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Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem

The recently emerging multi-portfolio selection problem lacks a proper framework to ensure that client privacy and database secrecy remain intact. Since privacy is of major concern these days, in this paper, we propose a variant of Beetle Antennae Search (BAS) known as Distributed Beetle Antennae Se...

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Autores principales: Khan, Ameer Tamoor, Cao, Xinwei, Liao, Bolin, Francis, Adam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9496093/
https://www.ncbi.nlm.nih.gov/pubmed/36134927
http://dx.doi.org/10.3390/biomimetics7030124
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author Khan, Ameer Tamoor
Cao, Xinwei
Liao, Bolin
Francis, Adam
author_facet Khan, Ameer Tamoor
Cao, Xinwei
Liao, Bolin
Francis, Adam
author_sort Khan, Ameer Tamoor
collection PubMed
description The recently emerging multi-portfolio selection problem lacks a proper framework to ensure that client privacy and database secrecy remain intact. Since privacy is of major concern these days, in this paper, we propose a variant of Beetle Antennae Search (BAS) known as Distributed Beetle Antennae Search (DBAS) to optimize multi-portfolio selection problems without violating the privacy of individual portfolios. DBAS is a swarm-based optimization algorithm that solely shares the gradients of portfolios among the swarm without sharing private data or portfolio stock information. DBAS is a hybrid framework, and it inherits the swarm-like nature of the Particle Swarm Optimization (PSO) algorithm with the BAS updating criteria. It ensures a robust and fast optimization of the multi-portfolio selection problem whilst keeping the privacy and secrecy of each portfolio intact. Since multi-portfolio selection problems are a recent direction for the field, no work has been done concerning the privacy of the database nor the privacy of stock information of individual portfolios. To test the robustness of DBAS, simulations were conducted consisting of four categories of multi-portfolio problems, where in each category, three portfolios were selected. To achieve this, 200 days worth of real-world stock data were utilized from 25 NASDAQ stock companies. The simulation results prove that DBAS not only ensures portfolio privacy but is also efficient and robust in selecting optimal portfolios.
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spelling pubmed-94960932022-09-23 Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem Khan, Ameer Tamoor Cao, Xinwei Liao, Bolin Francis, Adam Biomimetics (Basel) Article The recently emerging multi-portfolio selection problem lacks a proper framework to ensure that client privacy and database secrecy remain intact. Since privacy is of major concern these days, in this paper, we propose a variant of Beetle Antennae Search (BAS) known as Distributed Beetle Antennae Search (DBAS) to optimize multi-portfolio selection problems without violating the privacy of individual portfolios. DBAS is a swarm-based optimization algorithm that solely shares the gradients of portfolios among the swarm without sharing private data or portfolio stock information. DBAS is a hybrid framework, and it inherits the swarm-like nature of the Particle Swarm Optimization (PSO) algorithm with the BAS updating criteria. It ensures a robust and fast optimization of the multi-portfolio selection problem whilst keeping the privacy and secrecy of each portfolio intact. Since multi-portfolio selection problems are a recent direction for the field, no work has been done concerning the privacy of the database nor the privacy of stock information of individual portfolios. To test the robustness of DBAS, simulations were conducted consisting of four categories of multi-portfolio problems, where in each category, three portfolios were selected. To achieve this, 200 days worth of real-world stock data were utilized from 25 NASDAQ stock companies. The simulation results prove that DBAS not only ensures portfolio privacy but is also efficient and robust in selecting optimal portfolios. MDPI 2022-08-29 /pmc/articles/PMC9496093/ /pubmed/36134927 http://dx.doi.org/10.3390/biomimetics7030124 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Khan, Ameer Tamoor
Cao, Xinwei
Liao, Bolin
Francis, Adam
Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem
title Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem
title_full Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem
title_fullStr Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem
title_full_unstemmed Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem
title_short Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem
title_sort bio-inspired machine learning for distributed confidential multi-portfolio selection problem
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9496093/
https://www.ncbi.nlm.nih.gov/pubmed/36134927
http://dx.doi.org/10.3390/biomimetics7030124
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