Financial networks with static and dynamic thresholds

Qiu, Tian; Zheng, Bo; Chen, Guang

doi:10.1088/1367-2630/12/4/043057

Cited by 57 publications

(37 citation statements)

References 50 publications

(42 reference statements)

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“…Several efforts have been made to study stock market from the perspective of complex networks26272829303132. In these networks, nodes are companies, stocks, and even time series of stock prices.…”

Section: Methodsmentioning

confidence: 99%

Trading Network Predicts Stock Price

Sun

Shen

Xue

2014

Sci Rep

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Stock price prediction is an important and challenging problem for studying financial markets. Existing studies are mainly based on the time series of stock price or the operation performance of listed company. In this paper, we propose to predict stock price based on investors' trading behavior. For each stock, we characterize the daily trading relationship among its investors using a trading network. We then classify the nodes of trading network into three roles according to their connectivity pattern. Strong Granger causality is found between stock price and trading relationship indices, i.e., the fraction of trading relationship among nodes with different roles. We further predict stock price by incorporating these trading relationship indices into a neural network based on time series of stock price. Experimental results on 51 stocks in two Chinese Stock Exchanges demonstrate the accuracy of stock price prediction is significantly improved by the inclusion of trading relationship indices.

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Section: Methodsmentioning

confidence: 99%

Trading Network Predicts Stock Price

Sun

Shen

Xue

2014

Sci Rep

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“…The earliest was joint multifractal analysis to study the cross-multifractal nature of two joint multifractal measures through the scaling behaviors of the joint moments [1,2,[18][19][20], which is a multifractal cross-correlation analysis based on the partition function approach (MF-X-PF) [21]. Over the past decade, detrended cross-correlation analysis (DCCA) has become the most popular method of investigating the long-range power-law cross correlations between two nonstationary time series [14,[22][23][24], and this method has numerous variants [25][26][27][28][29][30][31][32][33]. Statistical tests can be used to measure these cross correlations [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Detrended partial cross-correlation analysis of two nonstationary time series influenced by common external forces

et al. 2015

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When common factors strongly influence two power-law cross-correlated time series recorded in complex natural or social systems, using classic detrended cross-correlation analysis (DCCA) without considering these common factors will bias the results. We use detrended partial cross-correlation analysis (DPXA) to uncover the intrinsic power-law cross-correlations between two simultaneously recorded time series in the presence of nonstationarity after removing the effects of other time series acting as common forces. The DPXA method is a generalization of the detrended cross-correlation analysis that takes into account partial correlation analysis. We demonstrate the method by using bivariate fractional Brownian motions contaminated with a fractional Brownian motion. We find that the DPXA is able to recover the analytical cross Hurst indices, and thus the multi-scale DPXA coefficients are a viable alternative to the conventional cross-correlation coefficient. We demonstrate the advantage of the DPXA coefficients over the DCCA coefficients by analyzing contaminated bivariate fractional Brownian motions. We calculate the DPXA coefficients and use them to extract the intrinsic cross-correlation between crude oil and gold futures by taking into consideration the impact of the US dollar index. We develop the multifractal DPXA (MF-DPXA) method in order to generalize the DPXA method and investigate multifractal time series. We analyze multifractal binomial measures masked with strong white noises and find that the MF-DPXA method quantifies the hidden multifractal nature while the MF-DCCA method fails.

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“…Complex dynamic systems may usually be non-stationary, or at least with features of non-stationary states [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. For example, the auto-correlation of the dynamic fluctuations is rather strong in a large class of complex dynamic systems [18-20, 22, 29, 30].…”

Section: Introductionmentioning

confidence: 99%

Temporal correlation functions of dynamic systems in non-stationary states

Chen

Zheng

et al. 2018

New J. Phys.

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Besides the fluctuation-dissipation theorem around the stationary state, how the fluctuations in nonstationary states correlate with the future motion of the dynamic variables remains a challenging problem. Further, most temporal correlations of the dynamic variables are zero or very weak in a large class of dynamic systems if the dynamic effects of non-stationary states are not considered. We propose novel methods to compute the temporal correlation functions taking into account the dynamic effects of the non-stationary states. In various dynamic systems, we reveal that the past dynamic fluctuations do drive the future motion of the dynamic variables. This dynamic effect of the non-stationary states is a robust, intrinsic and important property of the complex dynamic systems.

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Financial networks with static and dynamic thresholds

Cited by 57 publications

References 50 publications

Trading Network Predicts Stock Price

Trading Network Predicts Stock Price

Detrended partial cross-correlation analysis of two nonstationary time series influenced by common external forces

Temporal correlation functions of dynamic systems in non-stationary states

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