Wang, GWY, Yang, Z, Zhang, D, Huang, A and Yang, Z (2017) Application of Bayesian networks in analysing tanker shipping bankruptcy risks. Maritime Business Review, 2 (3). ISSN 2397-3757

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Abstract

Purpose: This study aims to develop an assessment methodology using a Bayesian network (BN) to predict the failure probability of oil tanker shipping firms.
Design/methodology/approach: This paper proposes a bankruptcy prediction model by applying the hybrid of logistic regression and Bayesian probabilistic networks.
Findings: The proposed model shows its potential of contributing to a powerful tool to predict financial bankruptcy of shipping operators, and provides important insights to the maritime community as to what performance measures should be taken to ensure the shipping companies’ financial soundness under dynamic environments.
Research limitations/implications: The model and its associated variables can be expanded to include more factors for an in-depth analysis in future when the detailed information at firm level becomes available.
Practical implications: The results of this study can be implemented to oil tanker shipping firms as a prediction tool for bankruptcy rate.
Originality/value: Incorporating quantitative statistical measurement, the application of BN in financial risk management provides advantages to develop a powerful early warning system in shipping, which has unique characteristics such as capital intensive and mobile assets, possibly leading to catastrophic consequences.

Item Type:	Article
Additional Information:	The AAM is deposited under the above licence and any reuse is allowed in accordance with the terms outlined by the licence. To reuse the AAM for commercial purposes, permission should be sought by contacting permissions@emeraldinsight.com.
Subjects:	H Social Sciences > HD Industries. Land use. Labor H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management H Social Sciences > HE Transportation and Communications
Divisions:	Maritime & Mechanical Engineering (merged with Engineering 10 Aug 20)
Publisher:	Emerald
Date Deposited:	17 Feb 2020 11:45
Last Modified:	04 Sep 2021 07:53
DOI or ID number:	10.1108/mabr-12-2016-0032
URI:	https://researchonline.ljmu.ac.uk/id/eprint/12260

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