Proceedings of the 7th International Conference on Management, Education, Information and Control (MEICI 2017)

Quantitative Strategies Based on an Improved K-means Algorithm

Authors
Xinyu Wang, Lian Xue, Ruiyu Yu, Yike Wu, Qunfang Yu
Corresponding Author
Xinyu Wang
Available Online October 2017.
DOI
10.2991/meici-17.2017.129How to use a DOI?
Keywords
An improved K-means algorithm;Rolling stock;Quantitative strategy
Abstract

In this paper, a kind of improved k-clustering stock-picking method is adopted to cluster the 300 indexes of the csi 300 indexes, and investigate the average yield of all kinds, and select the class with the highest average rate of return to be held as our portfolio, thus achieving the excess return. The empirical evidence shows that, using the strategy of this paper to select the stocks and roll it, the excess accumulated yield of the portfolio is better than that of the csi 300.

Copyright
© 2017, 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 7th International Conference on Management, Education, Information and Control (MEICI 2017)
Series
Advances in Intelligent Systems Research
Publication Date
October 2017
ISBN
10.2991/meici-17.2017.129
ISSN
1951-6851
DOI
10.2991/meici-17.2017.129How to use a DOI?
Copyright
© 2017, 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  - Xinyu Wang
AU  - Lian Xue
AU  - Ruiyu Yu
AU  - Yike Wu
AU  - Qunfang Yu
PY  - 2017/10
DA  - 2017/10
TI  - Quantitative Strategies Based on an Improved K-means Algorithm
BT  - Proceedings of the 7th International Conference on Management, Education, Information and Control (MEICI 2017)
PB  - Atlantis Press
SP  - 655
EP  - 658
SN  - 1951-6851
UR  - https://doi.org/10.2991/meici-17.2017.129
DO  - 10.2991/meici-17.2017.129
ID  - Wang2017/10
ER  -