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A Cluster Analysis of Stock Market Data Using Hierarchical SOMs
- Source :
- AI 2016: Advances in Artificial Intelligence ISBN: 9783319501260, Australasian Conference on Artificial Intelligence
- Publication Year :
- 2016
-
Abstract
- The analysis of stock markets has become relevant mainly because of its financial implications. In this paper, we propose a novel methodology for performing a structured cluster analysis of stock market data. Our proposed method uses a tree-based neural network called the TTOSOM. The TTOSOM performs self-organization to construct tree-based clusters of vector data in the multi-dimensional space. The resultant tree possesses interesting mathematical properties such as a succinct representation of the original data distribution, and a preservation of the underlying topology. In order to demonstrate the capabilities of our method, we analyze 206 assets of the Italian stock market. We were able to establish topological relationships between various companies traded on the Italian stock market and visually inspect the resultant taxonomy. The results that we obtained, briefly reported here (but more elaborately in [10]), were amazingly accurate and reflected the real-life relationships between the stocks.
- Subjects :
- Artificial neural network
Computer science
Mathematical properties
020206 networking & telecommunications
02 engineering and technology
computer.software_genre
Original data
0202 electrical engineering, electronic engineering, information engineering
Cluster (physics)
020201 artificial intelligence & image processing
Stock market
Data mining
Cluster analysis
computer
Stock (geology)
Subjects
Details
- Language :
- English
- ISBN :
- 978-3-319-50126-0
- ISBNs :
- 9783319501260
- Database :
- OpenAIRE
- Journal :
- AI 2016: Advances in Artificial Intelligence ISBN: 9783319501260, Australasian Conference on Artificial Intelligence
- Accession number :
- edsair.doi.dedup.....c24605f70f438d5f53b7d41e66a310dc