A Rough Petri Nets Model for Stock Trading Signal Detection

被引：0
|
作者：
Shih, Po-Yuan [1 ]
Shih, Dong-Her [2 ]
Hung, Shin-Yuan [3 ]
Shih, Ming-Hung [4 ]
机构：
[1] Natl Chung Cheng Univ, Dept Finance, Chiayi, Taiwan
[2] Natl Yunlin Univ Sci & Technol, Dept Informat Management, Touliu, Yunlin, Taiwan
[3] Natl Chung Cheng Univ, Coll Management, Dept Informat Management, Chiayi, Taiwan
[4] Iowa State Univ, Dept Elect & Elect Engn, Ames, IA 50011 USA
来源：
PROCEEDINGS OF 2016 IEEE INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA ANALYSIS (ICCCBDA 2016) | 2016年
关键词：
rough Petri nets; stock trading signals; technical indicator analysis; data mining;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Stock trading signal detection is an important approach for investors in financial investment. Most studies focus on the precise price prediction, but trading point detection is more practical than price prediction. This paper proposes a novel approach by using rough Petri nets to discover the trading rules hidden in historical data. Experiment results show that our proposed stock trading signal detection can obtain a considerable return in investment.
引用
收藏
页码：135 / 139
页数：5
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