Yang Ziya, Zhu Yucheng, Chen Jiaxin, Xie Songyan, Liu Cheng
School of Business and Tourism, Sichuan Agricultural University, China.
College of Architecture and Urban Rural Planning, Sichuan Agricultural University, China.
Heliyon. 2024 Mar 3;10(6):e26169. doi: 10.1016/j.heliyon.2024.e26169. eCollection 2024 Mar 30.
Public opinion will significantly affect investor decision-making and stock prices, which ultimately has an impact on the long-term development of the new energy industry. This paper mainly aims to delve in the impact of public opinion on the efficacy of financial risk early warning effect and try to establish an enhanced financial risk early warning model for the new energy list companies. To achieve this, we collect the financial data and public evaluation texts of 185 new energy listed companies, converting the text into emotional indicators which are combined with financial indicators to build a financial risk early warning model for new energy listed companies. The contributions of this paper are as follows: (1) The experiment validation demonstrates that the combination of 7 deep learning models and Bagging algorithm highly improves the accuracy of the sentiment analysis model, achieving an accuracy of 84.09%. (2) The accuracy of financial early warning models is generally enhanced after adding sentiment indicators, among which the accuracy of the BP neural network model reached 95.78%. (3) Through clustering analysis, the evaluation models can productively divide the warning intervals, thereby bolstering the interpretability and applicability of early warning results. Therefore, we suggest that when establishing the financial early warning system, it's necessary to take public opinions into consideration. Aside from improving the early warning effect, it also can be used as a separate indicator for daily monitoring.
