Soft Computing in Economics and Finance
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1 Ludmila Dymowa Soft Computing in Economics and Finance 4y Springer
2 1 Introduction 1 References 5 i 2 Applications of Modern Mathematics in Economics and Finance Fuzzy'Set Theory and Applied Interval Analysis in Economical and Financial Applications Economic and Financial Applications of Rough Sets Theory Artificial Neural Network-Based Applications in Economics and Finance Applications of Multiple Criteria Decision Making in Economics and Finance Summary and Discussion 29 References 30 3 The Methods for Uncertainty Modeling Fuzzy Set Theory Basic Definitions Operations on Fuzzy Sets Operations on Fuzzy Numbers Generalizations of Fuzzy Set Theory Interval Arithmetic Dempster-Shafer Theory of Evidence Basic Definitions Combination of Evidence in the Dempster-Shafer Theory 70
3 3.3.3 The Methods for Interval and Fuzzy Numbers Comparison Based on the Probabilistic Approach and Dempster-Shafer Theory Intuitionistic Fuzzy Sets in the Framework of Dempster-Shafer Theory Summary and Discussion 97 References 98 MCDM with Applications in Economics and Finance MCDM in the Fuzzy Setting Tool Steel Material Selection Problem Subsethood Measure for Linguistic Representation of Fuzzy Numbers Common Representation of Different Types of Local Criteria Probabilistic Method for Fuzzy Numbers Comparison Aggregation of Local Criteria and Aggregating 5 Modes Multiple Criteria Investment Project Evaluation in the Fuzzy Setting 137 4A1 Local Criteria Building Ranking the Local Criteria Numerical Evaluation of the Comparing Investment Projects Hierarchical Structure of Local Criteria Fuzzy MCDM and Optimization in the Stock Screening Multiple Criteria Performance of Firms General Criterion of Firm's "Health" Based on Financial Rations Two-Criteria Performance of Firm Based on Stocks Prices History The Comparison of Stocks Ranking Methods Stock Ranking with the Use of Multiple Criteria Optimization Multiple Criteria Fuzzy Evaluation and Optimization in Budgeting The Problem Formulation Fuzzy NPV and Risk Evaluation The Set of Crisp IRR Estimations Based on Fuzzy Cash Flows A Method for Numerical Solution of the Project Optimization Problem Summary and Discussion 178 References 179
4 XI 5 Interval and Fuzzy Arithmetic in Logistic Fuzzy Linear Programming Approach to the Distribution Planning Problem The Methods for the Solution of Fuzzy Linear Programming Problem The Direct Fuzzy Extension of the Simplex Method Numerical Studies Multiple Criteria Fuzzy Distribution Planning Problem The Problem Formulation The Solution of Multiple Criteria Fuzzy Distribution Problem Using the Aggregation of Aggregation Modes Summary and Discussion 202 References The Synthesis of Fuzzy Logic and DST in Stock Trading Decision Support Systems Stock Trading Systems Based on Conventional Fuzzy Logic Modern Approaches to Building Stock Trading "i Systems." Technical Analysis Indicators and Their Fuzzy Representation Stock Trading System Based on the Mamdani's Approach Expert System Based on Logic-Motivated Fuzzy Logic Operators Comparing the Trading Systems Based on Mamdani's Approach and Logic-Motivated Fuzzy Logic Operators The Stock Trading System Based on Fuzzy Logic and Evidential Reasoning Experts Systems Based on Rule-Base Evidential Reasoning A Modern Approach to the Rule-Base Evidential Reasoning Stock Trading Expert System Summary and Discussion 237 References Application of Interval and Fuzzy Analysis in Economic Modeling Basics of "Interval Zero Extension" Method The Problem Formulation Solution Linear Fuzzy Equations 255
5 XII Contents 7.2 Solving Interval Linear Systems and the Interval Leontiev's Input-Output Problem Solving Systems of Interval Linear Equations Application to the Interval Leontief'S Input-Output Model of Economics Solving Nonlinear Interval and Fuzzy Equations Fuzzy Internal Rate of Return in Budgeting The Problem Formulation Fuzzy Internal Rate of Return for Crisp Interval Cash Flows. Basics Numerical Solution of the Nonlinear Fuzzy Problem of Internal Rate of Return Calculation Possible Applications Summary and Discussion 287 References 288 Index 293
$1,300 + 1,500 + 1,900 = $4,700. in cash flows. The project still needs to create another: $5,500 4,700 = $800
1. To calculate the payback period, we need to find the time that the project has recovered its initial investment. After three years, the project has created: $1,300 + 1,500 + 1,900 = $4,700 in cash flows.
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