Evaluation of company investment value based on machine learning
This work addresses investment value prediction for financial analysts, but it is incremental as it applies standard machine learning techniques to a specific domain.
The paper tackled the problem of evaluating company investment value by building models using comprehensive company data, achieving a Root-Mean-Square Error (RMSE) of 3.047 with a stacking model.
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.