Indonesia Stock Exchange Liquid Stocks Identification using Self-Organizing Map

被引:0
作者
Widiputra, Harya [1 ]
Christianto, Leo [2 ]
机构
[1] Perbanas Inst, Fac Informat Technol, Jakarta, Indonesia
[2] Swiss German Univ, Fac Informat Technol, Tangerang, Indonesia
来源
2012 2ND INTERNATIONAL CONFERENCE ON UNCERTAINTY REASONING AND KNOWLEDGE ENGINEERING (URKE) | 2012年
关键词
Unsupervised learning; Self-Organizing Map; liquid stocks identification; NEURAL-NETWORK;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Being able to gain profit is one of the main objectives of people who work in the financial area. Yet, the volatility of the stock price movement makes the task of predicting future condition of a stock market difficult to accomplish. One approach known to provide a safe strategy in stock trading is by collecting only those stocks, which are considered as liquid. The Indonesia Stock Exchange market (IDX) publishes this list of liquid stocks every six months (known as the LQ45 list). Having prior knowledge of stocks that will be in the upcoming LQ45 list would then be a great help to assist people who work in the Financial area in planning their future investment and gain profit. This study proposed the use of unsupervised data mining technique called the Self-Organizing Map algorithm (SOM) to perform early identification of liquid stocks from all listed companies in the IDX by dynamically creating a group of liquid stocks based on their historical states.
引用
收藏
页码:131 / 136
页数:6
相关论文
共 21 条
[1]  
[Anonymous], 2006, Introduction to Data Mining
[2]  
Barbalho JM, 2001, IEEE IJCNN, P442, DOI 10.1109/IJCNN.2001.939060
[3]   Unsupervised Learning [J].
Barlow, H. B. .
NEURAL COMPUTATION, 1989, 1 (03) :295-311
[4]  
Borgelt, 2006, THESIS
[5]  
Caruana R., 2006, ACM INT C P SER, P161, DOI [10.1145/1143844.1143865, DOI 10.1145/1143844.1143865]
[6]   Breast cancer diagnosis using self-organizing map for sonography [J].
Chen, DR ;
Chang, RF ;
Huang, YL .
ULTRASOUND IN MEDICINE AND BIOLOGY, 2000, 26 (03) :405-411
[7]  
D Anil K Jain R.C., 1988, Algorithms for Clustering Data
[8]  
De Silva L., 2006, P INT C INF AUT DEC, P155
[9]  
Fayyad U, 1996, AI MAG, V17, P37
[10]   Clustering by competitive agglomeration [J].
Frigui, H ;
Krishnapuram, R .
PATTERN RECOGNITION, 1997, 30 (07) :1109-1119