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.
.- Part I: Foundations.
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Chapter 1: Introduction to Optimal Execution.
.- Part II: Tools and techniques.
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Chapter 2: Python Stack for Design and Visualization in Financial
Engineering.
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Chapter 3: Neurodynamic approaches to cardinality-constrained portfolio
optimization.
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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.
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Chapter 6: Data Mining Techniques for Predicting the Non-Performing Assets
(NPA) of Banks in India.
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Chapter 7: Multiobjective optimization of mean-variance-downside-risk
portfolio selection models.
.- Part III: Risk assessment and ethical considerations.
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Chapter 8: Bankruptcy Forecasting Of Indian Manufacturing Companies Post
Ibc Using Machine Learning Techniques.
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Chapter 9: Ensemble Deep Reinforcement Learning for Financial Trading.
Part IV: Real-world Applications.
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Chapter 10: Bibliometric Analysis of Digital Financial Reporting.
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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 Bitcoins energy consumption and its
environmental implications, etc.
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