The first and only book to systematically address methodologies and processes of leveraging non-traditional information sources in the context of investing and risk management
Harnessing non-traditional data sources to generate alpha, analyze markets, and forecast risk is a subject of intense interest for financial professionals. A growing number of regularly-held conferences on alternative data are being established, complemented by an upsurge in new papers on the subject. Alternative data is starting to be steadily incorporated by conventional institutional investors and risk managers throughout the financial world. Methodologies to analyze and extract value from alternative data, guidance on how to source data and integrate data flows within existing systems is currently not treated in literature. Filling this significant gap in knowledge,
The Book of Alternative Data
is the first and only book to offer a coherent, systematic treatment of the subject.
This groundbreaking volume provides readers with a roadmap for navigating the complexities of an array of alternative data sources, and delivers the appropriate techniques to analyze them. The authors—leading experts in financial modeling, machine learning, and quantitative research and analytics—employ a step-by-step approach to guide readers through the dense jungle of generated data. A first-of-its kind treatment of alternative data types, sources, and methodologies, this innovative book:
Provides an integrated modeling approach to extract value from multiple types of datasets
Treats the processes needed to make alternative data signals operational
Helps investors and risk managers rethink how they engage with alternative datasets
Features practical use case studies in many different financial markets and real-world techniques
Describes how to avoid potential pitfalls and missteps in starting the alternative data journey
Explains how to integrate information from different datasets to maximize informational value
is an indispensable resource for anyone wishing to analyze or monetize different non-traditional datasets, including Chief Investment Officers, Chief Risk Officers, risk professionals, investment professionals, traders, economists, and machine learning developers and users.