CFA Level 2: Quantitative Methods
Master the multiple regression, time-series, machine learning, and big data techniques tested in the CFA Level II Quantitative Methods topic area. This course covers the full 2026 curriculum: formulating and diagnosing multiple regression models, evaluating model fit with ANOVA and joint hypothesis tests, detecting misspecification, heteroskedasticity, serial correlation, and multicollinearity, extending regression with dummy variables and logistic models, building autoregressive time-series models with unit-root and seasonality testing, and applying supervised and unsupervised machine learning to investment problems. Every chapter teaches through runnable Python and assesses in the exam's own vignette format: item sets with data exhibits, three-option questions, and distractors built from real candidate errors. The course closes with a full timed item-set exam. Aligned to the 5-10% CFA Level II exam weight.
- Level: intermediate
- Duration: 5 weeks
- 9 sections
- Price: $20
Topics: multiple regression, regression assumptions, ANOVA table, joint hypothesis test, adjusted R-squared, AIC and BIC, model misspecification, heteroskedasticity, serial correlation, multicollinearity, Breusch-Pagan test, Durbin-Watson test, variance inflation factor, influence analysis, dummy variables, logistic regression, time-series analysis, autoregressive models, covariance stationarity, unit root test, random walk, mean reversion, seasonality, ARCH, cointegration, machine learning, supervised learning, unsupervised learning, penalized regression, random forest, principal components analysis, k-means clustering, big data projects, text analytics, CFA Level II, quantitative methods, item sets, vignettes
What's inside
- Quantitative Methods (CFA Level II)
- Chapter 1: Basics of Multiple Regression and Underlying Assumptions
- Chapter 2: Evaluating Regression Model Fit and Interpreting Model Results
- Chapter 3: Model Misspecification
- Chapter 4: Extensions of Multiple Regression
- Chapter 5: Time-Series Analysis
- Chapter 6: Machine Learning
- Chapter 7: Big Data Projects
- Chapter 8: Quantitative Methods Item-Set Exam