Stock Market Prediction Using the Representative Features
Date Issued
2009
Date
2009
Author(s)
Kao, Rung-Tai
Abstract
With disclosure regulation, a large amount of financial reports is available for investment purposes and research analysis. In this thesis, 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, where each financial report is represented by a feature vector. The proposed method consists of three phases. First, we use the hierarchical agglomerative clustering (HAC) algorithm to divide the feature vectors into several clusters. Second, for each cluster, we recursively divide the feature vectors within the cluster into several clusters by the K-means algorithm until most feature vectors in each cluster have the same label. Then, we compute the centroid for each cluster. The centroids are called the representative feature vectors of the clusters. Finally, we use these representative feature vectors to predict the stock price movements. The experimental results show that the proposed method outperforms the SVM method in terms of accuracy and average profits.
Subjects
stock price prediction
financial report
document clustering.
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