[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125898-en":3,"doc-seo-125898-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125898,2336474466712,"Maeve","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Using Machine Learning Techniques to Assess the Financial Impact of the COVID-19 Pandemic on the Global Aviation Industry - Research overview","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.","Bond University Research Repository  \nUsing Machine Learning Techniques to Assess the Financial Impact of the COVID-19 Pandemic on the Global Aviation Industry  \nHalteh, Khaled; AlKhoury, Ritab; Adek Ziadat, Salem ; Gepp, Adrian; Kumar, Kuldeep  \nPublished in:  \nTransportation Research Interdisciplinary Perspectives  \nDOI:  \n[https://doi.org/10.1016/j.trip.2024.101043](https://doi.org/10.1016/j.trip.2024.101043)  \nLicence:  \nCC BY  \nLink to output in Bond University research repository.  \nRecommended citation(APA):  \nHalteh, 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)  \nGeneral rights  \nCopyright 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.  \nFor more information, or if you believe that this document breaches copyright, please contact the Bond University research repository coordinator.  \nDownload date: 18 May 2024  \nTransportation Research Interdisciplinary Perspectives 24 (2024) 101043  \nContents lists available at ScienceDirect  \nTransportation Research Interdisciplinary Perspectives  \njournal [homepage:](homepage: www.sciencedirect.com/journal/transportation)[ www.sciencedirect.com/journal/transportation](homepage: www.sciencedirect.com/journal/transportation)research-interdisciplinary-perspectives  \n| Using machine learning techniques to assess the financial impact of the COVID-19 pandemic on the global aviation industry\u003Cbr>Khaled Halteha, *, Ritab AlKhourya, Salem Adel Ziadata, Adrian Gepp b, Kuldeep Kumar b\u003Cbr>a Al-Ahliyya Amman University, Al-Saro, Al-Salt, Jordan\u003Cbr>b Bond University, 14 University Dr, Robina, QLD, 4226, Australia |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| JEL Codes: G010\u003Cbr>R400\u003Cbr>Keywords:\u003Cbr>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. |\n\n1. Introduction  \nAccurately 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 ","cbCaiftSuIOtQ3R4","https://ap.wps.com/l/cbCaiftSuIOtQ3R4","pdf",883511,4,1,11,"English","en",105,"# Introduction\n## Study approach and modeling goals\n## Industry context and pandemic relevance\n## Data-driven predictive methods","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study targets accurate prediction of financial distress in the aviation industry, particularly under external shocks such as the COVID-19 pandemic.\"},{\"question\":\"Which machine learning methods are used to build the prediction models?\",\"answer\":\"The study leverages decision trees, random forests, and stochastic gradient boosting to develop financial distress prediction models.\"},{\"question\":\"What predictors and models show the strongest performance in forecasting distress?\",\"answer\":\"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.\"}]","Using Machine Learning Techniques to Assess the Financial Impact of the COVID-19 Pandemic on the Global Aviation Industry - 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