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

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

Reading 1: Introduction to Linear Regression

-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 2: Multiple Regression

a: Formulate a multiple regression equation to describe the relationship between a dependent variable and several independent variables and determine the statistical significance of each independent variable;

b:  Interpret estimated regression coefficients and their p-value;

c: Formulate a null and an alternative hypothesis about the population value of a regression coefficient, calculate the value of the test statistic, and determine whether to reject the null hypothesis at a given level of significance;

d: Interpret the results of hypothesis tests of regression coefficients;

e: Calculate and interpret a predicted value for the dependent variable, given an estimated regression model and assumed values for the independent variables;

f: Explain the assumptions of a multiple regression model;

g: Calculate and interpret the Fstatistic, and describe how it is used in regression analysis;

h: Contrast and interpret the R2 and adjusted R2 in multiple regression;

i: Evaluate how well a regression model explains the dependent variable by analyzing the output of the regression equation and an ANOVA table;

j: Formulate and interpret  a multiple regression, including qualitative independent variables;

k: Explain the types of heteroskedasticity and how heteroskedasticity and serial correlation affect statistical inference;

-l: Describe multicollinearity and explain its causes and effects in regression analysis;

-m: Describe how model misspecification affects the results of regression analysis and describe how to avoid common forms of misspecification;

n: Interpret an estimated logistic regression;

o: Evaluate and interpret a multiple regression model and its results.

Reading 3: 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 4: 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 5: 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
    Daniel Glyn
    2021-03-24
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    Nyka Smith
    2021-02-18
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    Badr Moubile
    2021-02-13
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    2021-01-27
    Excellent explantions, very clear!
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    2021-01-14
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    sindhushree reddy
    2021-01-07
    Crisp and short ppt of Frm chapters and great explanation with examples.