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Answer: Minimize the sum of squared differences between the actual and estimated stock returns.
The OLS procedure is a method for estimating the unknown parameters in a linear regression model. The method minimizes the sum of squared differences between the actual, observed, returns and the returns estimated by the linear approximation. The smaller the sum of the squared differences between observed and estimated values, the better the estimated regression line fits the observed data points. **Key Points:** - OLS minimizes the sum of squared residuals (differences between actual and predicted values) - This is mathematically expressed as minimizing Σ(y_i - ŷ_i)² where y_i are actual stock returns and ŷ_i are estimated stock returns - The procedure finds the best-fitting line that minimizes the overall prediction error - This is the fundamental optimization criterion in linear regression analysis
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A risk manager performs an ordinary least squares (OLS) regression to estimate the sensitivity of a stock's return to the return on the S&P 500 Index. This OLS procedure is designed to:
A
Minimize the square of the sum of differences between the actual and estimated S&P 500 Index returns.
B
Minimize the square of the sum of differences between the actual and estimated stock returns.
C
Minimize the sum of differences between the actual and estimated squared S&P 500 Index returns.
D
Minimize the sum of squared differences between the actual and estimated stock returns.