Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
184 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Network characteristics of financial networks (2311.17567v1)

Published 29 Nov 2023 in cs.CE

Abstract: We embrace a fresh perspective to auditing by analyzing a large set of companies as complex financial networks rather than static aggregates of balance sheet data. Preliminary analyses show that network centrality measures within these networks could significantly enhance auditors' insights into financial structures. Utilizing data from over 300 diverse companies, we examine the structure of financial statement networks through bipartite graph analysis, exploring their scale-freeness by comparing degree distributions to power-law and exponential models. Our findings indicate heavy-tailed degree distribution for financial account nodes, networks that grow with the same diameter, and the presence of influential hubs. This study lays the groundwork for future auditing methodologies where baseline network statistics could serve as indicators for anomaly detection, marking a substantial advancement in audit research and network science.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (36)
  1. Global auditing services industry (2020 to 2027) - market trends and drivers - researchandmarkets.com (2020).
  2. Financial statement networks: an application of network theory in audit. \JournalTitleJournal of Network Theory in Finance 4, 59–85 (2018).
  3. Freeman, L. C. Centrality in social networks conceptual clarification. \JournalTitleSoc. Networks 1, 215–239 (1978).
  4. Community structure in social and biological networks. \JournalTitleProceedings of the national academy of sciences 99, 7821–7826 (2002).
  5. Statistical mechanics of complex networks. \JournalTitleRev. Mod. Phys. 74, 47–97 (2002).
  6. Bonneau, R. Learning biological networks: From modules to dynamics. \JournalTitleNat. Chem. Biol. 4, 658–664 (2008).
  7. Neural message passing for quantum chemistry. In International conference on machine learning, 1263–1272 (PMLR, 2017).
  8. Complex brain networks: graph theoretical analysis of structural and functional systems. \JournalTitleNature reviews neuroscience 10, 186–198 (2009).
  9. Emergence of scaling in random networks. \JournalTitlescience 286, 509–512 (1999).
  10. Newman, M. E. J. The structure and function of complex networks. \JournalTitleSIAM Rev. 45, 167–256 (2003).
  11. Collective dynamics of ‘small-world’networks. \JournalTitlenature 393, 440–442 (1998).
  12. Small Worlds Among Interlocking Directors: Network Structure and Distance in Bipartite Graphs. \JournalTitleComputational & Mathematical Organization Theory 10, 69–94 (2004).
  13. The small world network structure of boards of directors. \JournalTitleAvailable at SSRN 546963 (2004).
  14. Statistical properties of corporate board and director networks. \JournalTitleEur. Phys. J. B 38, 345–352 (2004).
  15. Newman, M. E. Scientific collaboration networks. i. network construction and fundamental results. \JournalTitlePhys. Rev. E Stat. Nonlin. Soft Matter Phys. 64, 016131 (2001).
  16. For the few not the many? the effects of affirmative action on presence, prominence, and social capital of women directors in norway. \JournalTitleScandinavian Journal of Management 27, 44–54 (2011).
  17. Emergent properties of a new financial market: American venture capital syndication, 1960–2005. \JournalTitleManage. Sci. 53, 1181–1198 (2007).
  18. Emergence of complexity in financial networks. In Ben-Naim, E., Frauenfelder, H. & Toroczkai, Z. (eds.) Complex Networks, 399–423 (Springer Berlin Heidelberg, Berlin, Heidelberg, 2004).
  19. Bipartite producer–consumer networks and the size distribution of firms. \JournalTitlePhysica A: Statistical Mechanics and its Applications 363, 359–366 (2006).
  20. The scale-free topology of market investments (2005).
  21. Reality mining: sensing complex social systems. \JournalTitlePers. Ubiquit. Comput. 10, 255–268 (2006).
  22. Scaling and statistical models for affiliation networks: patterns of participation among soviet politicians during the brezhnev era. \JournalTitleSoc. Networks 24, 231–259 (2002).
  23. Improving recommendation lists through topic diversification. In Proceedings of the 14th international conference on World Wide Web, WWW ’05, 22–32 (Association for Computing Machinery, New York, NY, USA, 2005).
  24. Newman, M. E. Scientific collaboration networks. ii. shortest paths, weighted networks, and centrality. \JournalTitlePhysical review E 64, 016132 (2001).
  25. Epistemic communities: Description and hierarchic categorization. \JournalTitleMath. Popul. Stud. 12, 107–130 (2005).
  26. Network analysis of 2-mode data. \JournalTitleELSEVIER Social Networks 19, 243–269 (1997).
  27. Basic notions for the analysis of large two-mode networks. \JournalTitleSoc. Networks 30, 31–48 (2008).
  28. Borgatti, S. P. 2-mode concepts in social network analysis. \JournalTitleEncyclopedia of complexity and system science 6, 8279–8291 (2009).
  29. Freeman, L. C. A set of measures of centrality based on betweenness. \JournalTitleSociometry 40, 35–41 (1977).
  30. Faust, K. Centrality in affiliation networks. \JournalTitleSoc. Networks 19, 157–191 (1997).
  31. An analysis of the japanese credit network. \JournalTitleEvolutionary and Institutional Economics Review 7, 209–232 (2011).
  32. Publishing: Handful of papers dominates citation. \JournalTitleNature 491, 40–40 (2012).
  33. Power-law distributions in empirical data. \JournalTitleSIAM review 51, 661–703 (2009).
  34. powerlaw: a python package for analysis of heavy-tailed distributions. \JournalTitlePloS one 9, e85777 (2014).
  35. The reduction of a graph to canonical form and the algebra which appears therein. https://www.iti.zcu.cz/wl2018/pdf/wl_paper_translation.pdf (1968). Accessed: 2023-2-24.
  36. Wasserstein weisfeiler-lehman graph kernels. \JournalTitlearXiv preprint arXiv:1906.01277 (2019).

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now