Predicting stock market movements using network science: An information theoretic approach

Understand

A stock market is considered as one of the highly complex systems, which consists of many components whose prices move up and down without having a clear pattern.

• The complex nature of a stock market challenges us on making a reliable prediction of its future movements.
• In this paper, we aim at building a new method to forecast the future movements of Standard & Poor's 500 Index (S&P 500) by constructing time-series complex networks of S&P 500 underlying companies by connecting them with links whose weights are given by the mutual information of 60-minute price movements of the pairs of the companies with the consecutive 5,340 minutes price records.
• We showed that the changes in the strength distributions of the networks provide an important information on the network's future movements.