Advanced Analytics for Finance : Theory and Empirics Using Big Data

Designed for graduate and advanced undergrad students, this text synthesizes statistics and econometrics with contemporary artificial intelligence and machine learning methods. Applying empirical methods to massive high-frequency databases, it explores market microstructure, risk, market efficiency, equities, fixed income securities, and options.

Businesses are increasingly leveraging big data in financial analysis to improve decision-making, risk management, and market competitiveness, and professionals who know how to apply this data are in high demand. Designed for graduate programs and advanced undergraduate studies, this text synthesizes traditional statistics and econometrics with contemporary artificial intelligence and machine learning methods, preparing readers for the realities of modern-day financial data analysis. It studies known unknowns versus unknown unknowns and provides a systematic and objective characterization of statistical versus actual significance. Applying advanced theoretical and empirical methods to massive high-frequency databases, the book explores market microstructure, risk, market efficiency, equities, fixed income securities, and options. Grounded in over three decades of research, consulting, management, and teaching experience, it serves as a comprehensive and practical resource for students, practitioners, and scholars in capital markets, advanced analytics, and litigation.