Abstract
In order to study the correlation between stock prices and financial indicators of Chinese listed companies, this paper selects representative and relevant data from CSMAR to study 12 indicators as input layers, which reflect the solvency, profitability and management ability of the companies. Based on the lasso algorithm, the data dimension is reduced to 5 indicators; a lasso RBF neural network is constructed and the neural network is trained to obtain the company's stock price simulation. The results show that the model has good robustness and the accuracy of the stock price fitting is 93%. Among them, the main contribution of net investment cash flow to stock price is 25.46, which provides some suggestions for stock price prediction analysis.
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