  In this paper, company investment value evaluation models are established
based on comprehensive company information. After data mining and extracting a
set of 436 feature parameters, an optimal subset of features is obtained by
dimension reduction through tree-based feature selection, followed by the
5-fold cross-validation using XGBoost and LightGBM models. The results show
that the Root-Mean-Square Error (RMSE) reached 3.098 and 3.059, respectively.
In order to further improve the stability and generalization capability,
Bayesian Ridge Regression has been used to train a stacking model based on the
XGBoost and LightGBM models. The corresponding RMSE is up to 3.047. Finally,
the importance of different features to the LightGBM model is analysed.
