Quantitative Methods: CFA® Level II

It is 5–10% of the 2027 Level II exam. 39 learning outcomes across 7 learning modules.

84 practice questions and 0 formula cards on this topic in the app.

Basics of Multiple Regression and Underlying Assumptions

• Set out which investment questions multiple linear regression can answer and the steps involved in running one
• Build a multiple linear regression, explain how several explanatory variables jointly relate to the variable being explained, and say what each fitted coefficient means
• Explain what a multiple linear regression takes for granted and read residual plots for signs that those conditions fail

Evaluating Regression Model Fit and Interpreting Model Results

• Judge how much of the variation in the explained variable a multiple regression captures, using ANOVA output and goodness-of-fit statistics
• Set up tests of whether a multiple regression's coefficients, taken two or more at a time, are jointly significant and explain what the outcome of such joint tests tells you
• Work out a forecast of the dependent variable from a fitted regression and given values of the explanatory variable, and explain what it means

Model Misspecification

• Explain how a wrongly specified model distorts regression output and how the usual specification errors can be prevented
• Set out the kinds of heteroskedasticity and their consequences for statistical inference
• Say what serial correlation is and what it does to statistical inference
• Say what multicollinearity is and what it does to a regression analysis

Extensions of Multiple Regression

• Explain influence analysis and the techniques used to spot observations that sway a regression unduly
• Build a multiple regression containing qualitative explanatory variables and explain what its output means
• Build a logistic regression model and explain what its output means

Time-Series Analysis

• Work out a trend forecast for a time series, whether fitted as a linear or a log-linear trend, from given trend coefficients, and judge the result
• Explain what decides the choice between a linear and a log-linear trend for a given series, and assess where trend models fall short
• Explain why a time series needs to be covariance stationary and what it means when a series lacks stationarity
• Set out how an order-p autoregressive (AR) model is built and, from its fitted coefficients, work out forecasts one and two periods ahead
• Explain how residual autocorrelations let you check whether an autoregressive model suits a time series
• Say what mean reversion is and work out the level a series tends to revert to
• Distinguish forecasts made within the sample from those made outside it, and rank time-series models by forecast accuracy using root mean squared error
• Explain why time-series model coefficients can be unstable
• Set out the features of a random walk and how it differs from a covariance stationary process
• Explain what unit roots mean for time-series work, when they tend to arise and how to detect them, and show how to transform a series with a unit root so that an AR model can be used on it
• Set out the stages of testing a series for a unit root (nonstationarity) and explain how the test connects to autoregressive models
• Explain how to detect and adjust for seasonal effects in a time-series model, and work out and explain a forecast from an AR model that includes a seasonal lag
• Say what autoregressive conditional heteroskedasticity (ARCH) is and show how ARCH models help forecast a time series' variance
• Explain how to check time-series variables for cointegration and/or nonstationarity before putting them into a linear regression
• Choose a suitable time-series model for a given investment problem and give reasons for the choice

Machine Learning

• Explain what deep learning is, and what machine learning is in its supervised and unsupervised forms
• Explain overfitting and recognise ways to deal with it
• Explain supervised machine learning algorithms (support vector machine, penalized regression, k-nearest neighbor, random forest, classification and regression tree, and ensemble learning) and decide which kinds of problem each handles best
• Explain unsupervised machine learning algorithms (k-means clustering, principal components analysis and hierarchical clustering) and decide which kinds of problem each handles best

Big Data Projects

• Recognise the stages of a data analysis project and explain each one
• Set out the aims, stages and examples of data preparation and wrangling
• Judge how well a machine learning algorithm fits
• Set out the aims, techniques and examples of exploring data
• Explain techniques for extracting, choosing and engineering features from text data
• Set out the aims, stages and techniques of training a model
• Explain how text-based data is prepared, wrangled and explored for use in financial forecasting