Applying fuzzy logic for the digital economy and society. ([2019])

• Record Type: Book
• Further Information: Note: Andreas Meier, [and 2 others], editors.
• Editors: Meier, Andreas
• Contents: Intro; Preface; Contents; Contributors; 1 Testing Hypotheses by Fuzzy Methods: A Comparison with the Classical Approach; 1.1 Introduction and Motivation; 1.2 Definitions and Notations; 1.3 Testing Hypotheses by the Classical Approach; 1.4 Testing Hypotheses by the Fuzzy Approach; 1.4.1 The Fuzzy Tests by Confidence Intervals; 1.4.2 The Fuzzy p-Value; 1.5 Defuzzification of the Fuzzy Decisions by the Signed Distance; 1.5.1 The Signed Distance; 1.5.2 The Defuzzified Decision by Confidence Intervals; 1.5.3 The Defuzzified p-Value; 1.6 Tests of the Mean: Numerical Examples with Real Data 1.6.1 The Setups1.6.2 The Tests; 1.7 Classical Approach vs. Fuzzy Approach; 1.8 Recommendations of Use; 1.9 Conclusion; References; 2 Interpolative Boolean Approach for Fuzzy Portfolio Selection; 2.1 Introduction; 2.2 Fuzzy Methods in Portfolio Selection; 2.3 Theoretical Background: Interpolative Boolean Algebra; 2.3.1 Symbolic and Value Level of IBA; 2.3.2 Logical Aggregation; 2.3.3 IBA Dissimilarity Measure; 2.4 Logical Clustering for Portfolio Selection; 2.4.1 Logical Clustering; 2.4.2 Stock Selection Problem; 2.4.3 Results and Discussion; 2.5 Logical DuPont Method 2.5.1 Logical DuPont Method for Portfolio Selection2.5.2 Data and Parameter Settings; 2.5.3 Results and Discussion; 2.6 Conclusion; References; 3 A Fuzzy-Based Discounts Recommender System for Public Tax Payment; 3.1 Introduction; 3.2 Background; 3.2.1 Tax Payments in Ecuador; 3.2.2 RSs for eCommerce and eGovernment; 3.2.3 Fuzzy Logic Overview; 3.2.4 Fuzzy Logic Applied to Marketing; 3.3 Fuzzy-Based RS Model; 3.3.1 Fuzzy Sets; 3.3.1.1 General Discount; 3.3.1.2 Specific Discount; 3.3.1.3 Recommendation Notification Policies; 3.3.2 System Architecture; 3.3.2.1 Message Handler 3.3.2.2 Request Validation3.3.2.3 Fuzzy Recommender System; 3.3.3 Recommendation Computation Process; 3.4 Model Simulation; 3.4.1 Dataset Acquisition; 3.4.2 Simulation Design; 3.4.3 Outcomes; 3.5 Conclusions, Limitations, and Future Work; References; 4 Fuzzy Based Investment Portfolio Management; 4.1 Introduction; 4.2 State-of-the-Art Fuzzy Logic in Finance; 4.2.1 Fuzzy Logic for Technical Analysis; 4.2.2 Rate of Return Forecasting Through Information Using Rule Extraction; 4.2.3 Use of Fuzzy Time Series in Investment Analysis and Forecasting 4.3 Portfolio Selection and Evaluation Through Fuzzy Based Systems4.3.1 Volatility Forecasting Through Self Optimal and Fuzzy C-Means Clustering Techniques; 4.3.1.1 Fuzzy C-Means Clustering; 4.3.1.2 Self Optimal Clustering; 4.3.2 Fuzzy Decision Based Trading Transaction Costs Modeling; 4.3.3 Fuzzy Based Asset Selection Through Technical Indicator; 4.3.4 MIMO Fuzzy Modeling Based Interest Rate Forecasting; 4.4 Portfolio Functioning and Optimization Through Fuzzy Based Systems; 4.4.1 Neuro Fuzzy System Based Portfolio Functioning; 4.4.2 Fuzzy Decision Support System For Portfolio Functioning
• Publisher Details: Cham, Switzerland : Springer
• Publication Date: 2019
• Extent: 1 online resource
• Subjects: 332.6; Portfolio management; Fuzzy logic; Decision making; BUSINESS & ECONOMICS / Finance; Electronic books; Electronic books
• Languages: English
• ISBNs: 9783030033682; 3030033686
• Related ISBNs: 3030033678; 9783030033675
• Notes: Note: Includes bibliographical references.; Note: Description based on online resource; title from digital title page (viewed on March 14, 2019).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
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