FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313251594> ?p ?o ?g. }

• W4313251594 endingPage "29" @default.
• W4313251594 startingPage "1" @default.
• W4313251594 abstract "ABSTRACTThis paper focuses on the computation issue of portfolio optimisation with scenario-based mean-Average Value at Risk (AVaR) in credibilistic environment. The portfolio optimisation problem is designed in two cases: risk taker model and risk-averse model. The main idea is to replace the portfolio selection models with linear programming (LP) problems. Since the computing time required for solving LP greatly depends on the dimension and the structure of the problem, the conventional numerical methods are usually less effective in real-time applications. One promising approach to handle online applications is to employ recurrent neural networks based on circuit implementation. Hence, according to the convex optimisation theory and some concepts of ordinary differential equations, a neural network model for solving the LP problems related to portfolio selection problems is presented. The equilibrium point of the proposed model is proved to be equivalent to the optimal solution of the original problem. It is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the portfolio selection problem with fuzzy returns. Some illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper.KEYWORDS: Fuzzy variablesportfolio selectionaverage value at riskcrisp equivalent programmingneural networkstabilityconvergent Disclosure statementNo potential conflict of interest was reported by the author(s)." @default.
• W4313251594 created "2023-01-06" @default.
• W4313251594 creator A5013864499 @default.
• W4313251594 creator A5016813528 @default.
• W4313251594 creator A5076420611 @default.
• W4313251594 date "2022-12-27" @default.
• W4313251594 modified "2023-09-27" @default.
• W4313251594 title "Mean-AVaR in credibilistic portfolio management via an artificial neural network scheme" @default.
• W4313251594 cites W1628915767 @default.
• W4313251594 cites W1964755988 @default.
• W4313251594 cites W1981086091 @default.
• W4313251594 cites W1982811442 @default.
• W4313251594 cites W1988901237 @default.
• W4313251594 cites W2003911542 @default.
• W4313251594 cites W2007903480 @default.
• W4313251594 cites W2008818950 @default.
• W4313251594 cites W2011474549 @default.
• W4313251594 cites W2013325881 @default.
• W4313251594 cites W2019291268 @default.
• W4313251594 cites W2030765498 @default.
• W4313251594 cites W2045395686 @default.
• W4313251594 cites W2048375654 @default.
• W4313251594 cites W2053734038 @default.
• W4313251594 cites W2065001413 @default.
• W4313251594 cites W2070721072 @default.
• W4313251594 cites W2081509626 @default.
• W4313251594 cites W2083128450 @default.
• W4313251594 cites W2092241327 @default.
• W4313251594 cites W2097113878 @default.
• W4313251594 cites W2127152800 @default.
• W4313251594 cites W2144305242 @default.
• W4313251594 cites W2162271322 @default.
• W4313251594 cites W2319520740 @default.
• W4313251594 cites W2477919182 @default.
• W4313251594 cites W2478866070 @default.
• W4313251594 cites W2570125889 @default.
• W4313251594 cites W2621025216 @default.
• W4313251594 cites W2751793745 @default.
• W4313251594 cites W2766543954 @default.
• W4313251594 cites W2790151569 @default.
• W4313251594 cites W2887974685 @default.
• W4313251594 cites W2888303819 @default.
• W4313251594 cites W2889351970 @default.
• W4313251594 cites W2889475612 @default.
• W4313251594 cites W2899431703 @default.
• W4313251594 cites W2901399869 @default.
• W4313251594 cites W2903922263 @default.
• W4313251594 cites W2937448857 @default.
• W4313251594 cites W2961570542 @default.
• W4313251594 cites W2975058239 @default.
• W4313251594 cites W2976379839 @default.
• W4313251594 cites W2985609539 @default.
• W4313251594 cites W2999397151 @default.
• W4313251594 cites W3000565563 @default.
• W4313251594 cites W3002523158 @default.
• W4313251594 cites W3033341699 @default.
• W4313251594 cites W3122662914 @default.
• W4313251594 cites W3123552065 @default.
• W4313251594 cites W3140974591 @default.
• W4313251594 cites W3145083929 @default.
• W4313251594 cites W3163396132 @default.
• W4313251594 cites W3168737497 @default.
• W4313251594 cites W3200525399 @default.
• W4313251594 doi "https://doi.org/10.1080/0952813x.2022.2153271" @default.
• W4313251594 hasPublicationYear "2022" @default.
• W4313251594 type Work @default.
• W4313251594 citedByCount "0" @default.
• W4313251594 crossrefType "journal-article" @default.
• W4313251594 hasAuthorship W4313251594A5013864499 @default.
• W4313251594 hasAuthorship W4313251594A5016813528 @default.
• W4313251594 hasAuthorship W4313251594A5076420611 @default.
• W4313251594 hasConcept C10138342 @default.
• W4313251594 hasConcept C126255220 @default.
• W4313251594 hasConcept C154945302 @default.
• W4313251594 hasConcept C162324750 @default.
• W4313251594 hasConcept C202444582 @default.
• W4313251594 hasConcept C2524010 @default.
• W4313251594 hasConcept C2780821815 @default.
• W4313251594 hasConcept C28719098 @default.
• W4313251594 hasConcept C33676613 @default.
• W4313251594 hasConcept C33923547 @default.
• W4313251594 hasConcept C41008148 @default.
• W4313251594 hasConcept C50644808 @default.
• W4313251594 hasConcept C58166 @default.
• W4313251594 hasConcept C81917197 @default.
• W4313251594 hasConceptScore W4313251594C10138342 @default.
• W4313251594 hasConceptScore W4313251594C126255220 @default.
• W4313251594 hasConceptScore W4313251594C154945302 @default.
• W4313251594 hasConceptScore W4313251594C162324750 @default.
• W4313251594 hasConceptScore W4313251594C202444582 @default.
• W4313251594 hasConceptScore W4313251594C2524010 @default.
• W4313251594 hasConceptScore W4313251594C2780821815 @default.
• W4313251594 hasConceptScore W4313251594C28719098 @default.
• W4313251594 hasConceptScore W4313251594C33676613 @default.
• W4313251594 hasConceptScore W4313251594C33923547 @default.
• W4313251594 hasConceptScore W4313251594C41008148 @default.
• W4313251594 hasConceptScore W4313251594C50644808 @default.
• W4313251594 hasConceptScore W4313251594C58166 @default.

• next