The Impact of Industrial Policy on Photovoltaic Enterprise Risk Using an LDA Based-Deep Neural Network Model

The development and utilization of new and renewable resources of energy has become an important layout of the development strategy in China.Photovoltaic industry is an important strategic emerging industry for the development and utilization of new energy in China.Therefore,it is important for the government to make policy to ensure the stable and orderly development of photovoltaic enterprises to accelerate the industrial structure transition in China.This paper collects the policies on photovoltaic industry,and then analyzes the industrial policy with Latent Dirichlet Allocation(LDA).LDA is generally used in document topic label extraction and recommendation system.However,this paper applies it to policy theme analysis to study the impact of policy information flow on the risk of photovoltaic enterprises.Previous studies on photovoltaic enterprise risk examined traditional financial indicators,such as asset-liability Ratio and ROE.However,the textual information in the industrial policy has rarely been studied to quantitatively analyze photovoltaic enterprise risk.In our proposed method,LDA is first used to extract the text features hiding in the text of the industrial policies,and deep neural networks then are trained on the data,which include the text features and traditional numeric features for predict photovoltaic enterprise risk.The experimental results show that the industrial policy of the current quarter has a significant effect on photovoltaic enterprise risk.Compared with this,the industrial policy of last quarter has a weak impact on the photovoltaicenterprise risk.The proposed model is a useful tool for the prediction of the photovoltaic enterprise risk.