Kim Minjun, Sayama Hiroki
1Department of Systems Science and Industrial Engineering, Binghamton University, State University of New York, Binghamton, 13902 New York USA.
2Center for Collective Dynamics of Complex Systems, Binghamton University, State University of New York, Binghamton, 13902 New York USA.
Appl Netw Sci. 2017;2(1):35. doi: 10.1007/s41109-017-0055-y. Epub 2017 Oct 10.
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.
股票市场被认为是高度复杂的系统之一，它由许多成分组成，其价格波动无常，没有明显的模式。股票市场的复杂性使我们难以对其未来走势做出可靠的预测。在本文中，我们旨在构建一种新方法来预测标准普尔500指数（S&P 500）的未来走势，即通过构建S&P 500成分公司的时间序列复杂网络，将它们用权重由成对公司连续5340分钟价格记录的60分钟价格变动的互信息给出的链接连接起来。我们表明，网络强度分布的变化为网络的未来走势提供了重要信息。我们利用强度分布和诸如中心性等网络测量构建了几个指标，并通过线性组合将最佳的两个预测指标结合起来。我们发现，组合预测指标与S&P 500的变化呈现二次关系，这使我们能够预测S&P 500一步未来变化的幅度。结果表明，当组合预测指标较高时，S&P 500指数出现显著波动。在进行实际指数预测方面，我们构建了包含和不包含网络测量的自回归积分移动平均（ARIMA）模型，并比较了它们的预测能力。我们发现，将网络测量添加到ARIMA模型中可提高模型准确性。这些发现对金融市场政策制定者来说是有用的指标，他们可以据此在市场发生剧烈变化之前对市场进行干预；对量化投资者来说，有助于改进他们的预测模型。
