Dynamics of essential interaction between firms on financial reports


Autoři: Hayato Goto aff001;  Eduardo Viegas aff001;  Hideki Takayasu aff002;  Misako Takayasu aff002;  Henrik Jeldtoft Jensen aff001
Působiště autorů: Centre for Complexity Science and Department of Mathematics, Imperial College London, London, United Kingdom aff001;  Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan aff002;  Sony Computer Science Laboratories, Tokyo, Japan aff003
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225853

Souhrn

Companies tend to publish financial reports in order to articulate strategies, disclose key performance measurements as well as summarise the complex relationships with external stakeholders as a result of their business activities. Therefore, any major changes to business models or key relationships will be naturally reflected within these documents, albeit in an unstructured manner. In this research, we automatically scan through a large and rich database, containing over 400,000 reports of companies in Japan, in order to generate structured sets of data that capture the essential features, interactions and resulting relationships among these firms. In doing so, we generate a citation type network where we empirically observe that node creation, annihilation and link rewiring to be the dominant processes driving its structure and formation. These processes prompt the network to rapidly evolve, with over a quarter of the interactions between firms being altered within every single calendar year. In order to confirm our empirical observations and to highlight and replicate the essential dynamics of each of the three processes separately, we borrow inspiration from ecosystems and evolutionary theory. Specifically, we construct a network evolutionary model where we adapt and incorporate the concept of fitness within our numerical analysis to be a proxy real measure of a company’s importance. By making use of parameters estimated from the real data, we find that our model reliably replicates degree distributions and motif formations of the citation network, and therefore reproducing both macro as well as micro, local level, structural features. This is done with the exception of the real frequency of bidirectional links, which are primarily formed as a result of an entirely separate and distinct process, namely the equity investments from one company into another.

Klíčová slova:

Finance – Metabolic networks – Network analysis – Network motifs – Probability density – Probability distribution – Simulation and modeling – Test statistics


Zdroje

1. Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang DU. Complex networks: Structure and dynamics. Phys Rep 2006; 424(4):175–308. doi: 10.1016/j.physrep.2005.10.009

2. Schweitzer F, Fagiolo G, Sornette D, Vega-Redondo F, Vespignani A, White DR. Economic networks: The new challenges. Science 2009; 325(5939):422–425. doi: 10.1126/science.1173644 19628858

3. Jackson MO. Social and economic networks. Princeton university press; 2010.

4. Watts DJ, Strogatz SH. Collective dynamics of ‘small-world’ networks. Nature 1998; 393: 440–442. doi: 10.1038/30918 9623998

5. Barabási AL, Albert R. Emergence of scaling in random networks. Science 1999; 286(5439):509–512. doi: 10.1126/science.286.5439.509 10521342

6. Stanley MH, Amaral LA, Buldyrev SV, Havlin S. Scaling behaviour in the growth of companies. Nature 1996; 379(6568):804–806. doi: 10.1038/379804a0

7. Amaral LAN, Buldyrev SV, Havlin S, Leschhorn H, Maass P, Salinger MA, Stanley HE, Stanley MH. Scaling behavior in economics: I. Empirical results for company growth. J Phys I France 1997; 7(4):621–633. doi: 10.1051/jp1:1997180

8. Takayasu H, Okuyama K. Country dependence on company size distributions and a numerical model based on competition and cooperation. Fractals 1998; 6(01):67–79. doi: 10.1142/S0218348X98000080

9. Axtell RL. Zipf distribution of US firm sizes. Science 2001; 293(5536):1818–1820. doi: 10.1126/science.1062081 11546870

10. Fu D, Pammolli F, Buldyrev SV, Riccaboni M, Matia K, Yamasaki K, Stanley HE. The growth of business firms: Theoretical framework and empirical evidence. Proc Natl Acad Sci USA 2005; 102(52):18801–18806. doi: 10.1073/pnas.0509543102 16365284

11. Takayasu M, Sameshima S, Watanabe H, Ohnishi T, Iyatomi H, Iino T, Kobayashi Y, Kamehama K, Ikeda Y, Takayasu H, Watanabe K. Massive Economics Data Analysis by Econophysics Methods-The case of companies’ network structure. Annu Rep Earth Simul Cent 2008;263–268.

12. Watanabe H, Takayasu H, Takayasu M. Relations between allometric scalings and fluctuations in complex systems: The case of Japanese firms. Phys A 2013; 392(4):741–756. doi: 10.1016/j.physa.2012.10.020

13. Takayasu M, Watanabe H, Takayasu H. Generalised central limit theorems for growth rate distribution of complex systems. J Stat Phys 2014; 155(1):47–71. doi: 10.1007/s10955-014-0956-4

14. Daepp MI, Hamilton MJ, West GB, Bettencourt LM, The mortality of companies. J R Soc Interface 2015; 464: 20150120. doi: 10.1098/rsif.2015.0120

15. Goto H, Viegas E, Henrik JJ, Takayasu H, Takayasu M. Appearance of Unstable Monopoly State Caused by Selective and Concentrative Mergers in Business Networks. Sci Rep 2017; 7: 5064. doi: 10.1038/s41598-017-05362-5

16. Atalay E, Hortacsu A, Roberts J, Syverson C. Network structure of production. Proc Natl Acad Sci USA 2011; 201015564.

