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Answer: OLS minimizes the sum of squared differences between the actual and estimated stock returns
The correct answer is D. 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. Option A, B, and C are incorrect because they do not accurately describe the OLS estimation process. Options A and B incorrectly suggest minimizing the square of the sum of differences between actual and estimated returns for either the S&P 500 Index or the stock, while option C incorrectly suggests minimizing the sum of differences between the actual and estimated squared S&P 500 Index returns. These approaches would allow positive and negative differences to cancel each other out, which is not the case with OLS, where the focus is on minimizing the sum of squared differences to ensure a better fit of the regression line to the data.
Author: LeetQuiz Editorial Team
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A risk manager is evaluating how the return of a particular stock responds to the return of the S&P 500 Index. To accomplish this, the manager employs an ordinary least squares (OLS) regression analysis. Which of the following statements accurately describes the OLS process?
A
OLS minimizes the square of the sum of differences between the actual and estimated S&P 500 Index returns
B
OLS minimizes the square of the sum of differences between the actual and estimated stock returns
C
OLS minimizes the sum of differences between the actual and estimated squared S&P 500 Index returns
D
OLS minimizes the sum of squared differences between the actual and estimated stock returns
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