Use CNN-LSTM network to analyze secondary market data

In the secondary market, analysis method is mainly based on the statistical and artificial modeling method. We proposed to use neural network to analysis secondary market financial data. First of all, puts forward the idea of using the neural network to analysis the financial secondary market. Secondly, we prove the feasibility of using the neural network to analyze the data in the secondary market, and designs a kind of artificial neural networks which is suitable for dealing with financial data called CNN-LSTM network. Compares to the traditional simple statistical methods and some other neural network methods such as logistic regression, convolution neural network (CNN), long and short term memory network (LSTM) and other methods, in the market price changes in a relatively short period of time in the forecast and The forecast for a longer period of time has improved significantly, by 10% over simple statistical methods and 5% higher than other neural networks. Proposed a more effective way to analyze the financial secondary market data.

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