Study Notes for CFA® Level II – Quantitative Methods  – offered by AnalystPrep

Study Notes for CFA® Level II – Quantitative Methods – offered by AnalystPrep

Reading 0: Introduction to Linear Regression (Now Part of Level I)

-a. Describe a simple linear regression model and the roles of the dependent and independent variables in the model;

-b. Describe the least squares criterion, how it is used to estimate regression coefficients, and their interpretation;

-c. Explain the assumptions underlying the simple linear regression model and describe how residuals and residual plots indicate if these assumptions may have been violated;

 -d. Calculate and interpret the coefficient of determination and the F-statistic in a simple linear regression;

-e. Describe the use of analysis of variance (ANOVA) in regression analysis, interpret ANOVA results, and calculate and interpret the standard error of estimate in a simple linear regression;

-f. Formulate a null and alternative hypothesis about a population value of a regression coefficient, and determine whether the null hypothesis is rejected at a given level of significance;

-g. Calculate and interpret the predicted value for the dependent variable, and a prediction interval for it, given an estimated linear regression model and a value for the independent variable; 

-h. Describe different functional forms of simple linear regressions;

Reading 1: Basics of Multiple Regression and Underlying Assumptions

-a. Describe the types of investment problems addressed by multiple linear regression and the regression process;

-b. Formulate a multiple linear regression model, describe the relation between the dependent variable and several independent variables, and interpret estimated regression coefficients;

-c. Explain the assumptions underlying a multiple linear regression model and interpret residual plots indicating potential violations of these assumptions;

Reading 2: Evaluating Regression Model Fit and Interpreting Model Results

-a. Evaluate how well a multiple regression model explains the dependent variable by analyzing ANOVA table results and measures of goodness of fit;

-b. Formulate hypotheses on the significance of two or more coefficients in a multiple regression model and interpret the results of the joint hypothesis tests;

-c. Calculate and interpret a predicted value for the dependent variable, given the estimated regression model and assumed values for the independent variable;

Reading 3: Model Misspecification

-a. Describe how model misspecification affects the results of a regression analysis and how to avoid common forms of misspecification;

-b. Explain the types of heteroskedasticity and how it affects statistical inference;

-c. Explain serial correlation and how it affects statistical inference;

-d. Explain multicollinearity and how it affects regression analysis;

Reading 4: Extensions of Multiple Regression

-a. Describe Influence analysis and methods of detecting influential data points;

-b. Formulate and interpret a multiple regression model that includes qualitative independent variables;

-c. Formulate and interpret a logistic regression model;

Reading 5: Time Series Analysis

-a. Calculate and evaluate the predicted trend value for a time series, modeled as either a linear trend or a log-linear trend, given the estimated trend coefficients;

-b. Describe factors that determine whether a linear or a log-linear trend should be used with a particular time series and evaluate limitations of trend models;

-c. Explain the requirement for a time series to be covariance stationary and describe the significance of a series that is not stationary;

-d. Describe the structure of an autoregressive (AR) model of order p and calculate one- and two period-ahead forecasts given the estimated coefficients;

-e. Explain how autocorrelations of the residuals can be used to test whether the autoregressive model fits the time series;

-f. Explain mean reversion and calculate a mean-reverting level;

-g. Contrast in-sample and out-of-sample forecasts and compare the forecasting accuracy of different time-series models based on the root mean squared error criterion;

-h. Explain the instability of coefficients of time-series models;

-i. Describe characteristics of random walk processes and contrast them to covariance stationary processes;

-j. Describe implications of unit roots for time-series analysis, explain when unit-roots are likely to occur and how to test for them, and demonstrate how a time series with a unit root can be transformed so it can be analyzed with an AR model;

-k. Describe the steps of the unit root test for nonstationary and explain the relation of the test to autoregressive time-series models;

-l. Explain how to test and correct for seasonality in a time-series model and calculate and interpret a forecasted value using an AR model with a seasonal lag;

-m. Explain autoregressive conditional heteroskedasticity (ARCH) and describe how ARCH models can be applied to predict the variance of a time series;

-n. Explain how time-series variables should be analyzed for nonstationary and/or cointegration before use in linear regression;

-o. Determine an appropriate time-series model to analyze a given investment problem and justify that choice;

Reading 6: Machine Learning

-a. Describe supervised machine learning, unsupervised machine learning, and deep learning;

-b. Describe overfitting and identify methods of addressing it;

-c. Describe supervised machine learning algorithms—including penalized regression, support vector machine, k-nearest neighbor, classification and regression tree, ensemble learning, and random forest—and determine the problems for which they are best suited;

-d. Describe unsupervised machine learning algorithms—including principal components analysis, K-means clustering, and hierarchical clustering—and determine problems for which they are best suited;

-e. Describe neural networks, deep learning nets, and reinforcement learning;

Reading 7: Big Data Projects

-a. Identify and explain steps in a data analysis project;

-b. Describe objectives, steps, and examples of preparing and wrangling data;

-c. Describe objectives, methods, and examples of data exploration;

-d. Describe objectives, steps, and techniques in model training;

-e. Describe preparing, wrangling, and exploring text-based data for financial forecasting;

-f. Describe methods for extracting, selecting and engineering features from textual data;

-g. Evaluate the fit of a machine learning algorithm;

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