A Study of Prediction of Airline Stock Price through Oil Price with Long Short-Term Memory Model

This study aims to present a model that predicts the stock price of an airline by setting the economic and technical information of oil as features and taking advantage of the LSTM method. In this study, oil price data for about seven years from January 4, 2016, to April 14, 2023, were collected through FinanceDataReader. The collected data is a total of 1,833 days of AA stock price data. The price data consists of six categories: Date, Open, High, Low, Close, Volume, and Change (price is based on dollars). Data is stored every 24 hours, so it was judged to be most suitable for short-term price prediction (24 hours later) to be conducted in this study. In this paper, normalized closing price data was trained for 50 epochs. As a result of learning, the loss value converged close to 0. The MSE measured by the accuracy of the model shows a result of 0.00049. The significance of this study is as follows. First, it is meaningful in that it can present indicators such as more sophisticated predictions and risk management to airline companies. Oil price as our selected feature can compensate for the poor performance of a simple model and its limitations on overfitting.

@article{Choi2023,
  title     = {A Study of Prediction of Airline Stock Price through Oil Price with Long Short-Term Memory Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {5},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Jae Won Choi and Youngkeun Choi},
  doi       = {10.14569/IJACSA.2023.0140509},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140509}
}