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The rapid changes that have taken place globally on the economic, social and business fronts characterized the 20th century. The magnitude of these changes has formed an extremely complex and unpredictable decision-making framework, which is difficult to model through traditional approaches. The main purpose of this book is to present the most recent advances in the development of innovative techniques for managing the uncertainty that prevails in the global economic and management environments. These techniques originate mainly from fuzzy sets theory. However, the book also explores the integration of fuzzy sets with other decision support and modeling disciplines, such as multicriteria decision aid, neural networks, genetic algorithms, machine learning, chaos theory, etc. The presentation of the advances in these fields and their real world applications adds a new perspective to the broad fields of management science and economics. Contents: Decision Making, Management and Marketing: Algorithms for Orderly Structuring of Financial OC ObjectsOCO (J Gil-Aluja); A Fuzzy Goal Programming Model for Evaluating a Hospital Service Performance (M Arenas et al.); A Group Decision Making Method Using Fuzzy Triangular Numbers (J L Garc a-Lapresta et al.); Developing Sorting Models Using Preference Disaggregation Analysis: An Experimental Investigation (M Doumpos & C Zopounidis); Stock Markets and Portfolio Management: The Causality Between Interest Rate, Exchange Rate and Stock Price in Emerging Markets: The Case of the Jakarta Stock Exchange (J Gupta et al.); Fuzzy Cognitive Maps in Stock Market (D Koulouriotis et al.); Neural Network vs Linear Models of Stock Returns: An Application to the UK and German Stock Market Indices (A Kanas); Corporate Finance and Banking Management: Expertons and Behaviour of Companies with Regard to the Adequacy Between Business Decisions and Objectives (A Couturier & B Fioleau); Multiple Fuzzy IRR in the Financial Decision Environment (S F Gonzilez et al.); An Automated Knowledge Generation Approach for Managing Credit Scoring Problems (M Michalopoulos et al.); and other papers. Readership: Financial managers, economists, management scientists and computer scientists."
This study reviews the problem of the individual investor and applies to it a methodology based on fuzzy sets and the theory of possibility. The investment decision is characterized by uncertainty, imprecision and complexity, which lessen the effectiveness of conventional calculus and probability tools. In contrast, fuzzy set theory and its modeling language provide objects of analysis and algebra that are well suited to this problem. New concepts such as quot;fuzzy portfolio weightsquot; are introduced. The result of our research is a qualitative, general, and practical model for individual investors' decision making, which is based on Smith's (1974) asset-mix model.
First published in 1998, this volume was designed to lead to an operational model of Advanced Manufacturing Technology (AMT) decision making which incorporated the mathematics of fuzzy set theory. The rapid advancement of robotics, automated technologies and software such as CAD and CAM have made such studies paramount. Here, analyses of a questionnaire survey and field study of major UK manufacturing companies together provide a simulating portrayal of AMT investment decision making and have been expanded upon with a model using fuzzy set theory.
Fuzzy set approaches are suitable to use when the modeling of human knowledge is necessary and when human evaluations are needed. Fuzzy set theory is recognized as an important problem modeling and solution technique. It has been studied ext- sively over the past 40 years. Most of the early interest in fuzzy set theory pertained to representing uncertainty in human cognitive processes. Fuzzy set theory is now - plied to problems in engineering, business, medical and related health sciences, and the natural sciences. This book handles the fuzzy cases of classical engineering e- nomics topics. It contains 15 original research and application chapters including different topics of fuzzy engineering economics. When no probabilities are available for states of nature, decisions are given under uncertainty. Fuzzy sets are a good tool for the operation research analyst facing unc- tainty and subjectivity. The main purpose of the first chapter is to present the role and importance of fuzzy sets in the economic decision making problem with the literature review of the most recent advances.
This book describes five qualitative investment decision-making methods based on the hesitant fuzzy information. They are: (1) the investment decision-making method based on the asymmetric hesitant fuzzy sigmoid preference relations, (2) the investment decision-making method based on the hesitant fuzzy trade-off and portfolio selection, (3) the investment decision-making method based on the hesitant fuzzy preference envelopment analysis, (4) the investment decision-making method based on the hesitant fuzzy peer-evaluation and strategy fusion, and (5) the investment decision-making method based on the EHVaR measurement and tail analysis.
The book proposes techniques, with an emphasis on the financial sector, which will make recommendation systems both accurate and explainable. The vast majority of AI models work like black box models. However, in many applications, e.g., medical diagnosis or venture capital investment recommendations, it is essential to explain the rationale behind AI systems decisions or recommendations. Therefore, the development of artificial intelligence cannot ignore the need for interpretable, transparent, and explainable models. First, the main idea of the explainable recommenders is outlined within the background of neuro-fuzzy systems. In turn, various novel recommenders are proposed, each characterized by achieving high accuracy with a reasonable number of interpretable fuzzy rules. The main part of the book is devoted to a very challenging problem of stock market recommendations. An original concept of the explainable recommender, based on patterns from previous transactions, is developed; it recommends stocks that fit the strategy of investors, and its recommendations are explainable for investment advisers.
Fuzzy Logic is an analytical tool used in the modeling of those phenomena that fall outside the scope of exact sciences. It is used in the analysis of complex and highly nonlinear processes, where mathematical models or standard classic logic cannot define conditions inherent to such processes, e.g. human thinking. Kurt Peray's detailed analysis of the new approaches and techniques for Risk Control and Portfolio Asset Allocation - which uses the principles of Fuzzy Logic - helps you to make decisions as to when to buy, hold or sell. While making independent and educated decisions, you will be able to hedge your portfolio from the volatile forces in the market, and will offset the erosive impact of inflation and taxation. In this electronic age, investors have quick access to important information relevant to the decision process. The guidelines and formulas that serve as foundations to the Fuzzy Logic approach gives you the ability to build customized programs. Investing in Mutual Funds Using Fuzzy Logic is for the individual who wants to invest in financial instruments that will provide a return for growth. With the investment approach he devised, Peray guides the you towards achieving your investment goals.
This book presents a range of investment appraisal methods and models to help readers make good investment decisions. Each approach is thoroughly described, evaluated, and illustrated using examples, with its assumptions and limitations analyzed in terms of their implications for investment decision-making practice. Getting investment decisions right is crucial but due to a complex and dynamic business environment this remains a challenging management task.