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1. Multiple Regression

  • 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
  • d. evaluate how well a multiple regression model explains the dependent variable by analyzing ANOVA table results and measures of goodness of fit
  • e. formulate hypotheses on the significance of two or more coefficients in a multiple regression model and interpret the results of the joint hypothesis tests
  • f. calculate and interpret a predicted value for the dependent variable, given the estimated regression model and assumed values for the independent variable
  • g. describe how model misspecification affects the results of a regression analysis and how to avoid common forms of misspecification
  • h. explain the types of heteroskedasticity and how it affects statistical inference
  • i. explain serial correlation and how it affects statistical inference
  • j. explain multicollinearity and how it affects regression analysis
  • k. describe influence analysis and methods of detecting influential data points
  • l. formulate and interpret a multiple regression model that includes qualitative independent variables
  • m. formulate and interpret a logistic regression model

2. 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 nonstationarity 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 nonstationarity and/or cointegration before use in a linear regression
  • o. determine an appropriate time-series model to analyze a given investment problem and justify that choice

3. 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 the problems for which they are best suited
  • e. describe neural networks, deep learning nets, and reinforcement learning

4. 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. evaluate the fit of a machine learning algorithm
  • d. describe objectives, methods, and examples of data exploration
  • e. describe methods for extracting, selecting and engineering features from textual data
  • f. describe objectives, steps, and techniques in model training
  • g. describe preparing, wrangling, and exploring text-based data for financial forecasting

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