Stock market co-movement assessment using a three-phase clustering method

An automatic stock market categorization system would be invaluable to individual investors and financial experts, providing them with the opportunity to predict the stock price changes of a company with respect to other companies. In recent years, clustering all companies in the stock markets based on their similarities in the shape of the stock market has increasingly become a common scheme. However, existing approaches are impractical because the stock price data are high-dimensional data and the changes in the stock price usually occur with shift, which makes the categorization more complex. Moreover, no stock market categorization method that can cluster companies down to the sub-cluster level, which are very meaningful to end users, has been developed. Therefore, in this paper, a novel three-phase clustering model is proposed to categorize companies based on the similarity in the shape of their stock markets. First, low-resolution time series data are used to approximately categorize companies. Then, in the second phase, pre-clustered companies are split into some pure sub-clusters. Finally, sub-clusters are merged in the third phase. The accuracy of the proposed method is evaluated using various published data sets in different domains. We show that this approach has good performance in efficiency and effectiveness compared to existing conventional clustering algorithms. There are many works related to stock market analysis includ-Clustering is a data mining technique in which similar data are automatically placed into related groups without advanced knowledge of the group definitions. Clustering of companies in the stock market is very useful for managers, investors, and policy makers. It can be performed based on several factors, such as the size of the companies, their annual profit, and the industry category. For example, Nanda et al. (2010) used the returns of the stock for variable time intervals along with the validation ratios to cluster the companies listed in Bombay Stock Exchange (BSE). However, these features usually change over time; thus, they are improper for categorization purposes. Industry-based categorization is also not preferable due to evidence that financial analysts are biased by industry categorization (Krüger et al., 2012). Conversely, the closing prices of stocks related to each company are stored as time series data. Companies can be categorized by the clustering of their stock price time series. Clustering companies based on the time series of their stock price is particularly advantageous in co-movement assessment. Identifying homogeneous groups of stocks where the movement in one market …