Machine Learning

Section: Machine Learning Methods in Finance Estimated study time: 60 minutes Content: Machine learning (ML) has become an increasingly important topic in CFA Level 2, reflecting the growing role of data science in investment management. The CFA curriculum distinguishes between supervised learning (where the model learns a mapping from labeled input data to outputs), unsupervised learning (where the model finds structure in unlabeled data), and deep learning (multi-layer neural networks). At Level 2, candidates must understand the conceptual underpinnings of key algorithms, their applications in finance, their limitations, and the overfitting problem — they are not expected to implement algorithms mathematically but must evaluate their appropriate use and interpret results in vignette scenarios. Supervised learning methods include linear regression (for continuous outcomes), logistic regression (for binary classification such as predicting default vs. non-default), classification and regression trees (CART), random forests, gradient boosting (e.g., XGBoost), and support vector machines (SVM). In finance, supervised learning applications include predicting credit default, classifying market regimes, forecasting earnings surprises, and identifying alpha signals. A critical concept is the bias-variance tradeoff: simple models (e.g., linear regression) have high bias and low variance — they tend to underfit; complex models (e.g., deep neural networks with many layers) have low bias and high variance — they tend to overfit to training data and generalize poorly to new data. Regularization techniques such as LASSO (L1 penalty on coefficient magnitude) and ridge regression (L2 penalty on squared coefficient magnitude) reduce variance by shrinking coefficients, at the cost of slightly increased bias. The training, validation, and test set framework is fundamental to evaluating ML models without overfitting. The full dataset is divided into a training set (used to fit the…