This is the heavy hitter. A Simple Regression only uses one independent variable, but financial reality is complex. Stock returns depend on the market, interest rates, sector trends, and company-specific news.

The model memorizes noise instead of learning signal.

This reading focuses on practical issues when datasets are massive (terabytes) or messy.

This tells you the percentage of the variation in the dependent variable explained by the independent variable. $$R^2 = \fracRSSSST$$

If you have just passed the CFA Level 1 exam, you might be under the impression that Quantitative Methods is mostly about statistics, probability distributions, and hypothesis testing. Welcome to —a significant leap in complexity and application.

The exam will ask: “Which algorithm reduces overfitting via penalty on coefficient size?” Answer: LASSO or Ridge.

Understanding how "alternative data" (social media sentiment, satellite imagery) is processed for investment insights.

How to survive the Big Data, Machine Learning, and Time-Series gauntlet.

The foundation of Level 2 Quant is . The formula is:

Multiple Linear Regression uses multiple independent variables ($X_1, X_2, \dots, X_k$). $$Y_i = b_0 + b_1X_1i + b_2X_2i + \dots + \epsilon_i$$

The exam tests this material heavily in the format (mini-cases). You will see one, sometimes two, vignettes purely on quantitative methods. You cannot afford to guess here.