Financial Analysis System Design for Institutional Users Based on Deep Learning

Abstract

To strengthen the ability to obtain effective financial data in complex network environment, a distributed information data acquisition and analysis strategy based on deep learning is proposed. Based on service-oriented architecture, the overall requirements and data analysis of the platform are provided an integrated infrastructure platform under cloud platform is established. The analysis module of the core of system uses LSTM networks to model financial data to process complex financial market data inputs. Then, the detailed implementation processes of data acquisition, pre-partition and storage module are also realized. Finally, a large number of institutional user samples are used as case studies to test the function and performance of the system. The results show that such model can provide effective prediction results and it has higher accuracy in time series data prediction. Besides, the system resource scheduling has high efficiency and stability on real financial big datasets.