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Evaluation of company investment value based on machine learning

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  • Junfeng Hu
  • Xiaosa Li
  • Yuru Xu
  • Shaowu Wu
  • Bin Zheng

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.

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  • Junfeng Hu & Xiaosa Li & Yuru Xu & Shaowu Wu & Bin Zheng, 2020. "Evaluation of company investment value based on machine learning," Papers 2010.01996, arXiv.org.
    • Handle: RePEc:arx:papers:2010.01996
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    References listed on IDEAS

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    1. Urbinati, Andrea & Bogers, Marcel & Chiesa, Vittorio & Frattini, Federico, 2019. "Creating and capturing value from Big Data: A multiple-case study analysis of provider companies," Technovation, Elsevier, vol. 84, pages 21-36.
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