Mining for Core Patterns in Stock Market Data

We introduce an algorithm that uses stock sector information directly in conjunction with time series subsequences for mining core patterns within the sectors of stock market data. The core patterns within a sector are representative groups of stocks for the sector when it shows coherent behavior. Multiple core patterns may exist in a sector at the same time. In comparison with clustering algorithms, the core patterns are shown to be more stable as the stock price evolves. The proposed algorithm has only one free parameter, for which we provide an empirical choice. We demonstrate the effectiveness of the algorithm through a comparison with the DBScan clustering algorithm using data from the Standard and Poor 500 Index.