Machine Learning Approaches in Financial AnalyticsКНИГИ » ПРОГРАММИНГ
Название: Machine Learning Approaches in Financial Analytics Автор: Leandros A. Maglaras, Sonali Das, Naliniprava Tripathy, Srikanta Patnaik Издательство: Springer Год: 2024 Страниц: 485 Язык: английский Формат: pdf (true), epub Размер: 53.1 MB
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 serves as a comprehensive guide to the intersection of Machine Learning and finance. It’s designed for both seasoned finance professionals seeking to integrate the latest technological advancements into their work and for data scientists eager to delve into the intricate world of financial analytics.
The financial world has always been a realm of complexity, marked by volatility, uncertainty, and dynamic interconnectedness. Traditional models and tools have often struggled to capture the multifaceted nature of this domain. However, Machine Learning techniques offer a paradigm shift, providing the capability to process vast amounts of data, identify patterns, and generate insights that were previously unimaginable.
Throughout the chapters of this book, we explore the fundamental principles of Machine Learning and how they can be applied to tackle a myriad of financial challenges. From predictive modeling, risk assessment, algorithmic trading, portfolio optimization, fraud detection, to customer segmentation, the potential applications are boundless.
Readers will embark on a journey that begins with foundational concepts and gradually progresses to advanced methodologies, allowing for a comprehensive understanding of both the financial and technological aspects. Real-world case studies and practical examples will illustrate how machine learning algorithms are transforming the way we perceive, analyze, and strategize within financial markets.
The practical problems in financial engineering are highly interdisciplinary, requiring as much facility with applied mathematics, statistics and programming as with finance. Solving mathematically challenging problems and writing efficient computer programs to price complex structured products is just one part of the puzzle, however. Given the number of design elements involved in creating such products, a front end that combines visualization and interactivity is as important as speed and efficiency of computations. In this note we highlight the power of the Python stack for designing graphical user interfaces for engineering structured product solutions by visualizing their payoffs and prices in a web browser. Object-oriented programming in Python combined with the power of NumPy, Matplotlib and Jupyter fits the bill perfectly for design and visualization in financial engineering. We find that Python combined with Jupyter is not only very well suited for designing and visualizing structured products and examining the impact on pricing as different design elements are tweaked, but it is also amenable to a variety of extensions and integration with other open-source computational finance libraries.
Part I. Foundations Part II. Tools and Techniques 2. Python Stack for Design and Visualization in Financial Engineering 3. Neurodynamic Approaches to Cardinality-Constrained Portfolio Optimization 4. Fully Homomorphic Encrypted Wavelet Neural Network for Privacy-Preserving Bankruptcy Prediction in Banks 5. Tools and Measurement Criteria of Ethical Finance Through Computational Finance 6. Data Mining Techniques for Predicting the Non-performing Assets (NPA) of Banks in India 7. Multiobjective Optimization of Mean–Variance-Downside-Risk Portfolio Selection Models Part III. Risk Assessment and Ethical Considerations Part IV. Real-World Applications
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