Machine Learning Approaches to Predict Shareholder Returns in the Hotel Industry

Under mounting competitive pressures, hotel 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 hotel industry using machine learning techniques by examining the efficacy of various machine learning models in predicting shareholder returns within the hotel industry. Additionally, it seeks to identify key financial metrics (financial ratios) and granular, industry-specific operational metrics that significantly influence stock performance, specifically Total Shareholder Returns (TSR) of hotel companies. Moreover, SHAP-based interpretability is employed to identify the key financial and operational drivers behind model predictions.