Machine Learning Approaches in Financial Analytics
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Beschreibung
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.
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.
Covers all aspects of financial markets along with effective exploration and exploitation of machine learning techniques Presents recent research on machine learning approaches in Financial Analytics Provides theoretical concepts, empirical analysis, and applications
Autor*in
Leandros A. Maglaras
Themen in »Machine Learning Approaches in Financial Analytics«
Fuzzy Sets, Rough Sets Granular Computing for Financial Application Evolutionary Computation for Financial Application Pricing of Structured Securities Behavioral Finance Financial Prediction and Forecasting Big Data Finance and Economics Neural Networks Modeling for Financial Application Deep Learning Models in Finance Probabilistic Modeling/Inference Trading systems & Trading Room Simulation Time Series Analysis Semantic Web and Linked Data Blockchain and Applications Machine Learning in Finance