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Alternative data and artificial intelligence techniquesapplications in investment and risk management /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Alternative data and artificial intelligence techniquesby Qingquan Tony Zhang, Beibei Li, Danxia Xie.
其他題名:	applications in investment and risk management /
作者:	Zhang, Qingquan Tony.
其他作者:	Li, Beibei.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	xxii, 330 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Portfolio managementData processing.
電子資源:	https://doi.org/10.1007/978-3-031-11612-4
ISBN:	9783031116124$q(electronic bk.)

Alternative data and artificial intelligence techniquesapplications in investment and risk management /
Zhang, Qingquan Tony.

Alternative data and artificial intelligence techniquesapplications in investment and risk management /[electronic resource] :by Qingquan Tony Zhang, Beibei Li, Danxia Xie. - Cham :Springer International Publishing :2022. - xxii, 330 p. :ill., digital ;24 cm. - Palgrave studies in risk and insurance,2523-823X. - Palgrave studies in risk and insurance..

Chapter 1: The introduction of the portfolio management and risk evaluation -- Chapter 2: The major trends in financial portfolio management -- Chapter 3: Machine Learning and AI in financial portfolio management -- Chapter 4: Introduction of Alternative data in Finance -- Chapter 5: Alternative Data utilization from country perspective -- Chapter 6: Smart Beta and Risk Factors based on Textural Data and Machine Learning -- Chapter 7: Smart Beta and Risk Factors based on IoTs and AIoTs Data -- Chapter 8: Environmental, Social Responsibility and Corporate Governance on Corporations -- Chapter 9: Case Study - Fraud and Deception Detection: Text-based Data Analytics -- Chapter 10: Case Study - Investment Risk Analysis based on Sentiment Analysis and implementation -- Chapter 11: Case Study - Analyzing the corporation performance with ESG Factors -- Chapter 12: Alternative Data Visualization in Python.

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. 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.

ISBN: 9783031116124$q(electronic bk.)

Standard No.: 10.1007/978-3-031-11612-4doiSubjects--Topical Terms:

537753
Portfolio management
--Data processing.

LC Class. No.: HG4529.5 / .Z43 2022

Dewey Class. No.: 332.1028563

Alternative data and artificial intelligence techniquesapplications in investment and risk management /
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