04405nam a22006015i 4500 978-3-031-61037-0 DE-He213 20251030112913.0 cr nn 008mamaa 240827s2024 sz | s |||| 0|eng d 9783031610370 978-3-031-61037-0 10.1007/978-3-031-61037-0 doi Q342 UYQ bicssc COM004000 bisacsh UYQ thema Machine Learning Approaches in Financial Analytics [electronic resource] / edited by Leandros A. Maglaras, Sonali Das, Naliniprava Tripathy, Srikanta Patnaik. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XX, 483 p. 105 illus., 88 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Intelligent Systems Reference Library, 1868-4408 ; 254 -- 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. This 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. Computational intelligence. 64052 Machine learning. 55059 Financial engineering. 58071 Artificial intelligence. 58441 Computational Intelligence. 64052 Machine Learning. 55059 Financial Engineering. 58071 Artificial Intelligence. 58441 Maglaras, Leandros A. editor. (orcid)0000-0001-5360-9782 https://orcid.org/0000-0001-5360-9782 edt http://id.loc.gov/vocabulary/relators/edt 67518 Das, Sonali. editor. edt http://id.loc.gov/vocabulary/relators/edt 82931 Tripathy, Naliniprava. editor. edt http://id.loc.gov/vocabulary/relators/edt 82932 Patnaik, Srikanta. editor. edt http://id.loc.gov/vocabulary/relators/edt 67152 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031610363 Printed edition: 9783031610387 Printed edition: 9783031610394 Intelligent Systems Reference Library, 1868-4408 ; 254 67034 https://doi.org/10.1007/978-3-031-61037-0 ZDB-2-INR ZDB-2-SXIT 62749 62749