In this paper, we examine the Capital Asset Pricing Model (CAPM) and demonstrate that when the log returns of an asset are subject to extreme risks or outliers or nonlinear relationship, the Linear Regression (LR) model may not perform well in predicting future returns. Instead, we propose using Quantile Regression, which is more robust to such data anomalies and provides better predictions.