Machine Learning Approaches in Financial Analytics
| Συγγραφή απο Οργανισμό/Αρχή: | |
|---|---|
| Άλλοι συγγραφείς: | , , , |
| Περίληψη: | XX, 483 p. 105 illus., 88 illus. in color. text |
| Γλώσσα: | Αγγλικά |
| Έκδοση: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
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| Έκδοση: | 1st ed. 2024. |
| Σειρά: | Intelligent Systems Reference Library,
254 |
| Θέματα: | |
| Διαθέσιμο Online: | https://doi.org/10.1007/978-3-031-61037-0 |
| Μορφή: | Ηλεκτρονική πηγή Βιβλίο |
Πίνακας περιεχομένων:
- -- 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.