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

Ameer Tamoor Khan, Xinwei Cao, Bolin Liao, Adam Francis

Biomimetics, Volume: 7, Issue: 3, Start page: 124

Swansea University Author: Adam Francis

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DOI (Published version): 10.3390/biomimetics7030124

Abstract

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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Published in: Biomimetics
ISSN: 2313-7673
Published: MDPI AG 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa60996
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spelling 2022-11-02T14:01:16.5201481 v2 60996 2022-08-31 Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem 8449248c17fec32f131097c0d1a768cc Adam Francis Adam Francis true false 2022-08-31 FGSEN 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. Journal Article Biomimetics 7 3 124 MDPI AG 2313-7673 multi-portfolio; optimization; swarm algorithm; beetle antennae search; stochastic algorithm; distributed beetle antennae search; investment; stocks 29 8 2022 2022-08-29 10.3390/biomimetics7030124 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University This work is supported by the National Natural Science Foundation of China under grant 61966014. 2022-11-02T14:01:16.5201481 2022-08-31T16:58:31.4301847 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Ameer Tamoor Khan 1 Xinwei Cao 2 Bolin Liao 3 Adam Francis 4 60996__25062__8042b58c6abd4d4d8b7a33b0feb8e486.pdf 60996_VoR.pdf 2022-08-31T17:01:27.1928056 Output 1532965 application/pdf Version of Record true © 2022 by the authors.This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license true eng https://creativecommons.org/licenses/by/4.0/
title Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem
spellingShingle Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem
Adam Francis
title_short 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_sort Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem
author_id_str_mv 8449248c17fec32f131097c0d1a768cc
author_id_fullname_str_mv 8449248c17fec32f131097c0d1a768cc_***_Adam Francis
author Adam Francis
author2 Ameer Tamoor Khan
Xinwei Cao
Bolin Liao
Adam Francis
format Journal article
container_title Biomimetics
container_volume 7
container_issue 3
container_start_page 124
publishDate 2022
institution Swansea University
issn 2313-7673
doi_str_mv 10.3390/biomimetics7030124
publisher MDPI AG
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
active_str 0
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.
published_date 2022-08-29T04:19:34Z
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score 11.012947

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