Open AccessA Comparative Study of Financial Big Data Standard System Based on Deep Learning Algorithms
Author(s) -
Huaxia Shen
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/563/5/052035
Subject(s) - big data , finance , diversification (marketing strategy) , market data , computer science , financial market , financial services , volatility (finance) , financial modeling , business , data mining , marketing
The standard system of financial big data involves a wide range of contents and diversification. Financial institutions in the process of operation and social sectors constitute a huge interweaving network, precipitating a large number of data. In this context, data security is particularly important. Therefore, based on the deep learning algorithm, the author compares and studies the financial big data standard system. The in-depth learning model is introduced into the financial market and combined with the traditional statistical model to forecast the volatility of the financial market and calculate its risk value. Through the research and comparative analysis of the domestic and international financial big data standard norm system, it is found that part of the domestic financial big data standard specification is revised by reference, while the other part has the characteristics of Chinese financial market. However, there is still room for further development in terms of financial big data regulation, information security, financial enterprise big data platform construction and analytical capabilities.