Using machine learning techniques to assess the financial impact of the COVID-19 pandemic on the global aviation industry
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 distr...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Transportation Research Interdisciplinary Perspectives |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590198224000290 |
| _version_ | 1797213335278583808 |
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| author | Khaled Halteh Ritab AlKhoury Salem Adel Ziadat Adrian Gepp Kuldeep Kumar |
| author_facet | Khaled Halteh Ritab AlKhoury Salem Adel Ziadat Adrian Gepp Kuldeep Kumar |
| author_sort | Khaled Halteh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T00:09:25Z |
| format | Article |
| id | doaj.art-b0a619f2ce384d159ac81a654b4ab256 |
| institution | Directory Open Access Journal |
| issn | 2590-1982 |
| language | English |
| last_indexed | 2024-04-24T10:56:38Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Transportation Research Interdisciplinary Perspectives |
| spelling | doaj.art-b0a619f2ce384d159ac81a654b4ab2562024-04-12T04:45:52ZengElsevierTransportation Research Interdisciplinary Perspectives2590-19822024-03-0124101043Using machine learning techniques to assess the financial impact of the COVID-19 pandemic on the global aviation industryKhaled Halteh0Ritab AlKhoury1Salem Adel Ziadat2Adrian Gepp3Kuldeep Kumar4Al-Ahliyya Amman University, Al-Saro, Al-Salt, Jordan; Corresponding author.Al-Ahliyya Amman University, Al-Saro, Al-Salt, JordanAl-Ahliyya Amman University, Al-Saro, Al-Salt, JordanBond University, 14 University Dr, Robina, QLD, 4226, AustraliaBond University, 14 University Dr, Robina, QLD, 4226, AustraliaPrediction 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.http://www.sciencedirect.com/science/article/pii/S2590198224000290G010R400 |
| spellingShingle | Khaled Halteh Ritab AlKhoury Salem Adel Ziadat Adrian Gepp Kuldeep Kumar Using machine learning techniques to assess the financial impact of the COVID-19 pandemic on the global aviation industry Transportation Research Interdisciplinary Perspectives G010 R400 |
| title | Using machine learning techniques to assess the financial impact of the COVID-19 pandemic on the global aviation industry |
| title_full | Using machine learning techniques to assess the financial impact of the COVID-19 pandemic on the global aviation industry |
| title_fullStr | Using machine learning techniques to assess the financial impact of the COVID-19 pandemic on the global aviation industry |
| title_full_unstemmed | Using machine learning techniques to assess the financial impact of the COVID-19 pandemic on the global aviation industry |
| title_short | Using machine learning techniques to assess the financial impact of the COVID-19 pandemic on the global aviation industry |
| title_sort | using machine learning techniques to assess the financial impact of the covid 19 pandemic on the global aviation industry |
| topic | G010 R400 |
| url | http://www.sciencedirect.com/science/article/pii/S2590198224000290 |
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