Reconstructing a complex financial network using compressed sensing based on low-frequency time series data

Zhang, Jinsheng; Gao, Xiangyun; Wang, Ze; Wu, Tao; Yiran, Zhao

doi:10.1016/j.frl.2022.103097

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…In a corporate debt network, an enterprise usually only has debt relationships with a few other enterprises, and the column vectors of the link matrix are sparse, which provides possibility for the debt network reconstruction by compressed sensing 13 . Si et al used compressed sensing to construct a complex financial network and improved the reconstruction accuracy 14 , 15 . The debt relationships among enterprises can be expressed through a network.…”

Section: Reconstruction Model Of Enterprise Debt Networkmentioning

confidence: 99%

Reconstruction of enterprise debt networks based on compressed sensing

Liang

Zhang

et al. 2023

Sci Rep

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This study aims at the problem of reconstruction the unknown links in debt networks among enterprises. We use the topological matrix of the enterprise debt network as the object of reconstruction and use the time series data of accounts receivable and payable as input and output information in the debt network to establish an underdetermined linear system about the topological matrix of the debt network. We establish an iteratively reweighted least-squares algorithm, which is an algorithm in compressed sensing. This algorithm uses reweighted $$\ell _2$$ ℓ 2 -minimization to approximate $$\ell _1$$ ℓ 1 -norm of the target vectors. We solve the $$\ell _1$$ ℓ 1 -minimization problem of the underdetermined linear system using the iteratively reweighted least-squares algorithm and obtain the reconstructed topological matrix of the debt network. Simulation experiments show that the topology matrix reconstruction method of enterprise debt networks based on compressed sensing can reconstruct over 70% of the unknown network links, and the error is controlled within 2%.

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Section: Reconstruction Model Of Enterprise Debt Networkmentioning

confidence: 99%

Reconstruction of enterprise debt networks based on compressed sensing

Liang

Zhang

et al. 2023

Sci Rep

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“…A complex network of stock price jumps was constructed using the minimum spanning tree algorithm, revealing significant correlations among stocks in the entire jump network, with manufacturing stocks playing the most crucial role (Hu et al, 2019). In the study (Si et al, 2022), the main research focused on constructing a complex financial network equivalent to highfrequency data using low-frequency time series data, and proposed an improved compressive sensing method. Based on financial network information variables and genetic algorithms, Liu et al (2019) proposed a method that combines network variables and GA-optimized Gradient Boosting Decision Tree (GBDT), referred to as FNI-GA-GBDT.…”

Section: Introductionmentioning

confidence: 99%

Transmission Risk of Stock Price Fluctuations Among Industries in Complex Financial Networks

Wang,

Ding,

Zhou

2024

JAFAS

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In recent years, many researchers have studied some complex phenomena in the world from a new angle, and the subject of complexity science was born. In the field of complexity research, the study of social economics is very common. Because of the characteristics of financial market and its position in social economy, it is very important in complexity research. Based on complex network theory and vector autoregressive model (VAR) study of the stock market fluctuation and discusses the conduction effect between stock volatility in the industry on the influence of the correlation of share price volatility, aims to better understand the operating mechanism of the stock market, the more effective controlling the market development direction, to promote the healthy development of China's financial markets. In this paper, the rise and fall of stocks in 11 industries in the CSI 300 industry index are selected as variables, and the data from January 1, 2024, to March 1, 2024, are selected as research samples. Granger test and vector autoregressive model are used to calculate the conduction benefits between different industries. The complex network is constructed with industry as node and stock conduction direction as edge.Based on the analysis, we can find that in the CSI 300 industry index, there are several industries as the influence center of volatility, and their stock price fluctuations will affect the market of related industries, thus affecting the whole body. Based on the market situation of related industries in recent years, the correlation of fluctuations of different stocks in the stock market can be explained, and the influence of industry factors on stock price fluctuations can be deeply explored.

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A link prediction method based on compressed sensing for social networks

Yang,

2023

Appl Intell

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Reconstructing a complex financial network using compressed sensing based on low-frequency time series data

Cited by 3 publications

References 27 publications

Reconstruction of enterprise debt networks based on compressed sensing

Reconstruction of enterprise debt networks based on compressed sensing

Transmission Risk of Stock Price Fluctuations Among Industries in Complex Financial Networks

A link prediction method based on compressed sensing for social networks

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