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Machine learning approaches in finan...

Maglaras, Leandros A.

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Machine learning approaches in financial analytics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning approaches in financial analytics/ edited by Leandros A. Maglaras ...[et al.].
other author:	Maglaras, Leandros A.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	xx, 483 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence - Financial applications. -
Online resource:	https://doi.org/10.1007/978-3-031-61037-0
ISBN:	9783031610370

Machine learning approaches in financial analytics
Machine learning approaches in financial analytics[electronic resource] /edited by Leandros A. Maglaras ...[et al.]. - Cham :Springer Nature Switzerland :2024. - xx, 483 p. :ill. (some col.), digital ;24 cm. - Intelligent systems reference library,v. 2541868-4408 ;. - Intelligent systems reference library ;v. 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.

ISBN: 9783031610370

Standard No.: 10.1007/978-3-031-61037-0doiSubjects--Topical Terms:

3493836
Artificial intelligence
--Financial applications.

LC Class. No.: HG4515.5

Dewey Class. No.: 332.640285631

Machine learning approaches in financial analytics
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505 0 $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.
520 $a 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.
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