Advances in Statistical Modeling and Financial Machine Learning (Hardcover)

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.

Amir Ahmad Dar, PhD, is currently serving as an Assistant Professor in the Department of Statistics at Lovely Professional University in India. Dr. Dar has an impressive academic portfolio, having published over 20 research papers and 10 book chapters. He has presented his work at eight national and international conferences. His research interests include actuarial statistics, experimental design, financial derivatives, and financial mathematics. He completed his BSc in Actuarial and Financial Mathematics at the Islamic University of Science and Technology in Awantipora, India. He pursued his MSc and PhD in Actuarial Science at B S Abdur Rahman University in Chennai, India.

Mohammad Shahfaraz Khan, PhD is the Assistant Professor (Finance and Accounting) in the Department of Business Administration in the College of Economics and Business Administration at the University of Technology and Applied Sciences (UTAS) (formerly College of Applied Sciences, Salalah), Salalah, Sultanate of Oman. He earned his PhD from Aligarh Muslim University (AMU), India, and also did his bachelor’s (Commerce) and master’s in Finance & Control (MFC) at the same university. He is a gold medalist for his postgraduation (MFC) work and qualified national eligibility test for lecturership. He has published several papers in the field of finance and accounting, and his areas of interest in research is foreign direct investment (FDI), stock market, investment management, fintech, banking and finance, Islamic banking, and behavioral finance.

Naushad Alam, PhD, is currently working in the Department of Finance and Economics at Dhofar University, Oman. He earned his Master of Finance and Control and PhD in Finance from Aligarh Muslim University, India. He has more than 10 years of teaching and research experience and has published research paper in journals with impact factors. He formerly worked at the GL Bajaj Institute of Management Research, India, and at Saudi Electronic University, Saudi Arabia.

Olayan Albalawi, PhD, is an Assistant Professor in the Department of Statistics in the Faculty of Science at the University of Tabuk, Saudi Arabia. He holds a PhD in Applied Statistics from the University of New South Wales, Australia, as well as a Masters of Statistics from the University of New Mexico, USA. Additionally, he obtained a master’s degree in Mathematical Science from the University of Queensland of Technology. He serves as a reviewer for the King Saud of Science Journal.