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Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem
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...
| Published in: | Biomimetics |
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| ISSN: | 2313-7673 |
| Published: |
MDPI AG
2022
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa60996 |
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| 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 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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| Keywords: |
multi-portfolio; optimization; swarm algorithm; beetle antennae search; stochastic algorithm; distributed beetle antennae search; investment; stocks |
| College: |
Faculty of Science and Engineering |
| Funders: |
This work is supported by the National Natural Science Foundation of China under grant 61966014. |
| Issue: |
3 |
| Start Page: |
124 |