Introduces alternative data utilized in state-of-art portfolio management and risk evaluation
Includes multiple use cases to illustrate the powerfulness of alternative data to study anomalies
Covers the largest amount of the alternative data providers and producers in the world
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 352 pp. Englisch. N° de réf. du vendeur 9783031116148
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Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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, an. N° de réf. du vendeur 1155871976
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Taschenbuch. Etat : Neu. Alternative Data and Artificial Intelligence Techniques | Applications in Investment and Risk Management | Qingquan Tony Zhang (u. a.) | Taschenbuch | Palgrave Studies in Risk and Insurance | xxii | Englisch | 2023 | Springer Nature Switzerland | EAN 9783031116148 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. N° de réf. du vendeur 127803850
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Introduces alternative data utilized in state-of-art portfolio management and risk evaluationIncludes multiple use cases to illustrate the powerfulness of alternative data to study anomaliesCovers the largest amount of the alternative data providers and producers in the worldSpringer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 352 pp. Englisch. N° de réf. du vendeur 9783031116148
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N° de réf. du vendeur 9783031116148
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