Title: Predicting stock market movements using network science: An information theoretic approach,
Abstract: 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. We built several metrics using the strength
distributions and network measurements such as centrality, and we combined the
best two predictors by performing a linear combination. We found that the
combined predictor and the changes in S&P 500 show a quadratic relationship,
and it allows us to predict the amplitude of the one step future change in S&P
500. The result showed significant fluctuations in S&P 500 Index when the
combined predictor was high. In terms of making the actual index predictions,
we built ARIMA models. We found that adding the network measurements into the
ARIMA models improves the model accuracy. These findings are useful for
financial market policy makers as an indicator based on which they can
interfere with the markets before the markets make a drastic change, and for
quantitative investors to improve their forecasting models.