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Influential factors on Chinese airlines’ profitability and forecasting methods

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  • Xu, Xu
  • McGrory, Clare Anne
  • Wang, You-Gan
  • Wu, Jinran

Abstract

We establish profit models to predict the performance of airlines in the short term using the quarterly profit data collected on the three largest airlines in China together with additional recent historical data on external influencing factors. In particular, we propose the application of the LASSO estimation method to this problem and we compare its performance with a suite of other more modern state-of-the-art approaches including ridge regression, support vector regression, tree regression and neural networks. It is shown that LASSO generally outperforms the other approaches in this study. We concluded a number of findings on the oil price and other influential factors on Chinese airline profitability.

Suggested Citation

  • Xu, Xu & McGrory, Clare Anne & Wang, You-Gan & Wu, Jinran, 2021. "Influential factors on Chinese airlines’ profitability and forecasting methods," Journal of Air Transport Management, Elsevier, vol. 91(C).
    • Handle: RePEc:eee:jaitra:v:91:y:2021:i:c:s0969699720305524
    DOI: 10.1016/j.jairtraman.2020.101969
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    References listed on IDEAS

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    1. Van Landeghem, H. & Beuselinck, A., 2002. "Reducing passenger boarding time in airplanes: A simulation based approach," European Journal of Operational Research, Elsevier, vol. 142(2), pages 294-308, October.
    2. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    3. Alfaro Navarro, José-Luis & Andrés Martínez, María-Encarnación & Trinquecoste, Jean-François, 2015. "The effect of the economic crisis on the behaviour of airline ticket prices. A case-study analysis of the New York–Madrid route," Journal of Air Transport Management, Elsevier, vol. 47(C), pages 48-53.
    4. Nero, Giovanni, 1999. "A note on the competitive advantage of large hub-and-spoke networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 35(4), pages 225-239, December.
    5. Narayan, Paresh Kumar & Sharma, Susan Sunila, 2011. "New evidence on oil price and firm returns," Journal of Banking & Finance, Elsevier, vol. 35(12), pages 3253-3262.
    6. Li, Jiahan & Chen, Weiye, 2014. "Forecasting macroeconomic time series: LASSO-based approaches and their forecast combinations with dynamic factor models," International Journal of Forecasting, Elsevier, vol. 30(4), pages 996-1015.
    7. Forsyth, Peter & Dwyer, Larry, 2010. "Exchange rate changes and the cost competitiveness of international airlines: The Aviation Trade Weighted Index," Research in Transportation Economics, Elsevier, vol. 26(1), pages 12-17.
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