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

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.Comment: 13 pages, 7 figures, 3 table

Construction Theory, Self-Replication, and the Halting Problem

This essay aims to propose construction theory, a new domain of theoretical research on machine construction, and use it to shed light on a fundamental relationship between living and computational systems. Specifically, we argue that self-replication of von Neumann's universal constructors holds a close similarity to circular computational processes of universal computers that appear in Turing's original proof of the undecidability of the halting problem. The result indicates the possibility of reinterpreting a self-replicating biological organism as embodying an attempt to solve the halting problem for a {\em diagonal} input in the context of construction. This attempt will never be completed because of the indefinite cascade of self-computation/construction, which accounts for the undecidability of the halting problem and also agrees well with the fact that life has maintained its reproductive activity for an indefinitely long period of time.Comment: 14 pages, 2 figures. Complexity, in pres