FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4367187382> ?p ?o ?g. }

• W4367187382 endingPage "055003" @default.
• W4367187382 startingPage "055003" @default.
• W4367187382 abstract "Abstract Financial experts seek to predict the variability of financial markets to ensure investors’ successful investments. However, there has been a big trend in finance in the last few years, which are the ESG (Economic, Social and Governance) criteria, due to the growing importance of investments being socially responsible, and because of the financial impact companies suffer when not complying with them. Consequently, creating a stock portfolio should consider not only its financial performance but compliance with ESG criteria. Portfolio optimization (PO) techniques previously applied to ESG portfolios, are all closed-form analytical ones. But the real world is rather a black box with unknown analytical expressions. Thus, in this paper we use Bayesian optimization (BO), a sequential state-of-the-art design strategy to optimize black-boxes with unknown analytical and costly-to-compute expressions, to maximize the performance of a stock portfolio under the presence of ESG criteria soft constraints incorporated into the objective function. And we compare it to two other black-box techniques widely applied for the optimization of ‘conventional portfolios’ (non-ESG ones): the metaheuristics Genetic algorithm (GA) and Simulated Annealing (SA). Although BO has many theoretical advantages over GA and SA, it has never been applied to PO. Thus, this paper investigates whether BO can be used in the ESG PO framework as an alternative and compares it with GA and SA. This is the research gap to which this paper responds. To show the empirical performance of BO, we carry out four illustrative experiments and find evidence of BO outperforming the baselines. Thus we add another different optimization approach to the world of ESG investing: a black-box non-heuristic optimization approach through BO. Our study is the first paper that leverages BO and ESG scores into a PO technique. This paper opens the door to many new research lines in (ESG) portfolio optimization." @default.
• W4367187382 created "2023-04-28" @default.
• W4367187382 creator A5004622327 @default.
• W4367187382 creator A5044603645 @default.
• W4367187382 creator A5070783543 @default.
• W4367187382 date "2023-05-01" @default.
• W4367187382 modified "2023-09-27" @default.
• W4367187382 title "Bayesian optimization of ESG (Environmental Social Governance) financial investments" @default.
• W4367187382 cites W1830479465 @default.
• W4367187382 cites W1965538564 @default.
• W4367187382 cites W1972560205 @default.
• W4367187382 cites W1993612039 @default.
• W4367187382 cites W1996223377 @default.
• W4367187382 cites W2024060531 @default.
• W4367187382 cites W2089471883 @default.
• W4367187382 cites W2090822694 @default.
• W4367187382 cites W2113188547 @default.
• W4367187382 cites W2123948893 @default.
• W4367187382 cites W2138490239 @default.
• W4367187382 cites W2142640031 @default.
• W4367187382 cites W2145044449 @default.
• W4367187382 cites W2151237529 @default.
• W4367187382 cites W2153580489 @default.
• W4367187382 cites W2166935405 @default.
• W4367187382 cites W2168408730 @default.
• W4367187382 cites W2192203593 @default.
• W4367187382 cites W2275582525 @default.
• W4367187382 cites W2485431299 @default.
• W4367187382 cites W2765493925 @default.
• W4367187382 cites W2807716572 @default.
• W4367187382 cites W2808000920 @default.
• W4367187382 cites W2921666799 @default.
• W4367187382 cites W2956885731 @default.
• W4367187382 cites W3003825174 @default.
• W4367187382 cites W3005883791 @default.
• W4367187382 cites W3010291643 @default.
• W4367187382 cites W3122942322 @default.
• W4367187382 cites W3123236028 @default.
• W4367187382 cites W3123348301 @default.
• W4367187382 cites W3123538194 @default.
• W4367187382 cites W3123608331 @default.
• W4367187382 cites W3124020741 @default.
• W4367187382 cites W3125622054 @default.
• W4367187382 cites W3125969230 @default.
• W4367187382 cites W3128163159 @default.
• W4367187382 cites W3145489280 @default.
• W4367187382 cites W4205900444 @default.
• W4367187382 cites W4211049957 @default.
• W4367187382 cites W4286111085 @default.
• W4367187382 cites W4293912498 @default.
• W4367187382 cites W4294349391 @default.
• W4367187382 cites W4296816688 @default.
• W4367187382 cites W4311050663 @default.
• W4367187382 doi "https://doi.org/10.1088/2515-7620/acd0f8" @default.
• W4367187382 hasPublicationYear "2023" @default.
• W4367187382 type Work @default.
• W4367187382 citedByCount "0" @default.
• W4367187382 crossrefType "journal-article" @default.
• W4367187382 hasAuthorship W4367187382A5004622327 @default.
• W4367187382 hasAuthorship W4367187382A5044603645 @default.
• W4367187382 hasAuthorship W4367187382A5070783543 @default.
• W4367187382 hasBestOaLocation W43671873821 @default.
• W4367187382 hasConcept C10138342 @default.
• W4367187382 hasConcept C11413529 @default.
• W4367187382 hasConcept C119857082 @default.
• W4367187382 hasConcept C126980161 @default.
• W4367187382 hasConcept C127413603 @default.
• W4367187382 hasConcept C141261163 @default.
• W4367187382 hasConcept C144133560 @default.
• W4367187382 hasConcept C149782125 @default.
• W4367187382 hasConcept C154945302 @default.
• W4367187382 hasConcept C162324750 @default.
• W4367187382 hasConcept C18903297 @default.
• W4367187382 hasConcept C202655437 @default.
• W4367187382 hasConcept C204036174 @default.
• W4367187382 hasConcept C2778049539 @default.
• W4367187382 hasConcept C2780821815 @default.
• W4367187382 hasConcept C39389867 @default.
• W4367187382 hasConcept C41008148 @default.
• W4367187382 hasConcept C68781425 @default.
• W4367187382 hasConcept C78519656 @default.
• W4367187382 hasConcept C86803240 @default.
• W4367187382 hasConceptScore W4367187382C10138342 @default.
• W4367187382 hasConceptScore W4367187382C11413529 @default.
• W4367187382 hasConceptScore W4367187382C119857082 @default.
• W4367187382 hasConceptScore W4367187382C126980161 @default.
• W4367187382 hasConceptScore W4367187382C127413603 @default.
• W4367187382 hasConceptScore W4367187382C141261163 @default.
• W4367187382 hasConceptScore W4367187382C144133560 @default.
• W4367187382 hasConceptScore W4367187382C149782125 @default.
• W4367187382 hasConceptScore W4367187382C154945302 @default.
• W4367187382 hasConceptScore W4367187382C162324750 @default.
• W4367187382 hasConceptScore W4367187382C18903297 @default.
• W4367187382 hasConceptScore W4367187382C202655437 @default.
• W4367187382 hasConceptScore W4367187382C204036174 @default.
• W4367187382 hasConceptScore W4367187382C2778049539 @default.
• W4367187382 hasConceptScore W4367187382C2780821815 @default.
• W4367187382 hasConceptScore W4367187382C39389867 @default.

• next