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Intelligent asset management

Cambria, Erik.

Intelligent asset management

Record Type:	Electronic resources : Monograph/item
Title/Author:	Intelligent asset managementby Frank Xing, Erik Cambria, Roy Welsch.
Author:	Xing, Frank.
other author:	Cambria, Erik.
Published:	Cham :Springer International Publishing :2019.
Description:	xxii, 149 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Asset allocation.
Online resource:	https://doi.org/10.1007/978-3-030-30263-4
ISBN:	9783030302634$q(electronic bk.)

Intelligent asset management
Xing, Frank.

Intelligent asset management[electronic resource] /by Frank Xing, Erik Cambria, Roy Welsch. - Cham :Springer International Publishing :2019. - xxii, 149 p. :ill. (some col.), digital ;24 cm. - Socio-affective computing,v.92509-5706 ;. - Socio-affective computing ;v.1..

Chapter 1. Introduction -- Chapter 2 -- Revisiting the Literature -- Chapter 3. Theoretical Underpinnings on Text Mining -- Chapter 4. Computational Semantics for Asset Correlations -- Chapter 5. Sentiment Analysis for View Modeling -- Chapter 6. Storage and Update of Domain Knowledge -- Chapter 7. Dialog Systems and Robo-advisory -- Chapter 8. Concluding Remarks -- Appendix -- Index.

This book presents a systematic application of recent advances in artificial intelligence (AI) to the problem of asset management. While natural language processing and text mining techniques, such as semantic representation, sentiment analysis, entity extraction, commonsense reasoning, and fact checking have been evolving for decades, finance theories have not yet fully considered and adapted to these ideas. In this unique, readable volume, the authors discuss integrating textual knowledge and market sentiment step-by-step, offering readers new insights into the most popular portfolio optimization theories: the Markowitz model and the Black-Litterman model. The authors also provide valuable visions of how AI technology-based infrastructures could cut the cost of and automate wealth management procedures. This inspiring book is a must-read for researchers and bankers interested in cutting-edge AI applications in finance.

ISBN: 9783030302634$q(electronic bk.)

Standard No.: 10.1007/978-3-030-30263-4doiSubjects--Topical Terms:

209567
Asset allocation.

LC Class. No.: HG4529.5 / .X564 2019

Dewey Class. No.: 332.6

Intelligent asset management
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245 1 0 $a Intelligent asset management $h [electronic resource] / $c by Frank Xing, Erik Cambria, Roy Welsch.
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505 0 $a Chapter 1. Introduction -- Chapter 2 -- Revisiting the Literature -- Chapter 3. Theoretical Underpinnings on Text Mining -- Chapter 4. Computational Semantics for Asset Correlations -- Chapter 5. Sentiment Analysis for View Modeling -- Chapter 6. Storage and Update of Domain Knowledge -- Chapter 7. Dialog Systems and Robo-advisory -- Chapter 8. Concluding Remarks -- Appendix -- Index.
520 $a This book presents a systematic application of recent advances in artificial intelligence (AI) to the problem of asset management. While natural language processing and text mining techniques, such as semantic representation, sentiment analysis, entity extraction, commonsense reasoning, and fact checking have been evolving for decades, finance theories have not yet fully considered and adapted to these ideas. In this unique, readable volume, the authors discuss integrating textual knowledge and market sentiment step-by-step, offering readers new insights into the most popular portfolio optimization theories: the Markowitz model and the Black-Litterman model. The authors also provide valuable visions of how AI technology-based infrastructures could cut the cost of and automate wealth management procedures. This inspiring book is a must-read for researchers and bankers interested in cutting-edge AI applications in finance.
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