Evaluation of company investment value based on machine learning

TL;DR

This study develops machine learning models to evaluate company investment value using extensive feature extraction, feature selection, and ensemble methods, achieving high accuracy and stability in predictions.

Contribution

Introduces a comprehensive machine learning framework with feature selection and stacking models for more accurate company investment evaluation.

Findings

RMSE of 3.098 with XGBoost

RMSE of 3.059 with LightGBM

RMSE of 3.047 with stacking model

Abstract

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.