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

Minjun Kim, Hiroki Sayama

Research output: Contribution to journalArticle

2 Citations (Scopus)

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-min price movements of the pairs of the companies with the consecutive 5340 min 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 with and without inclusion of network measurements, and compared the predictive power of them. 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.

Original languageEnglish
Article number35
JournalApplied Network Science
Volume2
Issue number1
DOIs
Publication statusPublished - 2017 Dec 1
Externally publishedYes

Fingerprint

Information science
Network Measurement
Stock Market
ARIMA Models
Predictors
Prediction
Centrality
Complex networks
Financial Markets
Mutual Information
Complex Networks
Forecast
Linear Combination
Forecasting
Large scale systems
Consecutive
Time series
Industry
Complex Systems
Inclusion

Keywords

  • ARIMA
  • Complex systems
  • Flash crash detection
  • Information theory
  • Kullback-Leibler divergence
  • Mutual information
  • Networks science
  • Stock market networks
  • Stock market prediction
  • Strength distribution

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computational Mathematics
  • General

Cite this

Predicting stock market movements using network science : an information theoretic approach. / Kim, Minjun; Sayama, Hiroki.

In: Applied Network Science, Vol. 2, No. 1, 35, 01.12.2017.

Research output: Contribution to journalArticle

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