企业信用内生网络模型及其演化研究
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摘要
本文考虑上游企业和下游企业两个部门,通过构建企业内生网络模型研究企业间信用关联内在形成机制及其演化特征。通过对内生网络模型仿真研究,结果表明本文构建的模型重现了现实企业系统存在的一些特征:企业信用网络度分布服从幂律分布,该网络具有无标度特征;较长的合并周期则能更好地反映企业间信用关系,且随着合并周期变大,网络密度也显著增大;企业资产规模分布具有幂律尾部特征,企业资产增长率随时间演化逐渐呈收敛状,且其概率分布近似于正态分布。
There are many relationships among enterprises in the socio-economic system,such as credit relationships and guarantees relationships among enterprises,which forms complex connections among enterprises.Theses complex connections bring many economic benefits to the development of enterprises,butthey also provide a medium for risk contagion among enterprises.Network theory provides a new research perspective for the inter-enterprise correlations.Based on actual data,it has found that there are some typical network structural characteristics among enterprises,such as scale-free networks.Revealing the micro-mechanism of the formation of enterprise networks helps to understand their structural characteristics.Therefore,in this paper,the upstream and downstream enterprises are considered,and an endogenous network model is constructed to study the internal formation mechanism and evolvement characteristics of inter-enterprise credit connections.Through simulation analysis of the endogenous network model,the results show that the model constructed in this paper reproduces the characteristics of the enterprise system in real life:the degree distribution of enterprise credit network is a power-law distribution,and this means the network is a scale-free network;longer aggregation periods might be preferable to have a better description of inter-enterprise credit relationships,and the network density increases with the increase of the aggregation period;the distribution of enterprise assets has the power-law tail,and the asset growth rate of enterprises gradually converges with time,and its probability distribution is approximate to the normal distribution.The key research of this paper is to explain the formation mechanism of enterprise credit network,which is one of the main contents of network theory research.Moreover,this study is the basis for further research on related practical issues,such as constructing enterprise evolution systems based on network theory to study the effect of economic policies.
There are many relationships among enterprises in the socio-economic system,such as credit relationships and guarantees relationships among enterprises,which forms complex connections among enterprises.Theses complex connections bring many economic benefits to the development of enterprises,butthey also provide a medium for risk contagion among enterprises.Network theory provides a new research perspective for the inter-enterprise correlations.Based on actual data,it has found that there are some typical network structural characteristics among enterprises,such as scale-free networks.Revealing the micro-mechanism of the formation of enterprise networks helps to understand their structural characteristics.Therefore,in this paper,the upstream and downstream enterprises are considered,and an endogenous network model is constructed to study the internal formation mechanism and evolvement characteristics of inter-enterprise credit connections.Through simulation analysis of the endogenous network model,the results show that the model constructed in this paper reproduces the characteristics of the enterprise system in real life:the degree distribution of enterprise credit network is a power-law distribution,and this means the network is a scale-free network;longer aggregation periods might be preferable to have a better description of inter-enterprise credit relationships,and the network density increases with the increase of the aggregation period;the distribution of enterprise assets has the power-law tail,and the asset growth rate of enterprises gradually converges with time,and its probability distribution is approximate to the normal distribution.The key research of this paper is to explain the formation mechanism of enterprise credit network,which is one of the main contents of network theory research.Moreover,this study is the basis for further research on related practical issues,such as constructing enterprise evolution systems based on network theory to study the effect of economic policies.
引文
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[2]隋聪,谭照林,王宗尧.基于网络视角的银行业系统性风险度量方法[J].中国管理科学,2016,24(5):54-64.
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[15]Mizuno T,Ohnishi T,Watanabe T.Structure of global buyer-supplier networks and its implications for conflict minerals regulations[J].EPJ Data Science,2016,5(1):1-15.
[16]Golo N,Kelman G,Bree D S,et al.Many-to-one contagion of economic growth rate across trade credit network of firms[J].Quantitative Finance,2015.
