Quantitative Analysis for Chinese and US-listed Pharmaceutical Companies by the LightGBM Algorithm - Zheng, Wenwen; Li, Junjun; Wang, Yu; Ye, Zhuyifan; Zhong, Hao; Kot, Hung W.; Ouyang, Defang; Chan, Ging (Current Computer - Aided Drug Design, 2023)
Aim: This article aims to quantitatively analyze the growth trend of listed pharmaceutical companies in the US and China by a machine learning algorithm. Background: In the last two decades, the global pharmaceutical industry has faced the dilemma of low research & development (R&D) success rate. The US is the world's largest pharmaceutical market, while China is the largest emerging market. Objective: To collect data from the database and apply machine learning to build the model. Methods: LightGBM algorithm was used to build the model and identify the factor important to the performance of pharmaceutical companies. Results: The prediction accuracy for US companies was 80.3%, while it was 64.9% for Chinese companies. The feature importance shows that the net profit growth rate and debt liability ratio are significant in financial indicators. The results indicated that the US may continue to dominate the global pharmaceutical industry, while several Chinese pharmaceutical companies rose sharply after 2015 with the narrowing gap between the Chinese and US pharmaceutical industries. Conclusion: In summary, our research quantitatively analyzed the growth trend of listed pharmaceutical companies in the US and China by a machine learning algorithm, which provide a novel perspective for the global pharmaceutical industry. According to the R&D capability and profitability, 141 US-listed and 129 China-listed pharmaceutical companies were divided into four levels to evaluate the growth trend of pharmaceutical firms.
Keywords: Pharmaceutical industry