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.
Highlights Effects of a number of revenue and cost factors on airlines' profitability are explored. A number of modern approaches including LASSO and machine learning are investigated. LASSO effectively removes redundant variables generally outperforming other approaches. Oil price negatively impacts short term profit of the Chinese airlines.
Influential factors on Chinese airlines’ profitability and forecasting methods
2020-10-16
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Airline , Fuel costs , Profitability , LASSO , Forecasting
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