國立虎尾科技大學 |

Intelligent Asset Management

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Intelligent Asset Management/ by Frank Xing, Erik Cambria, Roy Welsch.
作者:	Xing, Frank.
其他作者:	Cambria, Erik.
面頁冊數:	XXII, 149 p. 43 illus., 34 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Medicine. -
電子資源:	https://doi.org/10.1007/978-3-030-30263-4
ISBN:	9783030302634

Intelligent Asset Management
Xing, Frank.

Intelligent Asset Management[electronic resource] /by Frank Xing, Erik Cambria, Roy Welsch. - 1st ed. 2019. - XXII, 149 p. 43 illus., 34 illus. in color.online resource. - Socio-Affective Computing,92509-5706 ;. - Socio-Affective Computing,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

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

644133
Medicine.

LC Class. No.: R-RZ

Dewey Class. No.: 610

Intelligent Asset Management
LDR:02737nam a22004095i 4500 001 1013065
003 DE-He213
005 20200703045429.0
007 cr nn 008mamaa
008 210106s2019 gw | s |||| 0|eng d
020 $a 9783030302634 $9 978-3-030-30263-4
024 7 $a 10.1007/978-3-030-30263-4 $2 doi
035 $a 978-3-030-30263-4
050 4 $a R-RZ
072 7 $a MBGR $2 bicssc
072 7 $a MED000000 $2 bisacsh
072 7 $a MBGR $2 thema
082 0 4 $a 610 $2 23
100 1 $a Xing, Frank. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1307372
245 1 0 $a Intelligent Asset Management $h [electronic resource] / $c by Frank Xing, Erik Cambria, Roy Welsch.
250 $a 1st ed. 2019.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a XXII, 149 p. 43 illus., 34 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Socio-Affective Computing, $x 2509-5706 ; $v 9
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.
650 0 $a Medicine. $3 644133
650 0 $a Data mining. $3 528622
650 0 $a Artificial intelligence. $3 559380
650 0 $a E-business. $3 1254459
650 0 $a Electronic commerce. $3 557942
650 0 $a E-commerce. $2 gtt $3 654932
650 1 4 $a Biomedicine, general. $3 1253757
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a e-Business/e-Commerce. $3 1070351
650 2 4 $a e-Commerce/e-business. $3 768697
700 1 $a Cambria, Erik. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 883904
700 1 $a Welsch, Roy. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1307373
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030302627
776 0 8 $i Printed edition: $z 9783030302641
776 0 8 $i Printed edition: $z 9783030302658
830 0 $a Socio-Affective Computing, $x 2509-5706 ; $v 1 $3 1263730
856 4 0 $u https://doi.org/10.1007/978-3-030-30263-4
912 $a ZDB-2-SBL
912 $a ZDB-2-SXB
950 $a Biomedical and Life Sciences (SpringerNature-11642)
950 $a Biomedical and Life Sciences (R0) (SpringerNature-43708)