[17]周伶,山峻,张津.联合投资网络位置对投资绩效的影响——来自风险投资的实证研究[J].管理评论,2014(12):160-169.
[18]吉艳冰,王伟,赵亚伟.基于复杂网络理论的担保网络研究[J].复杂系统与复杂性科学,2014,11(2):17-23.
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[20]Henriet F,Hallegatte S,Tabourier L.Firm-network characteristics and economic robustness to natural disasters[J].Journal of Economic Dynamics and Control,2012,36(1):150-167.
[21]吕一博,程露,苏敬勤.“资源导向”下企业集群网络演进的多主体仿真研究[J].系统工程学报,2013,28(1):8-18.
[22]张燕,徐福缘.基于复杂网络的企业关系网演变研究[J].科技与管理,2010,12(5):70-74.
[23]张峰,杨育,贾建国,等.企业协同生产网络的拓扑特性分析[J].重庆大学学报,2012,35(6):21-27.
[24]Gatti D D,Gallegati M,Greenwald B,et al.The financial accelerator in an evolving credit network[J].Journal of Economic Dynamics and Control,2010,34(9):1627-1650.
[25]Riccetti L,Russo A,Gallegati M.Leveraged network-based financial accelerator[J].Journal of Economic Dynamics and Control,2013,37(8):1626-1640.
[26]Finger K,Fricke D,Lux T.Network analysis of the e-MID overnight money market:The informational value of different aggregation levels for intrinsic dynamic processes[J].Computational Management Science,2013,10(2-3):187-211.
[27]Lux T.Emergence of a core-periphery structure in a simple dynamic model of the interbank market[J].Journal of Economic Dynamics and Control,2015,52:A11-A23.
[28]Axtell R L.Zipf distribution of US firm sizes[J].Science,2001,293(5536):1818-1820.
[29]Fujiwara Y,Di Guilmi C,Aoyama H,et al.Do Pareto-Zipf and Gibrat laws hold true?An analysis with European firms[J].Physica A:Statistical Mechanics and its Applications,2004,335(1):197-216.
[30]He Jianmin,Sui Xin,Li Shouwei.An endogenous model of the credit network[J].Physica A:Statistical Mechanics and its Applications,2016,441:1-14.
[31]Tamura K,Miura W,Takayasu M,et al.Estimation of flux betweeninteracting nodes on huge interfirm networks[C]//International Journal of Modern Physics:Conference Series.World Scientific Publishing Company,2012,16:93-104.
[32]Ohnishi T,Takayasu H,Takayasu M.Hubs and authorities on Japanese interfirm network:Characterization of nodes in very large directed networks[J].Progress of Theoretical Physics Supplement,2009,179:157-166.
[2]隋聪,谭照林,王宗尧.基于网络视角的银行业系统性风险度量方法[J].中国管理科学,2016,24(5):54-64.
[3]陈庭强,李心丹,何建敏.基于熵空间交互理论的CRT网络信用风险传染模型[J].中国管理科学,2016,24(6):10-18.
[4]欧阳红兵,刘晓东.中国金融机构的系统重要性及系统性风险传染机制分析——基于复杂网络的视角[J].中国管理科学,2015,23(10):30-37.
[5]李守伟,何建敏.不同网络结构下银行间传染风险研究[J].管理工程学报,2012,26(4):71-76+99.
[6]卞曰瑭,李金生,何建敏,等.网络近邻择优策略下的股市羊群行为演化模型及仿真[J].中国管理科学,2013,21(3):40-49.
[7]隋聪,王宗尧.银行间网络的无标度特征[J].管理科学学报,2015,18(12):18-26.
[8]张古鹏.小世界创新网络动态演化及其效应研究[J].管理科学学报,2015,18(6):15-29.
[9]鲍勤,孙艳霞.网络视角下的金融结构与金融风险传染[J].系统工程理论与实践,2014,34(9):2202-2211.
[10]Watanabe H,Takayasu H,Takayasu M.Biased diffusion on the Japanese inter-firm trading network:Estimation of sales from the network structure[J].New Journal of Physics,2012,14(4):043034.
