ISBN:
9783031577086
Language:
English
Pages:
1 Online-Ressource(V, 65 p. 19 illus., 13 illus. in color.)
Edition:
1st ed. 2024.
Series Statement:
Studies in Computational Intelligence 1154
Parallel Title:
Erscheint auch als
Parallel Title:
Erscheint auch als
Parallel Title:
Erscheint auch als
Keywords:
Computational intelligence.
;
Artificial intelligence.
;
Finance.
;
Branding (Marketing).
Abstract:
-- 1. Estimating and Evaluation of International Capital Movements in The Perspective of Corporate Economic Geography. -- 2. Wave Net-TSRS Model for Timeseries Prediction in Finance. -- 3. Integrating Decision Analytics and Advanced Modeling in Financial and Economic Systems through Artificial Intelligence. -- 4. Co-movements between Bitcoin and Gold: Multivariate BEKK-GARCH Models. -- 5. An Approach for backtesting and algorithmic trading with liquidity and Hill Climbing algorithm. -- 6. Unleashing Economic Potential: Exploring the Synergy of Artificial Intelligence and Intelligent Automation.
Abstract:
The book is divided into sections according to the content of the chapters. The first section covers AI concepts with NP and financial issues. The second section covers AI techniques in relation to Fintech issues. The remaining sections are implementation and analysis. As science and technology development and algorithms are widely used in various fields, their influence and development have increased efficiency and productivity. The introduction of algorithms in the financial field has not been an exception to this. In recent years, the growth and development of the financial system have been in sync with the growth of technology. Fintechs were born at the intersection of these two sectors. What happens through the application of computer knowledge in the financial field, or the examination of the efficiency and effectiveness of their use and the interaction and combination of these two fields has been written very infrequently in the majority of books. In Fintech, there are problems that researchers focus on such as customer support, portfolio management, trading algorithms, fraud detection, credit risk assessment, insurance, and wealth management. The mentioned problems are complex and are mapped to NP problems in the field of artificial intelligence. In the last two decades, optimization algorithms, neural networks, and deep learning have been widely applied in many scientific and engineering fields to solve the mentioned problems. The purpose of this book is to provide the reader with the most used artificial intelligence methods for scientific and engineering problems. This book is used by students, scientists, and practitioners in various fields. The chapters are self-explanatory, and the reader can read each one separately. They describe the algorithm used, the chosen problem, and the implementation. In addition, practical examples, comparisons, and experimental results are presented.
DOI:
10.1007/978-3-031-57708-6
