ABSTRACT
Clustering is an adaptive procedure in which objects are clustered or grouped together, based on the principle of
maximizing the intra-class similarity and minimizing the inter-class similarity. Various clustering algorithms have
been developed which results to a good performance on datasets for cluster formation. This paper analyze the
major clustering algorithms: K-Means, Hierarchical clustering algorithm and reverse K means and compare the
performance of these three major clustering algorithms on the aspect of correctly class wise cluster building
ability of algorithm. An effective clustering method, HRK (Hierarchical agglomerative and Recursive K-means
clustering) is proposed, 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.