Focusing on multicriteria decision aid, this book presents a novel approach to the classification problem. It reviews existing research on classification method development, examining model procedures and analyzing performance in both experimental and real-world financial scenarios. This comprehensive analysis aims to enhance decision support systems and improve classification techniques in practical applications.
Exploring the intersection of financial engineering and multicriteria intelligent decision aiding systems, this book reviews current research and evaluates the effectiveness of innovative tools designed to tackle complex financial decision-making challenges. It offers a fresh perspective on how these systems can enhance problem-solving in the financial sector, making it a valuable resource for professionals and researchers alike.
Focusing on the fundamentals and techniques of multiple criteria decision making in finance, this book explores the intricacies of financial decisions, financial modeling, risk management, and financial engineering. It covers essential topics such as portfolio management, banking management, and credit scoring. Additionally, the text provides insights into further applications of multi-criteria analysis in finance and discusses future perspectives for its use in the sector.
Financial globalization has increased the significance of methods used in the evaluation of country risk, one of the major research topics in economics and finance. Written by experts in the fields of multicriteria methodology, credit risk assessment, operations research, and financial management, this book develops a comprehensive framework for evaluating models based on several classification techniques that emerge from different theoretical directions. This book compares different statistical and data mining techniques, noting the advantages of each method, and introduces new multicriteria methodologies that are important to country risk modeling. This work is a useful toolkit for economists, financial managers, bank managers, operations researchers, management scientists, and risk analysts. Moreover, the book can also be used as a supplementary textbook for graduate courses in finance and financial risk management.
This book explores recent advances in robustness analysis within decision aiding, optimization, and analytics. It illustrates the challenges robustness presents in various operations research and management science (OR/MS) contexts, alongside methodologies proposed from diverse perspectives. In addition to methodological developments, it showcases applications of robust techniques in engineering and management, highlighting real-world robustness issues and their solutions through OR/MS advancements. Robustness analysis aims to promote solutions acceptable under a wide range of hypotheses, assumptions, and estimates. Traditionally, robustness in OR/MS has focused on optimization under uncertainty, but scholars have expanded this view to encompass the broader decision support nature of OR/MS methodologies. As new challenges arise in the "big data" era, characterized by rapid information flow and complexity, the relevance of robustness issues increases. These challenges underscore the importance of providing sound decision support through advanced analytic tools, making the insights presented in this volume particularly timely and essential for strategic and operational decision-making on a global scale.