https://scholars.lib.ntu.edu.tw/handle/123456789/415117
標題: | An effective clustering approach to stock market prediction | 作者: | Lee A.J.T. Lin M.-C. Kao R.-T. Chen K.-T. |
關鍵字: | Document clustering;Financial report;Stock price prediction | 公開日期: | 2010 | 起(迄)頁: | 345-354 | 來源出版物: | PACIS 2010 - 14th Pacific Asia Conference on Information Systems | 摘要: | In this paper, we propose an effective clustering method, HRK (Hierarchical agglomerative and Recursive K-means clustering), to predict the short-term stock price movements after the release of financial reports. The proposed method consists of three phases. First, we convert each financial report into a feature vector and use the hierarchical agglomerative clustering method to divide the converted feature vectors into clusters. Second, for each cluster, we recursively apply the K-means clustering method to partition each cluster into sub-clusters so that most feature vectors in each subcluster belong to the same class. Then, for each sub-cluster, we choose its centroid as the representative feature vector. Finally, we employ the representative feature vectors to predict the stock price movements. The experimental results show the proposed method outperforms SVM in terms of accuracy and average profits. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/415117 |
顯示於: | 資訊管理學系 |
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