Using Machine Learning Techniques to Assess the Financial Impact of the COVID-19 Pandemic on the Global Aviation Industry - Research overview

Aug 5, 2026

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Prediction of financial distress is a crucial concern for decision-makers, especially in industries prone to external shocks, such as the aviation sector. This study applies machine learning models to a comprehensive global dataset of aviation companies to build accurate financial distress prediction tools. The work supports stakeholders’ decision-making in navigating the aviation industry’s challenges, highlighted by the COVID-19 pandemic. It develops non-parametric, data-driven solutions and performs comparative evaluations of prediction models.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

Study approach and modeling goals

Industry context and pandemic relevance

Data-driven predictive methods

Bond University Research Repository Using Machine Learning Techniques to Assess the Financial Impact of the COVID-19 Pandemic on the Global Aviation Industry Halteh, Khaled; AlKhoury, Ritab; Adek Ziadat, Salem ; Gepp, Adrian; Kumar, Kuldeep Published in: Transportation Research Interdisciplinary Perspectives DOI: [https://doi.org/10.1016/j.trip.2024.101043](https://doi.org/10.1016/j.trip.2024.101043) Licence: CC BY Link to output in Bond University research repository. Recommended citation(APA): Halteh, K. , AlKhoury, R. , Adek Ziadat, S. , Gepp, A. , & Kumar, K. (2024) . Using Machine Learning Techniques to Assess the Financial Impact of the COVID-19 Pandemic on the Global Aviation Industry. Transportation Research Interdisciplinary Perspectives, 24, 1-10 . Article 101043. [https://doi.org/10.1016/j.trip.2024.101043](https://doi.org/10.1016/j.trip.2024.101043) General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. For more information, or if you believe that this document breaches copyright, please contact the Bond University research repository coordinator. Download date: 18 May 2024 Transportation Research Interdisciplinary Perspectives 24 (2024) 101043 Contents lists available at ScienceDirect Transportation Research Interdisciplinary Perspectives journal [homepage:](homepage: www.sciencedirect.com/journal/transportation)[ www.sciencedirect.com/journal/transportation](homepage: www.sciencedirect.com/journal/transportation)research-interdisciplinary-perspectives | Using machine learning techniques to assess the financial impact of the COVID-19 pandemic on the global aviation industry
Khaled Halteha, *, Ritab AlKhourya, Salem Adel Ziadata, Adrian Gepp b, Kuldeep Kumar b
a Al-Ahliyya Amman University, Al-Saro, Al-Salt, Jordan
b Bond University, 14 University Dr, Robina, QLD, 4226, Australia | | | | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | JEL Codes: G010
R400
Keywords:
Aviation industry Financial distress prediction Machine learning COVID-19 | | Prediction of financial distress is a crucial concern for decision-makers, especially in industries prone to external shocks, such as the aviation sector. This study employs machine learning techniques on a comprehensive global dataset of aviation companies to develop highly accurate financial distress prediction models. These models empower stakeholders with informed decision-making capabilities to navigate the aviation industry’s challenges, most notably exemplified by the COVID-19 pandemic. The aviation industry holds substantial economic importance, contributing significantly to revenue, employment, and economic activity worldwide. However, its susceptibility to external factors underscores the need for robust predictive tools. Leveraging advances in machine learning, this study pioneers the application of data-driven, non-parametric solutions to the aviation sector, both before and after the pandemic. Importantly, this study addresses a gap in the field by conducting comparative evaluations of prediction models, which have been lacking in previous research efforts, often leading to inconclusive outcomes. Key findings of the study highlight the Random Forest and Stochastic Gradient Boosting models as the most accurate in forecasting financial distress within the aviation industry. Notably, the study identifies debt-to-equity, return on invested capital, and debt ratio as the most important predictors of financial distress in this context. | 1. Introduction Accurately forecasting financial distress is of utmost significance for policymakers and investors alike. To make effective policy decisions, it is essential to undertake a careful analysis of the relevant

FAQs

What problem does the study address?

The study targets accurate prediction of financial distress in the aviation industry, particularly under external shocks such as the COVID-19 pandemic.

Which machine learning methods are used to build the prediction models?

The study leverages decision trees, random forests, and stochastic gradient boosting to develop financial distress prediction models.

What predictors and models show the strongest performance in forecasting distress?

Random Forest and Stochastic Gradient Boosting are reported as the most accurate, and debt-to-equity, return on invested capital, and debt ratio are identified as key predictors.

Aug 5, 2026

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