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

## Explanation Ordinary Least Squares (OLS) regression aims to minimize the **sum of squared differences** between the **actual values** of the dependent variable and the **estimated values** from the regression model. In this specific case: - **Dependent variable**: Stock's return (what we're trying to predict) - **Independent variable**: S&P 500 return (the predictor) - **OLS objective**: Minimize the sum of squared differences between the **actual stock returns** and the **estimated stock returns** from the regression equation Let's analyze why the other options are incorrect: **Option A**: Incorrect - This describes minimizing differences related to the S&P 500 returns (independent variable), not the stock returns (dependent variable). **Option B**: Incorrect - This describes minimizing the square of the sum of differences, but OLS minimizes the **sum of squared differences**, not the square of the sum. **Option C**: Incorrect - This involves squared S&P 500 returns, which is not the objective of OLS regression. **Option D**: **Correct** - This accurately describes the OLS objective: minimizing the sum of squared residuals (differences between actual and estimated values of the dependent variable). The mathematical formulation for OLS is: $$\min \sum_{i=1}^{n} (Y_i - \hat{Y}_i)^2$$ Where: - $Y_i$ = actual stock return - $\hat{Y}_i$ = estimated stock return from regression - The sum is over all observations (i = 1 to n)

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