[11]Watanabe H,Takayasu H,Takayasu M.Relations between allometricscalings and fluctuations in complex systems:The case of Japanese firms[J].Physica A:Statistical Mechanics and its Applications,2013,392(4):741-756.
[12]Miura W,Takayasu H,Takayasu M.The origin of asymmetric behavior of money flow in the business firm network[J].The European Physical Journal Special Topics,2012,212(1):65-75.
[13]Mizuno T,Souma W,Watanabe T.The structure and evolution of buyer-supplier networks[J].PLoS one,2014,9(7):e100712.
[14]Ohnishi T,Takayasu H,Takayasu M.Network motifs in an inter-firm network[J].Journal of Economic Interaction and Coordination,2010,5(2):171-180.
[15]Mizuno T,Ohnishi T,Watanabe T.Structure of global buyer-supplier networks and its implications for conflict minerals regulations[J].EPJ Data Science,2016,5(1):1-15.
[16]Golo N,Kelman G,Bree D S,et al.Many-to-one contagion of economic growth rate across trade credit network of firms[J].Quantitative Finance,2015.
[17]周伶,山峻,张津.联合投资网络位置对投资绩效的影响——来自风险投资的实证研究[J].管理评论,2014(12):160-169.
[18]吉艳冰,王伟,赵亚伟.基于复杂网络理论的担保网络研究[J].复杂系统与复杂性科学,2014,11(2):17-23.
[19]沙浩伟,曾勇.交叉持股,网络位置与公司绩效的实证研究[J].管理科学,2014,27(1):131-142.
[20]Henriet F,Hallegatte S,Tabourier L.Firm-network characteristics and economic robustness to natural disasters[J].Journal of Economic Dynamics and Control,2012,36(1):150-167.
[21]吕一博,程露,苏敬勤.“资源导向”下企业集群网络演进的多主体仿真研究[J].系统工程学报,2013,28(1):8-18.
[22]张燕,徐福缘.基于复杂网络的企业关系网演变研究[J].科技与管理,2010,12(5):70-74.
[23]张峰,杨育,贾建国,等.企业协同生产网络的拓扑特性分析[J].重庆大学学报,2012,35(6):21-27.
[24]Gatti D D,Gallegati M,Greenwald B,et al.The financial accelerator in an evolving credit network[J].Journal of Economic Dynamics and Control,2010,34(9):1627-1650.
[25]Riccetti L,Russo A,Gallegati M.Leveraged network-based financial accelerator[J].Journal of Economic Dynamics and Control,2013,37(8):1626-1640.
[26]Finger K,Fricke D,Lux T.Network analysis of the e-MID overnight money market:The informational value of different aggregation levels for intrinsic dynamic processes[J].Computational Management Science,2013,10(2-3):187-211.
[27]Lux T.Emergence of a core-periphery structure in a simple dynamic model of the interbank market[J].Journal of Economic Dynamics and Control,2015,52:A11-A23.
[28]Axtell R L.Zipf distribution of US firm sizes[J].Science,2001,293(5536):1818-1820.
[29]Fujiwara Y,Di Guilmi C,Aoyama H,et al.Do Pareto-Zipf and Gibrat laws hold true?An analysis with European firms[J].Physica A:Statistical Mechanics and its Applications,2004,335(1):197-216.
[30]He Jianmin,Sui Xin,Li Shouwei.An endogenous model of the credit network[J].Physica A:Statistical Mechanics and its Applications,2016,441:1-14.
[31]Tamura K,Miura W,Takayasu M,et al.Estimation of flux betweeninteracting nodes on huge interfirm networks[C]//International Journal of Modern Physics:Conference Series.World Scientific Publishing Company,2012,16:93-104.
[32]Ohnishi T,Takayasu H,Takayasu M.Hubs and authorities on Japanese interfirm network:Characterization of nodes in very large directed networks[J].Progress of Theoretical Physics Supplement,2009,179:157-166.