【Abstract】

Financial status and its role in the national economy have been increasingly recognized. In order to deduce the source of monetary funds and determine their whereabouts, financial information and prediction have become a scientific method that cannot be ignored in the development of national economy. This paper improves the existing CNN and applies it to financial credit from different perspectives. Firstly, the noise of the collected data set is deleted, and then the clustering result is more stable by principal component analysis. The observation vectors are segmented to obtain a set of observation vectors corresponding to each hidden state. Based on the output of PCA algorithm, the authors recalculate the mean and variance of all kinds of observation vectors and use the new mean and covariance matrix as credit financial credit and then determine the best model parameters. The empirical results based on specific data from China’s stock market show that the improved convolutional neural network proposed in this paper has advantages and the prediction accuracy reaches.

【Keywords】

Convolution Neural Network, Data Discretization, Financial Credit, Principal Component Analysis

【Authors】

Dai Wensheng, IMI Senior Research Fellow, School of Finance, Renmin University of China