17. Vitali S, Glattfelder JB, Battiston S. The network of global corporate control. PloS one 2011; 6(10):e25995. doi: 10.1371/journal.pone.0025995 22046252

18. Miura W, Takayasu H, Takayasu M. Effect of coagulation of nodes in an evolving complex network. Phys Rev Lett 2012; 108(16):168701. doi: 10.1103/PhysRevLett.108.168701 22680760

19. Mizuno T, Ohnishi T, Watanabe T. Structure of global buyer-supplier networks and its implications for conflict minerals regulations. EPJ Data Science 2016; 5(1):2. doi: 10.1140/epjds/s13688-016-0063-7

20. Goto H, Takayasu H, Takayasu M. Estimating risk propagation between interacting firms on inter-firm complex network. PloS one 2017; 12(10):e0185712. doi: 10.1371/journal.pone.0185712 28972998

21. Goto H, Takayasu H, Takayasu M. Empirical analysis of firm-dynamics on japanese interfirm trade network. Social Modeling and Simulation plus Econophysics Colluquium 2014, Proc. Int. Conf.: 195–204.

22. DeZoort FT, Wilkins A, Justice SE. The effect of sme reporting framework and credit risk on lenders’ judgments and decisions. J Account Public Policy 2017; 36: 302–315. doi: 10.1016/j.jaccpubpol.2017.05.003

23. Akins B. Financial reporting quality and uncertainty about credit risk among ratings agencies. Account Rev 2018; 93: 1–22. doi: 10.2308/accr-51944

24. Viegas E, Goto H, Henrik JJ, Takayasu H, Takayasu M. Assembling real networks from synthetic and unstructured subsets: the corporate reporting case. Sci Rep 2019; Forthcoming. doi: 10.1038/s41598-019-47490-0

25. Teikoku Databank, Ltd. A sample report of Corporate Credit Research. 2017; Available from: https://www.teikoku.com/company-credit-reports.

26. Caldarelli G, Capocci A, De Los Rios P, Munoz MA. Scale-free networks from varying vertex intrinsic fitness. Phys Rev Lett 2002; 89(25):258702. doi: 10.1103/PhysRevLett.89.258702 12484927

27. Goto H, Viegas E, Henrik JJ, Takayasu H, Takayasu M. Smoluchowski Equation for Networks: Merger Induced Intermittent Giant Node Formation and Degree Gap. J Stat Phys 2018;1–15.

28. Kudo T. Mecab: Yet another part-of-speech and morphological analyzer. 2006; Available from: http://mecab.sourceforge.jp.

29. Simon HA. On a class of skew distribution functions. Biometrika 1955; 42(3/4):425–440. doi: 10.2307/2333389

30. Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U. Network motifs: simple building blocks of complex networks. Science 2002; 298(5594):824–827. doi: 10.1126/science.298.5594.824 12399590

31. Milo R, Itzkovitz S, Kashtan N, Levitt R, Shen-Orr S, Ayzenshtat I, Sheffer M, Alon U. Superfamilies of evolved and designed networks. Science 2004; 303(5663):1538–1542. doi: 10.1126/science.1089167 15001784

32. Milo R, Kashtan N, Itzkovitz S, Newman MEJ, Alon U. On the uniform generation of random graphs with prescribed degree sequences. arXiv preprint 2003; cond-mat/0312028.

33. Ohnishi T, Takayasu H, Takayasu M. Network motifs in an inter-firm network. J Econ Interact Coor 2010; 5(2):171–180. doi: 10.1007/s11403-010-0066-6

34. Maluck J, Donner RV, Takayasu H, Takayasu M. Motif formation and industry specific topologies in the Japanese business firm network. J Stat Mech-Theory E 2017; 5: 053404. doi: 10.1088/1742-5468/aa6ddb

35. Albert R, Jeong H, Barabási AL. Topology of evolving networks: local events and universality. Phys Rev Lett 2000; 85(24):5234. doi: 10.1103/PhysRevLett.85.5234 11102229

36. Moore C, Ghoshal G, Newman ME. Exact solutions for models of evolving networks with addition and deletion of nodes. Phys Rev E 2006; 74(3):036121. doi: 10.1103/PhysRevE.74.036121

37. Jeong H, Néda Z, Barabási AL. Measuring preferential attachment in evolving networks. EPL 2013; 61(4):567. doi: 10.1209/epl/i2003-00166-9

38. Hines TM. International Financial Reporting Standards: A Guide to Sources for International Accounting Standards. J Bus Financ Librariansh 2007; 12(3):3–26. doi: 10.1300/J109v12n03_02

39. Massey FJ Jr. The Kolmogorov-Smirnov test for goodness of fit. J Am Stat Assoc 1951; 46(253):68–78. doi: 10.1080/01621459.1951.10500769

40. Darling DA. The kolmogorov-smirnov, cramer-von mises tests. Ann Math Stat 1957; 28(4):823–838. doi: 10.1214/aoms/1177706788

41. Farrell PJ, Rogers-Stewart K. Comprehensive study of tests for normality and symmetry: extending the Spiegelhalter test. J Stat Comput Simul 2006; 76(9):803–816. doi: 10.1080/10629360500109023

42. Razali NM, Wah YB. Power comparisons of shapiro-wilk, kolmogorov-smirnov, lilliefors and anderson-darling tests. J Stat Model Anal 2011; 2(1):21–33.


Článek vyšel v časopise

PLOS One


2019 Číslo 12