Complex Networks in Finance

D’Arcangelis, Anna Maria; Rotundo, Giulia

doi:10.1007/978-3-319-40803-3_9

Cited by 21 publications

(9 citation statements)

References 80 publications

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“…A complex network, which is based on physics and mathematics theory, can tackle complicated practical problems [ 11 ]. It is especially suitable for modeling, analysis, and calculation in complex finance systems [ 12 ]. Nowadays, the literature on applying complex networks to finance is growing in size, and complex networks have become important tools in the finance field [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

The Evolution Characteristics of Systemic Risk in China’s Stock Market Based on a Dynamic Complex Network

Shi

Zheng

Guo

et al. 2020

Entropy

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The stock market is a complex system with unpredictable stock price fluctuations. When the positive feedback in the market amplifies, the systemic risk will increase rapidly. During the last 30 years of development, the mechanism and governance system of China’s stock market have been constantly improving, but irrational shocks have still appeared suddenly in the last decade, making investment decisions risky. Therefore, based on the daily return of all a-shares in China, this paper constructs a dynamic complex network of individual stocks, and represents the systemic risk of the market using the average weighting degree, as well as the adjusted structural entropy, of the network. In order to eliminate the influence of disturbance factors, empirical mode decomposition (EMD) and grey relational analysis (GRA) are used to decompose and reconstruct the sequences to obtain the evolution trend and periodic fluctuation of systemic risk. The results show that the systemic risk of China’s stock market as a whole shows a downward trend, and the periodic fluctuation of systemic risk has a long-term equilibrium relationship with the abnormal fluctuation of the stock market. Further, each rise of systemic risk corresponds to external factor shocks and internal structural problems.

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Section: Introductionmentioning

confidence: 99%

The Evolution Characteristics of Systemic Risk in China’s Stock Market Based on a Dynamic Complex Network

Shi

Zheng

Guo

et al. 2020

Entropy

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“…This means that the structure of the economic networks can seriously influence their dynamic. While huge studies have been devoted to the occurrence and diffusion of the crisis [32,35,36] in complexity economics [37,38,39,40,41,42,43,44], analyses of the responses of the networks to the recovery plans are lacking.…”

Section: Discussionmentioning

confidence: 99%

Optimization of the post-crisis recovery plans in scale-free networks

Bahrami

Chinichian

Hosseiny

et al. 2020

Physica A: Statistical Mechanics and its Applications

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General Motors or a local business, which one is it better to be stimulated in post-crisis recessions, when government stimulation is meant to overcome recessions? Due to the budget constraints, it is quite relevant to ask how government can increase the chance of economic recovery. One of the key elements to answer this question is to understand metastable features of crises in economic networks and their related hysteresis. The Ising model has been suggested for studying such features. In the homogenous networks, one needs at least a minimum budget, to force the network to switch its local equilibria, where such a minimum is independent of the network characteristics such as the average degree. In the scale free networks however, when the government aims to push the network to switch to another equilibrium, one may wonder which nodes are to be preferably stimulated in order to minimize the cost. In this paper, it is shown that stimulation of high degree nodes costs less in general. It is also found that in scale free networks, the stimulation cost depends on the networks features such as its assortativity. Although we confine our study to the Ising model in order to tackle a problem in economics, our analysis shines lights on many other problems concerning stimulations of socio-economic systems where dynamical hysteresis appears.

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“…Nevertheless, although network theory has been amply used in other sciences, including some successfully applications in various areas of economics, the literature on financial networks is still at an initial phase (Allen & Babus, 2009;Battison et al, 2016a). Moreover, most of the existing research applying network theory has mainly dealt with banking and financial institutions (Allen & Gale, 2000;Battison et al, 2016b;Braverman Minca;Kojaku et al, 2018;Fukker, 2018), management (Sharma Chopra, 2013;D'Arcangelis & Rotundo, 2016), insurance (Lin, Yu & Peterson, 2015) and financial regulation (Tennant, 2017;Battison, 2016a).…”

Section: Recent Related Literaturementioning

confidence: 99%

International Financial US Linkages: Networks Theory and MS-VAR Analyses

Sosa

Ortíz

Cabello

2019

REMEF

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This paper aims to examine the impact of the Global Financial Crisis on portfolio investment flows, as well as on stock market activity. Network Theory is used to analyze structural changes of foreign portfolio investment flows (FPI) to a sample of 13 developed countries and 6 emerging Latin American countries. Additionally, using daily data from 2003 to 2015, the dynamics of returns are analyzed to test whether the US market influenced these markets or vice versa; univariate (MS-AR) and multivariate (MS-VAR) regime-switching models are used. The evidence confirms the presence of two different regimes, low volatility and a high volatility for all markets. Findings suggest strengthening local productive and financial institutions in order to anchor FPI. The MS-(V)AR study is limited to stock markets from the Americas and Europe. Previous literature has not applied the innovative and complementary methodologies employed here to analyze financial crisis impacts on FPI flows. We conclude that US financial markets keep a close financial relationship with the most important European and American countries’ stock markets, both by receiving and delivering FPI, and in addition influencing the behavior of stock indexes.

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Complex Networks in Finance

Cited by 21 publications

References 80 publications

The Evolution Characteristics of Systemic Risk in China’s Stock Market Based on a Dynamic Complex Network

The Evolution Characteristics of Systemic Risk in China’s Stock Market Based on a Dynamic Complex Network

Optimization of the post-crisis recovery plans in scale-free networks

International Financial US Linkages: Networks Theory and MS-VAR Analyses

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