Abstract
The stocks sold on financial markets are classified by industry. The predefined category for each stock has no relation to its price movement in the financial market. In this paper, we use the graph partitioning based data mining method to determine the industrial classification of stocks only based on the historical data of them. The data mining method includes clustering and biclustering for time-series data. In this paper, we present the implemented algorithms on the stocks of S&P 500 index in four years. Based on this approach, we analyze the associations between stocks to forecast the movement direction of the financial market.
Original language | English (US) |
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State | Published - 2010 |
Externally published | Yes |
Event | IIE Annual Conference and Expo 2010 - Cancun, Mexico Duration: Jun 5 2010 → Jun 9 2010 |
Other
Other | IIE Annual Conference and Expo 2010 |
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Country/Territory | Mexico |
City | Cancun |
Period | 6/5/10 → 6/9/10 |
Keywords
- Biclustering
- Bipartite graph
- Clustering
- Financial stocks
- Graph partitioning
- Time series
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering