LEADER 04188nam 22007575 450 001 9910373915903321 005 20220323202208.0 010 $a3-030-30263-6 024 7 $a10.1007/978-3-030-30263-4 035 $a(CKB)4100000009844954 035 $a(DE-He213)978-3-030-30263-4 035 $a(MiAaPQ)EBC5978964 035 $a(PPN)260304069 035 $a(EXLCZ)994100000009844954 100 $a20191113d2019 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIntelligent Asset Management /$fby Frank Xing, Erik Cambria, Roy Welsch 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (XXII, 149 p. 43 illus., 34 illus. in color.) 225 1 $aSocio-Affective Computing,$x2509-5706 ;$v9 311 $a3-030-30262-8 320 $aIncludes bibliographical references and index. 327 $aChapter 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. 330 $aThis book presents a systematic application of recent advances in artificial intelligence (AI) to the problem of asset management. While natural language processing and text mining techniques, such as semantic representation, sentiment analysis, entity extraction, commonsense reasoning, and fact checking have been evolving for decades, finance theories have not yet fully considered and adapted to these ideas. In this unique, readable volume, the authors discuss integrating textual knowledge and market sentiment step-by-step, offering readers new insights into the most popular portfolio optimization theories: the Markowitz model and the Black-Litterman model. The authors also provide valuable visions of how AI technology-based infrastructures could cut the cost of and automate wealth management procedures. This inspiring book is a must-read for researchers and bankers interested in cutting-edge AI applications in finance. 410 0$aSocio-Affective Computing,$x2509-5706 ;$v9 606 $aMedicine 606 $aData mining 606 $aArtificial intelligence 606 $aElectronic commerce 606 $aElectronic commerce 606 $aElectronic commerce 606 $aBiomedicine, general$3https://scigraph.springernature.com/ontologies/product-market-codes/B0000X 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $ae-Business/e-Commerce$3https://scigraph.springernature.com/ontologies/product-market-codes/522060 606 $ae-Commerce/e-business$3https://scigraph.springernature.com/ontologies/product-market-codes/I26000 606 $aIntel·ligència artificial$2thub 608 $aLlibres electrònics$2thub 615 0$aMedicine. 615 0$aData mining. 615 0$aArtificial intelligence. 615 0$aElectronic commerce. 615 0$aElectronic commerce. 615 0$aElectronic commerce. 615 14$aBiomedicine, general. 615 24$aData Mining and Knowledge Discovery. 615 24$aArtificial Intelligence. 615 24$ae-Business/e-Commerce. 615 24$ae-Commerce/e-business. 615 7$aIntel·ligència artificial. 676 $a610 700 $aXing$b Frank$4aut$4http://id.loc.gov/vocabulary/relators/aut$0856358 702 $aCambria$b Erik$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aWelsch$b Roy$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910373915903321 996 $aIntelligent Asset Management$91912460 997 $aUNINA