Multiple regression basics

Multiple regression assumptions

Goodness of fit

Testing joint hypotheses for coefficients

Forecasting using multiple regression

Misspecified functional form

Heteroskedasticity

Serial correlation

Multicollinearity

Influence analysis

Dummy variables

Qualitative dependent variables

Linear and log-linear trend models

Trend models and testing for correlated errors

AR time-series models and covariance-stationary series

Detecting serially correlated errors in an AR model

Mean reversion and multiperiod forecasts

Comparing forecast model performance

Instability of regression coefficients

Random walks

Unit root test of nonstationarity

Moving-average time-series models

Seasonality in time-series models

ARMA and ARCH models

Regressions with more than one time series

Machine learning basics

Evaluating ML algorithm performance

Supervised ML algorithm: penalized regression

Support vector machine

K-nearest neighbor

Classification and regression tree

Ensemble learning and random forest

Classificaton of winning and losing funds case study

Unsupervised ML algorithms and principal component analysis

Clustering

K-means clustering

Hierarchical clustering

Clustering stocks based on co-movement similarity case study

Neural networks

Deep neural networks

Deep neural network-based equity factor model case study

Choosing an appropriate ML algorithm

Executing a data analysis project

Data preparation and wrangling

Unstructered (text) data

Data exploration objectives and methods

Unstructered data: text exploration

Model training, structured vs. unstructured data, and method selection

Performance evaluation

Tuning

Financial forecasting project

Financial forecasting project – data exploration

Financial forecasting project – model training

Financial forecasting project – results and interpretation