04168nam a22005535i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050000900172072001600181072002300197072001500220245016500235250001800400264006900418300006500487336002600552337002600578338003600604347002400640490006100664505132400725520054502049650003902594650002902633650003402662650003602696650003902732650002902771650003402800650003602834700015702870700008103027700009103108700008703199710003403286773002603320776003603346776003603382776003603418830006803454856004603522912001403568912001503582999001703597978-3-031-61037-0DE-He21320251030112913.0cr nn 008mamaa240827s2024    sz |    s    |||| 0|eng d  a97830316103709978-3-031-61037-07 a10.1007/978-3-031-61037-02doi 4aQ342 7aUYQ2bicssc 7aCOM0040002bisacsh 7aUYQ2thema10aMachine Learning Approaches in Financial Analyticsh[electronic resource] /cedited by Leandros A. Maglaras, Sonali Das, Naliniprava Tripathy, Srikanta Patnaik.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXX, 483 p. 105 illus., 88 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aIntelligent Systems Reference Library,x1868-4408 ;v2540 a -- Part I: Foundations.  -- Chapter 1: Introduction to Optimal Execution.  -- Part II: Tools and techniques.  -- Chapter 2: Python Stack for Design and Visualization in Financial Engineering.  -- Chapter 3: Neurodynamic approaches to cardinality-constrained portfolio optimization.  -- Chapter 4: Fully Homomorphic Encrypted Wavelet Neural Network for Privacy-Preserving Bankruptcy Prediction in Banks.  -- Chapter 5: Tools and Measurement Criteria of Ethical Finance through Computational Finance.  -- Chapter 6: Data Mining Techniques for Predicting the Non-Performing Assets (NPA) of Banks in India.  -- Chapter 7: Multiobjective optimization of mean-variance-downside-risk portfolio selection models.  -- Part III: Risk assessment and ethical considerations.  -- Chapter 8: Bankruptcy Forecasting Of Indian Manufacturing Companies Post Ibc Using Machine Learning Techniques.  -- Chapter 9: Ensemble Deep Reinforcement Learning for Financial Trading. Part IV: Real-world Applications.  -- Chapter 10: Bibliometric Analysis of Digital Financial Reporting.  -- Chapter 11: The Quest for Financing Environmental Sustainability in Emerging Nations: Can Internet Access and Financial Technology be Crucial?  -- Chapter 12: A comprehensive review of Bitcoin’s energy consumption and its environmental implications, etc.  aThis book addresses the growing need for a comprehensive guide to the application of machine learning in financial analytics. It offers a valuable resource for both beginners and experienced professionals in finance and data science by covering the theoretical foundations, practical implementations, ethical considerations, and future trends in the field. It bridges the gap between theory and practice, providing readers with the tools and knowledge they need to leverage the power of machine learning in the financial sector responsibly. 0aComputational intelligence.964052 0aMachine learning.955059 0aFinancial engineering.958071 0aArtificial intelligence.95844114aComputational Intelligence.96405224aMachine Learning.95505924aFinancial Engineering.95807124aArtificial Intelligence.9584411 aMaglaras, Leandros A.eeditor.0(orcid)0000-0001-5360-97821https://orcid.org/0000-0001-5360-97824edt4http://id.loc.gov/vocabulary/relators/edt9675181 aDas, Sonali.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9829311 aTripathy, Naliniprava.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9829321 aPatnaik, Srikanta.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9671522 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303161036308iPrinted edition:z978303161038708iPrinted edition:z9783031610394 0aIntelligent Systems Reference Library,x1868-4408 ;v25496703440uhttps://doi.org/10.1007/978-3-031-61037-0  aZDB-2-INR  aZDB-2-SXIT  c62749d62749