Valuation of science and technology innovation enterprises based on XGBoost-GA-BP neural network model.

ABSTRACT: The science and technology innovation board (STIB) is a critical initiative to support the high-quality development of technology innovation enterprises. Accurate valuation of STIB-listed enterprises is essential for optimizing resource allocation and enhancing capital market efficiency. However, existing valuation methods face significant challenges, including the presence of nonlinear data and low accuracy when assessing these enterprises. Given the capability of machine learning methods to address such issues, this study proposes a hybrid approach that combines Spearman correlation analysis and XGBoost feature selection to identify key indicators. The selected features are subsequently input into a GA-BP neural network for training and simulation. The empirical results, based on a datas

SUBMITTER: Shen C 

PROVIDER: S-EPMC12381020 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature