Proceedings of the 5th International Conference on Social Sciences and Economic Development (ICSSED 2020)

Research on Performance Evaluation System of Manufacturing Listed Companies Based on CNN

Authors
Zhang Chengwei
Corresponding Author
Zhang Chengwei
Available Online 2 April 2020.
DOI
https://doi.org/10.2991/assehr.k.200331.049How to use a DOI?
Keywords
convolutional neural network, key indicators, performance evaluation
Abstract
From the perspective of studying and evaluating the operating performance of enterprises, this paper chooses a system including, but not limited to, financial indicators. Based on this, in view of the uncertainty of the coupling between the evaluation indicators, a performance evaluation model of state-owned pillar manufacturing enterprises based on convolutional neural network (CNN) was established. Specific performance data from 2016 to 2018 of many representative companies in China’s listed entity manufacturing industry is selected as training and testing samples for neural networks. Finally, The trained CNN neural network is applied to the current evaluation and simulation prediction of corporate performance. The empirical analysis results in this paper have achieved satisfactory results.
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Proceedings
5th International Conference on Social Sciences and Economic Development (ICSSED 2020)
Part of series
Advances in Social Science, Education and Humanities Research
Publication Date
2 April 2020
ISBN
978-94-6252-946-5
ISSN
2352-5398
DOI
https://doi.org/10.2991/assehr.k.200331.049How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Zhang Chengwei
PY  - 2020
DA  - 2020/04/02
TI  - Research on Performance Evaluation System of Manufacturing Listed Companies Based on CNN
BT  - 5th International Conference on Social Sciences and Economic Development (ICSSED 2020)
PB  - Atlantis Press
SP  - 224
EP  - 231
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.200331.049
DO  - https://doi.org/10.2991/assehr.k.200331.049
ID  - Chengwei2020
ER  -