Answer: The regressions are prone to multicollinearity, making it difficult to interpret individual coefficients

## Explanation Cross-sectional regressions for predicting P/E ratios are indeed prone to multicollinearity issues. This occurs because the explanatory variables used in such regressions (such as growth rates, payout ratios, risk measures, etc.) are often highly correlated with each other. When multicollinearity is present: - Individual coefficient estimates become unstable - Standard errors of coefficients increase - It becomes difficult to interpret the individual impact of each explanatory variable - The regression results may appear statistically significant even when the relationship is not meaningful Option A is incorrect because regression coefficients and explanatory power do not typically remain stable over years due to changing market conditions and company fundamentals. Option B is incorrect because there is no requirement to use a standard set of explanatory variables - analysts can choose variables based on theoretical considerations and empirical evidence.

Author: LeetQuiz Editorial Team