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

Ameer Tamoor Khan1, Xinwei Cao2, Bolin Liao3

  • 1Department of Computing, The Hong Kong Polytechnic University, Hong Kong 999077, China.

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|September 22, 2022
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Summary
This summary is machine-generated.

A new Distributed Beetle Antennae Search (DBAS) algorithm optimizes multi-portfolio selection while protecting client privacy. This swarm-based method shares only gradients, not sensitive stock data, ensuring secure and efficient portfolio optimization.

Keywords:
beetle antennae searchdistributed beetle antennae searchinvestmentmulti-portfoliooptimizationstochastic algorithmstocksswarm algorithm

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Area of Science:

  • Computational Finance
  • Optimization Algorithms
  • Data Privacy

Background:

  • The multi-portfolio selection problem is an emerging area lacking privacy frameworks.
  • Client privacy and database secrecy are critical concerns in financial optimization.
  • Existing methods do not address the privacy of individual portfolios or stock data.

Purpose of the Study:

  • To propose a novel optimization framework for multi-portfolio selection that preserves privacy.
  • To introduce the Distributed Beetle Antennae Search (DBAS) algorithm for secure financial optimization.
  • To ensure the confidentiality of individual portfolios and underlying stock information.

Main Methods:

  • Developed Distributed Beetle Antennae Search (DBAS), a hybrid swarm-based optimization algorithm.
  • DBAS combines Particle Swarm Optimization (PSO) principles with Beetle Antennae Search (BAS) updating criteria.
  • The algorithm shares only portfolio gradients, not private data or stock details, among the swarm.

Main Results:

  • Simulations involved four categories of multi-portfolio problems with three portfolios each.
  • Real-world stock data from 25 NASDAQ companies over 200 days were used.
  • DBAS demonstrated efficiency and robustness in optimizing portfolios while maintaining privacy.

Conclusions:

  • DBAS effectively optimizes multi-portfolio selection problems without compromising privacy.
  • The algorithm provides a secure framework for financial optimization, safeguarding sensitive data.
  • DBAS is a robust and efficient solution for the privacy-preserving multi-portfolio selection challenge.