This book provides a new modeling approach for portfolio optimization problems involving a lack of sufficient historical data. The content mainly reflects the author’s extensive work on uncertainty portfolio optimization in recent years. Considering security returns as different variables, the book presents a series of portfolio optimization models in the framework of credibility theory, uncertainty theory and chance theory, respectively. As such, it offers readers a comprehensive and up-to-date guide to uncertain portfolio optimization models.

A detailed look at equity valuation and portfolio management Equity valuation is a method of valuing stock prices using fundamental analysis to determine the worth of the business and discover investment opportunities. In Equity Valuation and Portfolio Management Frank J. Fabozzi and Harry M. Markowitz explain the process of equity valuation, provide the necessary mathematical background, and discuss classic and new portfolio strategies for investment managers. Divided into two comprehensive parts, this reliable resource focuses on valuation and portfolio strategies related to equities. Discusses both fundamental and new techniques for valuation and strategies Fabozzi and Markowitz are experts in the fields of investment management and economics Includes end of chapter bullet point summaries, key chapter take-aways, and study questions Filled with in-depth insights and practical advice, Equity Valuation and Portfolio Management will put you in a better position to excel at this challenging endeavor.

Valuable insights on the major methods used in today's asset and risk management arena Risk management has moved to the forefront of asset management since the credit crisis. However, most coverage of this subject is overly complicated, misunderstood, and extremely hard to apply. That's why Steven Greiner—a financial professional with over twenty years of quantitative and modeling experience—has written Investment Risk and Uncertainty. With this book, he skillfully reduces the complexity of risk management methodologies applied across many asset classes through practical examples of when to use what. Along the way, Greiner explores how particular methods can lower risk and mitigate losses. He also discusses how to stress test your portfolio and remove the exposure to regular risks and those from "Black Swan" events. More than just an explanation of specific risk issues, this reliable resource provides practical "off-the-shelf" applications that will allow the intelligent investor to understand their risks, their sources, and how to hedge those risks. Covers modern methods applied in risk management for many different asset classes Details the risk measurements of truly multi-asset class portfolios, while bridging the gap for managers in various disciplines—from equity and fixed income investors to currency and commodity investors Examines risk management algorithms for multi-asset class managers as well as risk managers, addressing new compliance issues and how to meet them The theory of risk management is hardly ever spelled out in practical applications that portfolio managers, pension fund advisors, and consultants can make use of. This book fills that void and will put you in a better position to confidently face the investment risks and uncertainties found in today's dynamic markets.

Most of the existing portfolio selection models are based on the probability theory. Though they often deal with the uncertainty via probabilistic - proaches, we have to mention that the probabilistic approaches only partly capture the reality. Some other techniques have also been applied to handle the uncertainty of the ?nancial markets, for instance, the fuzzy set theory [Zadeh (1965)]. In reality, many events with fuzziness are characterized by probabilistic approaches, although they are not random events. The fuzzy set theory has been widely used to solve many practical problems, including ?nancial risk management. By using fuzzy mathematical approaches, quan- tative analysis, qualitative analysis, the experts’ knowledge and the investors’ subjective opinions can be better integrated into a portfolio selection model. The contents of this book mainly comprise of the authors’ research results for fuzzy portfolio selection problems in recent years. In addition, in the book, the authors will also introduce some other important progress in the ?eld of fuzzy portfolio optimization. Some fundamental issues and problems of po- folioselectionhavebeenstudiedsystematicallyandextensivelybytheauthors to apply fuzzy systems theory and optimization methods. A new framework for investment analysis is presented in this book. A series of portfolio sel- tion models are given and some of them might be more e?cient for practical applications. Some application examples are given to illustrate these models by using real data from the Chinese securities markets.

This book presents select proceedings of the International Conference on Applied Mathematics in Science and Engineering (AMSE 2019). Various topics covered include computational fluid dynamics, applications of differential equations in engineering, numerical methods for ODEs and PDEs, mathematical modeling and analysis of biological systems, optimal control and controllability of differential equations, fractional calculus and its applications, nonlinear analysis, and functional analysis. This book will be of interest to researchers, academicians and students in the fields of applied sciences, mathematics and engineering.

Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization The finance industry is seeing increased interest in new risk measures and techniques for portfolio optimization when parameters of the model are uncertain. This groundbreaking book extends traditional approaches of risk measurement and portfolio optimization by combining distributional models with risk or performance measures into one framework. Throughout these pages, the expert authors explain the fundamentals of probability metrics, outline new approaches to portfolio optimization, and discuss a variety of essential risk measures. Using numerous examples, they illustrate a range of applications to optimal portfolio choice and risk theory, as well as applications to the area of computational finance that may be useful to financial engineers. They also clearly show how stochastic models, risk assessment, and optimization are essential to mastering risk, uncertainty, and performance measurement. Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization provides quantitative portfolio managers (including hedge fund managers), financial engineers, consultants, and academic researchers with answers to the key question of which risk measure is best for any given problem.

This book compiles recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The book is motivated by the fact that some degree of uncertainty is inevitable in characterizing any realistic engineering systems. Discussion includes representative methods for addressing major sources of uncertainties in evolutionary computation, including handle of noisy fitness functions, use of approximate fitness functions, search for robust solutions, and tracking moving optimums.

This book explores the current state of the art in quantitative investment management across seven key areas. Chapters by academics and practitioners working in leading investment management organizations bring together major theoretical and practical aspects of the field.

This book presents peer-reviewed articles and recent advances on the potential applications of Science and Mathematics for future technologies, from the 8th International Conference on the Applications of Science and Mathematics (SCIEMATHIC 2022), held in Malaysia. It provides an insight about the leading trends in sustainable Science and Technology. Topics included in this proceedings are in the areas of Mathematics and Statistics, including Natural Science, Engineering and Artificial Intelligence.

This book brings together the latest findings on efficient solutions of multi/many-objective optimization problems from the leading researchers in the field. The focus is on solving real-world optimization problems using strategies ranging from evolutionary to hybrid frameworks, and involving various computation platforms. The topics covered include solution frameworks using evolutionary to hybrid models in application areas like Analytics, Cancer Research, Traffic Management, Networks and Communications, E-Governance, Quantum Technology, Image Processing, etc. As such, the book offers a valuable resource for all postgraduate students and researchers interested in exploring solution frameworks for multi/many-objective optimization problems.