Estimating risk propagation between interacting firms on inter-firm complex network.

Hayato Goto,Misako Takayasu,Hideki Takayasu,Tobias Preis

doi:10.1371/journal.pone.0185712

Hayato Goto, Misako Takayasu + Show 2 more

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https://doi.org/10.1371/journal.pone.0185712

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Journal: PloS one	Publication Date: Oct 3, 2017
Citations: 17	License type: CC BY 4.0

Affiliation: Tokyo Institute of Technology

Abstract

We derive a stochastic function of risk propagation empirically from comprehensive data of chain-reaction bankruptcy events in Japan from 2006 to 2015 over 5,000 pairs of firms. The probability is formulated by firm interaction between the pair of firms; it is proportional to the product of α-th power of the size of the first bankrupt firm and β-th power of that of the chain-reaction bankrupt firm. We confirm that α is positive and β is negative throughout the observing period, meaning that the probability of cascading failure is higher between a larger first bankrupt firm and smaller trading firm. We additionally introduce a numerical model simulating the whole ecosystem of firms and show that the interaction kernel is a key factor to express complexities of spreading bankruptcy risks on real ecosystems.

Highlights

It has been generally recognized that social and economic networks can be captured as complex systems whose nodes are individual agents that interact [1, 2]
By using a merger kernel estimated through an mergers and acquisitions (M&As) data analysis, the model reproduces business network characteristics with the parameter set estimated by real firm data [47]
We revise the model introducing a new effect of chain-reaction bankruptcy based on the empirically estimated kernel A(kf, kc) as follows; Step1 Start with N0 firms with real links given from the data of business transactions as shown in Table 1

Summary

Introduction

It has been generally recognized that social and economic networks can be captured as complex systems whose nodes are individual agents that interact [1, 2]. Economists and physicists have shown interest in the complexity underlying the systems and how they work [3,4,5,6,7,8,9,10] Their contributions provide profound new insights into such areas of interest as cascading failures and resilience [11,12,13,14,15,16,17,18,19,20] using the lens of the science of complex systems [21,22,23,24]. This network, whose nodes are firms that link through business transactions from customers and providers, producing a money flow (opposite from a goods/service flow) as shown in Fig 1(a), has statistical complex properties [25], such as small world property [26]

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