Proceedings of the 2020 2nd International Conference on Economic Management and Cultural Industry (ICEMCI 2020)

Research on Financial Statistics Model Method under the Background of Big Data

Authors
Yixin Chen
Corresponding Author
Yixin Chen
Available Online 30 November 2020.
DOI
https://doi.org/10.2991/aebmr.k.201128.099How to use a DOI?
Keywords
big data, financial statistics, influence, strategy
Abstract

With the rapid development of science and technology, China has entered the era of big data, which has new requirements for data collection and processing. For financial statistics, the original financial statistical model has been difficult to meet the development needs of financial statistics in the era of big data, and it is necessary to innovate the existing financial statistical model. Based on the analysis of the era of big data, this article focuses on the impact of the era of big data on financial statistics, and formulates financial model optimization strategies based on the impact.

Copyright
© 2020, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2020 2nd International Conference on Economic Management and Cultural Industry (ICEMCI 2020)
Series
Advances in Economics, Business and Management Research
Publication Date
30 November 2020
ISBN
978-94-6239-283-0
ISSN
2352-5428
DOI
https://doi.org/10.2991/aebmr.k.201128.099How to use a DOI?
Copyright
© 2020, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Yixin Chen
PY  - 2020
DA  - 2020/11/30
TI  - Research on Financial Statistics Model Method under the Background of Big Data
BT  - Proceedings of the 2020 2nd International Conference on Economic Management and Cultural Industry (ICEMCI 2020)
PB  - Atlantis Press
SP  - 517
EP  - 520
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.201128.099
DO  - https://doi.org/10.2991/aebmr.k.201128.099
ID  - Chen2020
ER  -