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Under mounting competitive pressures, airline companies have increasingly shifted their strategic focus toward maximizing returns for their shareholders. This research article addresses the need to fill the gap concerning the prediction of shareholder returns in the airline industry using machine learning techniques by examining the efficacy of various machine learning models in predicting shareholder returns within the airline industry. The research shows that Random Forest Regression and Long Short-Term Memory (LSTM) models excel in predicting TSR. Additionally, it seeks to identify key financial metrics that significantly influence the airline stock performance.

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Machine Learning Models to Predict Quarterly Shareholder Returns in the Airline Industry

  • 会议论文

  • 发表日期：

    2026年

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  • Le Duc Thinh,
  • Nguyen Thi Lan Phuong,
  • Dao Duc Anh,
  • Le Phuong Thao Nguyen,
  • Nguyen Ngoc Van Quynh,
  • Tran Chau Nhi

摘要

Under mounting competitive pressures, airline companies have increasingly shifted their strategic focus toward maximizing returns for their shareholders. This research article addresses the need to fill the gap concerning the prediction of shareholder returns in the airline industry using machine learning techniques by examining the efficacy of various machine learning models in predicting shareholder returns within the airline industry. The research shows that Random Forest Regression and Long Short-Term Memory (LSTM) models excel in predicting TSR. Additionally, it seeks to identify key financial metrics that significantly influence the airline stock performance.