Credit portfolio management using two-level particle swarm optimization

Fu Qiang Lu, Min Huang*, Wai Ki Ching, Tak Kuen Siu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

In this paper, we propose a novel Two-level Particle Swarm Optimization (TLPSO) to solve the credit portfolio management problem. A two-date credit portfolio management model is considered. The objective of the manager is to minimize the maximum expected loss of the portfolio subject to a given consulting budget constraint. The captured problem is very challenging due to its hierarchical structure and its time complexity, so the TLPSO is designed for the credit portfolio management model. The TLPSO has two searching processes, namely, "internal-search", the searching process of the maximization problem and "external-search", the searching process of the minimization problem. The performance of TLPSO is then compared with both the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO), in terms of efficient frontiers, fitness values, convergence rates, computational time consumption and reliability. The experiment results show that TLPSO is more efficient and reliable for the credit portfolio management problem than the other tested methods.

Original languageEnglish
Pages (from-to)162-175
Number of pages14
JournalInformation Sciences
Volume237
DOIs
Publication statusPublished - 10 Jul 2013

Fingerprint Dive into the research topics of 'Credit portfolio management using two-level particle swarm optimization'. Together they form a unique fingerprint.

Cite this