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The application of a neural network approach to predicting bankruptcy risks facing the major US air carriers: 1979–1996

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  • Davalos, Sergio
  • Gritta, Richard D.
  • Chow, Garland

Abstract

Airline bankruptcy, an unheard of event prior to the deregulation of the US airline industry, has become rather commonplace. Over 123 air carriers have filed receivership since 1982, and several large carriers have sought court protection more than once in the past decade. In spite of record airline profits over the past two years, the financial condition of many carriers still remains fragile. The huge financing requirements of the industry over the next decade, driven by the carriers’ need to replace aging fleets of aircraft, will create further stress for many. The ability to assess the level of this financial stress is important to many groups, including stockholders, bondholders, other creditors, financial analysts, governmental regulatory bodies, and the general public. For this reason, models that can forecast financial distress are useful. Building on prior research by several of the authors, who utilized multiple discriminant models driven by financial ratios, a neural network approach is employed to increase the reliability of the forecasts. In this paper, a neural net is trained with the result that it successfully classifies 26 out of 26 carriers in the holdout (test) set.

Suggested Citation

  • Davalos, Sergio & Gritta, Richard D. & Chow, Garland, 1999. "The application of a neural network approach to predicting bankruptcy risks facing the major US air carriers: 1979–1996," Journal of Air Transport Management, Elsevier, vol. 5(2), pages 81-86.
  • Handle: RePEc:eee:jaitra:v:5:y:1999:i:2:p:81-86
    DOI: 10.1016/S0969-6997(98)00042-8
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    Cited by:

    1. Soo Young Kim, 2018. "Predicting hospitality financial distress with ensemble models: the case of US hotels, restaurants, and amusement and recreation," Service Business, Springer;Pan-Pacific Business Association, vol. 12(3), pages 483-503, September.
    2. de Oliveira, Renan P. & Oliveira, Alessandro V.M., 2021. "Financial distress, survival network design strategies, and airline pricing: An event study of a merger between a bankrupt FSC and an LCC in Brazil," Journal of Air Transport Management, Elsevier, vol. 92(C).
    3. Lin, Chin-Shien & Khan, Haider A. & Chang, Ruei-Yuan & Wang, Ying-Chieh, 2008. "A new approach to modeling early warning systems for currency crises: Can a machine-learning fuzzy expert system predict the currency crises effectively?," Journal of International Money and Finance, Elsevier, vol. 27(7), pages 1098-1121, November.
    4. Akkoç, Soner, 2012. "An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish cred," European Journal of Operational Research, Elsevier, vol. 222(1), pages 168-178.
    5. Yin Shi & Xiaoni Li, 2021. "Determinants of financial distress in the European air transport industry: The moderating effect of being a flag-carrier," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-17, November.
    6. Jayasekera, Ranadeva, 2018. "Prediction of company failure: Past, present and promising directions for the future," International Review of Financial Analysis, Elsevier, vol. 55(C), pages 196-208.
    7. Yin Shi & Xiaoni Li & Maher Asal, 2023. "Impact of sustainability on financial distress in the air transport industry: the moderating effect of Asia–Pacific," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    8. Wei, Fangwu & Grubesic, Tony H., 2016. "The pain persists: Exploring the spatiotemporal trends in air fares and itinerary pricing in the United States, 2002–2013," Journal of Air Transport Management, Elsevier, vol. 57(C), pages 107-121.

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