Novel Financial Applications of Machine Learning and Deep Learning: Algorithms, Product Modeling, and Applications

This book presents the state-of-the-art applications of machine learning in finance, focusing on financial product modeling, risk management, and decision-making. It covers advanced techniques like Support Vector Machine, Neural Networks, Random Forest, and Deep Learning and provides software code and datasets for rigorous practice. It is intended for graduate students, researchers, and professionals in forecasting, modeling, trading, risk management, and economics.

Format: Hardback
Length: 231 pages
Publication date: 02 March 2023
Publisher: Springer International Publishing AG

This comprehensive book delves into the cutting-edge applications of machine learning in the finance domain, with a specific focus on financial product modeling. Its primary objective is to enhance the performance of models while mitigating risk and uncertainty. By presenting practical and managerial implications of financial and managerial decision support systems that encompass a diverse range of financial data traits, the book offers valuable insights for both practitioners and scholars. Furthermore, it serves as a valuable guide for implementing risk-adjusted financial product pricing systems, thereby contributing significantly to the financial literacy of the investigated study.

The book encompasses advanced machine learning techniques, including Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches, and their application to finance datasets. It also utilizes real-world financial instances to facilitate business product modeling and data analysis. To enhance the learning experience, the book includes software code, such as MATLAB, Python, and/or R, along with comprehensive datasets spanning various financial domains.

The primary target audience of this book includes graduate students and researchers seeking to delve into financial data analysis. Additionally, it caters to a broad audience, including academics, professional financial analysts, and policy-makers involved in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management. By offering a comprehensive roadmap for financial data analysis, this book empowers individuals to stay at the forefront of this rapidly evolving field.

Weight: 535g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031185519
Edition number: 1st ed. 2023