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: https://doi.org/10.1371/journal.pone.0225853


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


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