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

cfa-level-2 quantitative-method

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

05 Mar 2021

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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Daniel Glyn

2021-03-24

I have finished my FRM1 thanks to AnalystPrep. And now using AnalystPrep for my FRM2 preparation. Professor Forjan is brilliant. He gives such good explanations and analogies. And more than anything makes learning fun. A big thank you to Analystprep and Professor Forjan. 5 stars all the way!

michael walshe

2021-03-18

Professor James' videos are excellent for understanding the underlying theories behind financial engineering / financial analysis. The AnalystPrep videos were better than any of the others that I searched through on YouTube for providing a clear explanation of some concepts, such as Portfolio theory, CAPM, and Arbitrage Pricing theory. Watching these cleared up many of the unclarities I had in my head. Highly recommended.

Nyka Smith

2021-02-18

Every concept is very well explained by Nilay Arun. kudos to you man!

Badr Moubile

2021-02-13

Very helpfull!

Agustin Olcese

2021-01-27

Excellent explantions, very clear!

Jaak Jay

2021-01-14

Awesome content, kudos to Prof.James Frojan

sindhushree reddy

2021-01-07

Crisp and short ppt of Frm chapters and great explanation with examples.

Multiple Regression Model

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