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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 non stationarity 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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