Abstract
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.
Highlights
Lin Wenhao and Chen Xuebin et al, combined with the relevant data of Shanghai Securities, proposed a stock market risk prediction method based on GARCH(Generalized Autoregressive
The focus is how to improve the accuracy of risk prediction [11]. erefore, in Scientific Programming order to solve the above problems, based on extensive review of relevant literature, this study takes stock market risk warning as the research object, and proposes a LSTM-VaR market risk warning model in the basis of the LSTM(Long Short Term Memory) and the VaR(Value at Risk)
As can be seen from the table, the optimized LSTM model has the lowest root mean square error (RMSE) value compared with the other prediction method. erefore, the optimized LSTM model proposed in this study can effectively and well predict the stock return rate, which has certain advantages
Summary
Prediction methods based on artificial neural networks have achieved good prediction results in dealing with market non-linearity and time series dependence. Lin Wenhao and Chen Xuebin et al, combined with the relevant data of Shanghai Securities, proposed a stock market risk prediction method based on GARCH(Generalized Autoregressive. Conditional Heteroskedasticity model), which effectively realized the prediction of stock market risk [2, 3]; Li Xinxin and Liu Chengcheng et al built a risk prediction model with generalized vector autoregressive model [4, 5]; Guo Jing and Liu Wenchao et al evaluated the inherent volatility risk of the stock market through implied tail risk, greatly improving the risk warning ability [6, 7]; Zhou Wenhaoand Tian Chongwenet al., constructed a customer default model of banks on the basis of commercial banks data and the logistic regression, so as to improve the identification ability of customer risks of banks; the above methods are mainly through quantitative risk analysis [8, 9]. By using LSTM network to predict standard deviation of stock returns and VaR to measure value at risk, the stock market risk warning can be realized
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