Advances in Statistical Modeling and Financial Machine Learning - Amir Ahmad Dar

The interplay between mathematics, finance, and technology is becoming increasingly intricate and vital in today’s rapidly evolving world. This book explores this complex relationship, offering a thorough examination of advanced theories, models, and applications that are shaping the future across these interconnected fields. The chapters have been curated to reflect the diversity of current research and its practical implications, providing readers with a deeper understanding of both foundational concepts and innovative developments.

Divided into three sections, Advances in Statistical Modeling and Financial Machine Learning offers studies in foundations and developments in mathematical and statistical models, mathematical models in finance and emerging technological impacts, and control systems, fuzzy logic, and decision-making in complex environments.

The book begins with an exploration of queuing models, a fundamental area of operations research, which, while theoretical, has real-world applications in various industries, including telecommunications, healthcare, and retail, where managing customer flow and service time is essential for operational success. The book looks into the realm of statistical inference, focusing on stress-strength reliability measures, important in the fields of engineering, manufacturing, and quality control, providing valuable insights into risk assessment. A comparative study of exponentiated distributions is presented, highlighting the importance of selecting the appropriate statistical distribution to model data accurately, a decision that can significantly impact the outcomes of research and analysis.

The volume also looks at the transformative effects of the Fintech revolution, which is redefining how banking and finance operate, creating new opportunities for innovation while also presenting significant challenges. Chapters explore the impact of Fintech on traditional banking models, mathematical models for option pricing, the growing influence of machine learning in education, using data-driven strategies for stock price prediction.

Several novel modeling systems are discussed, such as using exponential weighted moving average control charts, smart decision-making with Pythagorean fuzzy sets in granular uncertainty, generalized uncertainty principles for Wigner–Ville distribution associated with the quaternion linear canonical transform, and fixed points in ordered metric spaces.

Offering a holistic view of the advancements in mathematics, finance, and technology, this volume will inspire new ideas and foster a deeper understanding of the intricate relationships that define these fields. By integrating advanced theoretical concepts with their practical applications, it offers a well-rounded understanding of the challenges and opportunities that characterize these fields today.