Discovering the Trading Pattern of Financial Market Participants: Comparison of Two Co-Clustering Methods
Co-clustering is rapidly becoming a powerful data analysis technique in varied fields, such as gene expression analysis, data and web mining, and market baskets analysis. In this paper, two co-clustering methods based on smooth plaid model (SPM) and parallel factor decomposition with sparse latent f...
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| Veröffentlicht in: | IEEE access 2018-01, Vol.6, p.14431-14438 |
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| creator | Shi, Guangwei Ren, Liying Miao, Zhongchen Gao, Jian Che, Yanzhe Lu, Jidong |
| description | Co-clustering is rapidly becoming a powerful data analysis technique in varied fields, such as gene expression analysis, data and web mining, and market baskets analysis. In this paper, two co-clustering methods based on smooth plaid model (SPM) and parallel factor decomposition with sparse latent factors (SLF-PARAFAC) are respectively applied to synthetic data set and investors' transaction-level data set from the China Financial Futures Exchange. We present the comparison between two methodologies. Both SLF-PARAFAC and SPM are efficient, robust, and well suited for discovering trading ecosystems in modern financial markets. We recognize temporal pattern differences of various trader types. The results help to develop a thorough understanding of trading behaviors, and to detect patterns and irregularities. |
| doi_str_mv | 10.1109/ACCESS.2018.2801263 |
| format | Article |
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| source | IEEE Open Access Journals; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals |
| subjects | Analytical models Baskets Clustering Co-clustering Data analysis Data mining Data models Datasets Ecosystems Gene expression Indexes machine learning Pattern recognition time-series trading behavior |
| title | Discovering the Trading Pattern of Financial Market Participants: Comparison of Two Co-Clustering Methods |
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