Influence Networks in the Foreign Exchange Market

Sousa, Arthur Matsuo Yamashita Rios de; Takayasu, Hideki; Takayasu, Misako

doi:10.1007/978-3-319-20591-5_1

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…We perform a local analysis in the sign time series through a sliding window procedure: In each window of length w we assume the time series can be described by a stationary sign binary Markov process and compute the probability of a symmetric process; we can then classify each window as corresponding to a symmetric process or not. Modeling the time series of the sign of price changes as Markov process is a consistent assumption from sampling time 0.4 s : It has been shown that the autocorrelation function for the price difference in this market goes to zero after few ticks [40]; also in S5 Appendix we observe that the auto mutual information [41] from the mentioned sampling time has similar behavior for the present data set. Results shown here refer to sampling time 0.4 s .…”

Section: Statistical Symmetries For Sign Time Series Of Usd/jpysupporting

confidence: 62%

Detection of statistical asymmetries in non-stationary sign time series: Analysis of foreign exchange data

Sousa

Takayasu

2017

PLoS ONE

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We extend the concept of statistical symmetry as the invariance of a probability distribution under transformation to analyze binary sign time series data of price difference from the foreign exchange market. We model segments of the sign time series as Markov sequences and apply a local hypothesis test to evaluate the symmetries of independence and time reversion in different periods of the market. For the test, we derive the probability of a binary Markov process to generate a given set of number of symbol pairs. Using such analysis, we could not only segment the time series according the different behaviors but also characterize the segments in terms of statistical symmetries. As a particular result, we find that the foreign exchange market is essentially time reversible but this symmetry is broken when there is a strong external influence.

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Section: Statistical Symmetries For Sign Time Series Of Usd/jpysupporting

confidence: 62%

Detection of statistical asymmetries in non-stationary sign time series: Analysis of foreign exchange data

Sousa

Takayasu

2017

PLoS ONE

Self Cite

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Financial complex network model based on textual mutual information

Yan-Feng¹,

Chao-Yong²

2018

Acta Phys. Sin.

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Complex networks are widely used in many problems of the financial field. It can be used to find the topological structure properties of the financial markets and to embody the interdependence between different financial entities. The correlation is important to create the complex networks of the financial markets. A novel approach to incorporating textual mutual information into financial complex networks as a measure of the correlation coefficient is developed in the paper. We will symbolize the multivariate financial time series firstly, and then calculate correlation coefficient with textual mutual information. Finally, we will convert it into a distance. To test the proposed method, four complex network models will be built with different correlation coefficients (Pearson's and textual mutual information's) and different network simplification methods (the threshold and minimum spanning tree). In addition, for the threshold networks, a quantile method is proposed to estimate the threshold automatically. The correlation coefficients are divided into 6 equal parts. And the midpoint of the 4th interval will be taken as the threshold according to our experience, which can make the MI methods and Pearson methods have the closest number of edges to compare the two methods. The data come from the closing prices of Chinese regional indexes including both Shanghai and Shenzhen stock market. The data range from January 4, 2006 to December 30, 2016, including 2673 trading days. In view of node correlation, the numerical results show that there are about 20% of the nonlinear relationships of the Chinese regional financial complex networks. In view of the network topology, four topological indicators for the regional financial complex network models will be calculated in the paper. For average weighted degree, the novel method can make the reserved nodes closely compared with Pearson's correlation coefficient. For network betweenness centralization, it can improve the betweenness importance of reserved nodes effectively. From the perspective of modularity, the novel method can detect better community structures. Finally, in dynamic network topology features, the data of regional indexes will be equally divided yearly for constructing complex network separately. The simplification method used in the section is the threshold method. The numerical results show that the proposed methods can successfully capture the two-abnormal fluctuation in the sample interval with the dynamics of the small-world and the network degree centralization. In addition, we find that the proposed regional financial network models follow the power-law distribution and are dynamically stable. Some developing regions are more important than the developed ones in the regional financial networks.

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Influence Networks in the Foreign Exchange Market

Cited by 2 publications

References 12 publications

Detection of statistical asymmetries in non-stationary sign time series: Analysis of foreign exchange data

Detection of statistical asymmetries in non-stationary sign time series: Analysis of foreign exchange data

Financial complex network model based on textual mutual information

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