Proceedings of the 2017 International Conference on Computational Science and Engineering (ICCSE 2017)

MA Empirical Analysis Based on the Cloud Computing Sector of Listed Companies

Authors
Zhi-heng Lin, Xiao-ming Huang, Pin Wang
Corresponding Author
Zhi-heng Lin
Available Online July 2017.
DOI
https://doi.org/10.2991/iccse-17.2017.47How to use a DOI?
Keywords
Cloud computing sector, MA, Empirical analysis
Abstract
With the method of statistical empirical analysis, This study aims to test the MA expert system via securities trading software according to authentic and open securities cloud computing sector data. By taking the annual net profit margin, rate of return and win rate as the management goal, the MA index is analyzed empirically. The annual rate of return and net profit margin of MA expert system are 47.38% and 47.42% of Shanghai securities composite index, suggesting the guidance of cloud computing sector investment by MA expert system cannot outperform the market index. 43.16% winning rate of MA expert system also indicates that the system risk for investors is huge. 8.89% annual rate of return is 5.08 times that of the one-year interest rate of bank deposit. Apparently, the results are just-so-so. Considering the low annual rate of return of MA expert system, the system is not an attractive investment scheme for the investors.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
Part of series
Advances in Computer Science Research
Publication Date
July 2017
ISBN
978-94-6252-404-0
ISSN
2352-538X
DOI
https://doi.org/10.2991/iccse-17.2017.47How 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  - Zhi-heng Lin
AU  - Xiao-ming Huang
AU  - Pin Wang
PY  - 2017/07
DA  - 2017/07
TI  - MA Empirical Analysis Based on the Cloud Computing Sector of Listed Companies
PB  - Atlantis Press
SP  - 269
EP  - 273
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccse-17.2017.47
DO  - https://doi.org/10.2991/iccse-17.2017.47
ID  - Lin2017/07
ER  -