|
|
|
|
| LEADER |
03789nam a2200589 4500 |
| 001 |
978-3-030-30263-4 |
| 003 |
DE-He213 |
| 005 |
20191113172212.0 |
| 007 |
cr nn 008mamaa |
| 008 |
191113s2019 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
|
| 040 |
|
|
|d GrThAP
|
| 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
|
| 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.
|
| 650 |
|
0 |
|a Data mining.
|
| 650 |
|
0 |
|a Artificial intelligence.
|
| 650 |
|
0 |
|a E-business.
|
| 650 |
|
0 |
|a Electronic commerce.
|
| 650 |
|
0 |
|a E-commerce.
|
| 650 |
1 |
4 |
|a Biomedicine, general.
|0 http://scigraph.springernature.com/things/product-market-codes/B0000X
|
| 650 |
2 |
4 |
|a Data Mining and Knowledge Discovery.
|0 http://scigraph.springernature.com/things/product-market-codes/I18030
|
| 650 |
2 |
4 |
|a Artificial Intelligence.
|0 http://scigraph.springernature.com/things/product-market-codes/I21000
|
| 650 |
2 |
4 |
|a e-Business/e-Commerce.
|0 http://scigraph.springernature.com/things/product-market-codes/522060
|
| 650 |
2 |
4 |
|a e-Commerce/e-business.
|0 http://scigraph.springernature.com/things/product-market-codes/I26000
|
| 700 |
1 |
|
|a Cambria, Erik.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
|
| 700 |
1 |
|
|a Welsch, Roy.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
|
| 710 |
2 |
|
|a SpringerLink (Online service)
|
| 773 |
0 |
|
|t Springer eBooks
|
| 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 9
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-030-30263-4
|z Full Text via HEAL-Link
|
| 912 |
|
|
|a ZDB-2-SBL
|
| 950 |
|
|
|a Biomedical and Life Sciences (Springer-11642)
|
