摘要：The market is intricate and complicated, and the existing risk warning models have problems of low efficiency and poor generalization in predicting market risk data. Aiming at the problems, this study takes stock market risk warning as the research object and proposes a market risk warning model based on LSTM-VaR. 15 variables in the three categories of basic transaction data, statistical technical indicators, and moving interval data are selected as the stock market characteristic indicators, the LSTM(Long Short Term Memory) prediction model is constructed and the standard deviation of stock returns is predicted. Based on the predicted results, the probability distribution of return rate under the conditional distribution is obtained, and the VaR(Value at Risk) is calculated. 1% and 5% sample quantiles are taken as the warning line, and the LSTM-VaR warning model is obtained. The results show that the RMSE value of the model is the smallest, which is 0.013762, when the activation function of the LSTM-VaR model is the Leaky ReLu function, the training periods epochs are 10, the time window length N is 9, the batch size is 8, the number of neurons in each layer is 50, the dropout probability is 0.1, and adam is used as the optimizer. Compared with traditional prediction models such as MLP, the proposed model has better performance and can well realize market risk warning.
