Alternative Data and Artificial Intelligence Techniques: Applications in Investment and Risk Management (Palgrave Studies in Risk and Insurance)

Zhang, Qingquan Tony; Li, Beibei; Xie, Danxia

ISBN 10: 3031116119 / ISBN 13: 9783031116117

Published by Palgrave Macmillan, 2022

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Bibliographic Details

• Title: Alternative Data and Artificial Intelligence Techniques: Applications in Investment and Risk Management (Palgrave Studies in Risk and Insurance)
• Publisher: Palgrave Macmillan
• Publication Date: 2022
• Language: English
• ISBN 10: 3031116119
• ISBN 13: 9783031116117
• Binding: Hardcover
• Condition: New

About this title

Synopsis

This book introduces a state-of-art approach in evaluating portfolio management and risk based on artificial intelligence and alternative data. The book covers a textual analysis of news and social media, information extraction from GPS and IoTs data, and risk predictions based on small transaction data, etc. The book summarizes and introduces the advancement in each area and highlights the machine learning and deep learning techniques utilized to achieve the goals. As a complement, it also illustrates examples on how to leverage the python package to visualize and analyze the alternative datasets, and will be of interest to academics, researchers, and students of risk evaluation, risk management, data, AI, and financial innovation.

About the Author

Qingquan Tony Zhang is an Adjunct Professor at the University of Illinois at Champaign, R.C. Evan Fellow, Gies Business School, focusing on finance, quantitative investment and entrepreneurship. He is President of the Chicago chapter of the Chinese American Association for Trading and Investment, who has long worked in FinTech, including artificial intelligence and big data. 

Beibei Li is an Associate Professor of IT & Management and Anna Loomis McCandless Chair at Carnegie Mellon University. Dr. Li has extensive experience at leveraging large-scale observational data analytics and experimental analysis with a strong focus on modeling individual user behavior across online, offline, and mobile channels for decision support. 

Danxia Xie is an Associate Professor in Economics at Tsinghua University, China. Dr. Xie’s teaching and research focuses on digital economy, finance, law and economics, and macroeconomics. Dr. Xie has also worked at Peterson Institute for International Economics, a top think tank at Washington, DC.