Analysis of financial pressure impacts on the health care industry with an explainable machine learning method: China versus the USA

Abstract

This study analyzes the role of financial pressure in forecasting the volatility of health care stock. The main finding shows that financial pressure helps to improve the volatility forecasting performance of the health care stock in both China and the USA. Empirical analysis further suggests that XGBoost performance outperforms other benchmark models, especially advanced machine learning models. This study also interprets predictions to help financial institutions and investors make correct decisions using Shapley additive explanations. The results illustrate that the prediction contribution of financial pressure is much stronger in China than in the USA. The prediction contribution distribution of the five-dimensional indicator of financial pressure in China is more discrete than in the USA. Different lag periods of financial pressure have an asymmetric predictive contribution to the volatility of the health care stock. The volatility of Chinese health care stock is mainly influenced by the five-dimensional indicator of financial pressure at the medium and late period lag but at the front and medium period lag for the USA. These findings are crucial for policymakers and investors in promoting the sustained health care stock market through financial pressure regulation.