Handbook of Alternative Data in Finance, Volume I motivates and challenges the reader to explore and apply Alternative Data in finance. The book provides a robust and in-depth overview of Alternative Data, including its definition, characteristics, difference from conventional data, categories of Alternative Data, Alternative Data providers, and more. The book also offers a rigorous and detailed exploration of process, application and delivery that should be practically useful to researchers and practitioners alike.

Features

Includes cutting edge applications in machine learning, fintech, and more

Suitable for professional quantitative analysts, and as a resource for postgraduates and researchers in financial mathematics

Features chapters from many leading researchers and practitioners

Authors: Gautam Mitra, Christina Erlwein-Sayer, Kieu Thi Hoang, Diana Roman, Zryan Sadik