actual stock returns - \( x_i \) = S&P 500 Index returns - \( \hat{y}_i \) = estimated stock returns from the model - \( \epsilon_i = y_i - \hat{y}_i \) = residuals OLS minimizes: \[ \sum_{i=1}^{n} (y_i - \hat{y}_i)^2 = \sum_{i=1}^{n} \epsilon_i^2 \] ### Why Other Options Are Incorrect: - **Option B**: Incorrect because it squares the sum of differences rather than summing squared differences - **Option C**: Incorrect because it focuses on S&P 500 returns rather than stock returns - **Option D**: Incorrect because it deals with squared S&P 500 returns rather than squared residuals Therefore, Option A correctly describes the OLS procedure as minimizing the sum of squared differences between actual and estimated stock returns.

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