Identification of Patterns in the Stock Market through Unsupervised Algorithms

Abstract

Making predictions in the stock market is a challenging task. At the same time, several studies have focused on forecasting the future behavior of the market and classifying financial assets. A different approach is to classify correlated data to discover patterns and atypical behaviors in them. In this study, we propose applying unsupervised algorithms to process, model, and cluster related data from two different data sources, i.e., Google News and Yahoo Finance, to identify conditions in the stock market that might help to support the investment decision-making process. We applied principal component analysis (PCA) and a k-means clustering approach to group data according to their principal characteristics. We identified four conditions in the stock market, one comprising the least amount of data, characterized by high volatility. The main results show that, regularly, the stock market tends to have a steady performance. However, atypical conditions are conducive to higher volatility.