Prediction of Transaction Risk in Financial Market Based on Neural Network Model

Abstract

In recent years, China’s economic development has grown rapidly. As an important part of China’s national economy, the financial market has become more and more important for risk avoidance and prediction in the transaction process in the financial market. China’s per capita income level has been continuously improved, and the national policies have been adjusted for high interest rate sectors such as real estate and the Internet, The prediction of transaction risk in financial market is becoming more and more important for investors in the market. With the continuous development of computer technology, neural network is widely used in the financial market. Through the neural network under computer technology, the prediction model of the financial market is built, and the data of the financial market are collected and analyzed in real time, so as to provide sufficient data for the study of real social information and people’s work. The experimental results show that the risk aversion efficiency of financial market transaction risk prediction based on neural network model is improved by 7.6%. Combined with computer intelligence, it fully displays the complexity of financial time series prediction model in wavelet clustering model and phase space reconstruction prediction model. For the limitations of manually analyzing the situation and one sidedness of the market, the risk points of historical market transactions in big data are sorted and reminded, the neural network algorithm is updated and iterated in an innovative way, and the financial risk prediction model is constructed by using the wavelet clustering prediction method.