Chapter 1: Advanced Quantitative Methods—Multiple Regression, Time-Series, and Machine Learning

In this section

• Multiple Regression Foundations and Applications
  Explore the building blocks of multiple regression analysis, its assumptions, interpretation, and practical applications in finance, especially in equity analysis and macroeconomic forecasting.
• Model Specification, Misspecification, and Extensions
  Explore how to choose the right variables, functional forms, and diagnostics in multiple regression models, including common pitfalls and best-practice extensions.
• Time-Series Analysis and Forecasting Techniques
  An in-depth exploration of how to model and forecast financial and economic data using time-series techniques, focusing on stationarity, AR/MA, and ARIMA frameworks.
• Advanced Topics in Time-Series—Volatility and Seasonality
  Explore ARCH and GARCH models for volatility forecasting, handle deterministic and stochastic seasonality, and discover practical applications of time-series methods in equity and commodity markets.
• Machine Learning and Big Data in Finance
  Explore how supervised, unsupervised, and deep learning techniques are transforming finance, from credit risk modeling to algorithmic trading. Learn best practices for data wrangling, feature engineering, and avoiding overfitting while navigating ethical and regulatory considerations.
• Simulation Methods for Scenario Testing
  Dive into historical and Monte Carlo simulations, scenario analysis, and stress-testing methods. Explore practical applications, limitations, and implementation tools for robust risk assessment in finance.