| 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. |
