Hostname: page-component-8448b6f56d-dnltx Total loading time: 0 Render date: 2024-04-24T06:23:29.536Z Has data issue: false hasContentIssue false
  • English
  • Français

Analysis of financial events under an assumption of complexity

Published online by Cambridge University Press:  04 December 2018

Yifei Li  and
John Evans
Yifei Li
Affiliation:
Sydney Business School, University of Wollongong, Wollongong, New South Wales, Australia
John Evans*
Affiliation:
Centre for Analysis of Complex Financial System, Sydney, New South Wales, Australia
*
*Correspondence to: John Evans, Centre for Analysis of Complex Financial System, PO Box 363 Summer Hill, Sydney, New South Wales, Australia. E-mail: jevans@pgeaus.onmicrosoft.com
Get access Rights & Permissions [Opens in a new window]

Abstract

The financial system can be shown to be a complex adaptive system consisting primarily of a federation of systems and systems of systems. There are significant similarities between the characteristics of natural systems and financial systems suggesting that the type of analysis employed in understanding natural systems could have application in financial system analysis. Cladistics analysis has been used extensively for analysis of biological systems and has accordingly been used in the social sciences for some years but a rigorous justification for adopting the analysis has not been undertaken. This paper discusses the appropriateness of applying cladistics analysis to financial systems, and then considers the appropriate methodology to be adopted for analysis of different financial events.

Keywords

Cladistics analysisComplex adaptive systemsFinancial risks

JEL classification

G22: Insurance • Insurance Companies • Actuarial Studies
Type
Paper
Information
Annals of Actuarial Science , Volume 13 , Issue 2 , September 2019 , pp. 360 - 377
Copyright
© Institute and Faculty of Actuaries 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ali, P., Anderson, M., O’Brien, L. & Ramsay, I. (2016). The incidence and causes of personal bankruptcy in Australia. Finsia Journal of Applied Finance, (4), 27–35.Google Scholar
Allan, N., Cantle, N., Godfrey, P. & Yin, Y. (2012). A review of the use of complex systems applied to risk appetite and emerging risks in ERM practice. British Actuarial Journal, 18(1), 1–72. https://doi.org/10.1017/S135732171200030X Google Scholar
Allan, N., Yin, Y. & Cantle, N. (2010). Risk DNA: an evolutionary approach to identifying emerging and adapting enterprise risk using phylogenetic analysis. Paper presented at the 2010 Enterprise Risk Management Symposium, Chicago. https://www.soa.org/library/monographs/other-monographs/2010/april/mono-2010-m-as10-1-allan.pdf [accessed 10-Jul-2018].Google Scholar
Archie, J.W. (1985). Methods for coding variable morphological features for numerical taxonomic analysis. Systematic Biology, 34(3), 326–345. https://doi.org/10.1093/sysbio/34.3.326 Google Scholar
Arthur, W.B., Durlauf, S. & Lane, D.A. (1997). Introduction: process and emergence in the economy. In W.B. Arthur, S. Durlauf & D.A. Lane (Eds.), The Economy as an Evolving Complex System II (Vol. XXVII, pp. 1–14). Addison-Wesley, MA.Google Scholar
Baum, D.A. & Smith, S.D. (2013). Tree Thinking: An Introduction to Phylogenetic Biology. Roberts and Company Publishers, Greenwood Village.Google Scholar
Boucher, J. & Denuit, M. (2006). Fixed versus random effects in Poisson regression models for claim counts: a case study with motor insurance. Astin Bulletin, 36(1), 285–301. https://doi.org/10.2143/AST.36.1.2014153 Google Scholar
Brown, J.W., Parins-Fukuchi, C., Stull, G.W., Vargas, O.M. & Smith, S.A. (2017). Bayesian and likelihood phylogenetic reconstructions of morphological traits are not discordant when taking uncertainty into consideration: a comment on Puttick et al. Proceedings of the Royal Society B: Biological Sciences, 284(1864), 20170986. https://doi.org/10.1098/rspb.2017.0986 Google Scholar
Camin, J.H. & Sokal, R.R. (1965). A method for deducing branching sequences in phylogeny. Evolution, 19(2), 311–326. https://doi.org/10.2307/2406441 Google Scholar
Carpenter, J.M. (1992). Random cladistics. Cladistics, 8(2), 147–153. https://doi.org/10.1111/j.1096-0031.1992.tb00059.x Google Scholar
Carpenter, J.M., Goloboff, P.A. & Farris, J.S. (1998). PTP is meaningless, T-PTP is contradictory: a reply to Trueman. Cladistics, 14(1), 105–116. https://doi.org/10.1111/j.1096-0031.1998.tb00206.x Google Scholar
Chernobai, A., Jarion, P. & Yu, F. (2011). The determinants of operational risk in U.S. financial institutions. Journal of Financial and Quantitative Analysis, 46(6), 1683–1725. https://doi.org/10.1017/S0022109011000500 Google Scholar
Cilliers, P. (1998). Complexity and postmodernism : understanding complex systems. Routledge, London, New York.Google Scholar
Colless, D.H. (1980). Congruence between morphometric and allozyme data for Menidia species: a reappraisal. Systematic Zoology, 29(3), 288–299. https://doi.org/10.2307/2412663 Google Scholar
Daníelsson, J. (2008). Blame the models. Journal of Financial Stability, 4(4), 321–328. https://doi.org/10.1016/j.jfs.2008.09.003 Google Scholar
Danielsson, J., James, K.R., Valenzuela, M. & Zer, I. (2016). Model risk of risk models. Journal of Financial Stability, 23, 79–91. https://doi.org/10.1016/j.jfs.2016.02.002 Google Scholar
Dollo, L. (1893). Les lois de l’évolution. Bulletin de la Société Belge de Géologie, VII, 164–166.Google Scholar
Evans, J., Allan, N. & Cantle, N. (2017). A new insight into the world economic forum global risks. Economic Papers: A journal of applied economics and policy, 36(2), 185–197. https://doi.org/10.1111/1759-3441.12172 Google Scholar
Evans, J. & Li, Y. (2018a). A review of global banking regulation under an assumption of complexity. Annals of Actuarial Science. http://doi.org/10.1017/S1748499518000301 Google Scholar
Evans, J. & Li, Y. (2018b). A systems analysis of motor vehicle claims. https://doi.org/10.2139/ssrn.3252224 Google Scholar
Faith, D.P. & Cranston, P.S. (1991). Could a cladogram this short have arisen by chance alone?: on permutation tests for cladistic structure. Cladistics, 7(1), 1–28. https://doi.org/10.1111/j.1096-0031.1991.tb00020.x Google Scholar
Farris, J.S. (1973). A probability model for inferring evolutionary trees. Systematic Zoology, 22(3), 250–256.Google Scholar
Farris, J.S. (1983). The logical basis of phylogenetic analysis. In N.I. Platnick & V.A. Funk (Eds.), Advances in Cladistics. Volume 2, Proceedings of the Second Meeting of the Willi Hennig Society (pp. 7–36). Columbia University Press, New York.Google Scholar
Farris, J.S., Kluge, A.G. & Carpenter, J.M. (2001). Popper and likelihood versus “Popper*”. Systematic Biology, 50(3), 438–444.Google Scholar
Felsenstein, J. (1973). Maximum likelihood and minimum-steps methods for estimating evolutionary trees from data on discrete characters. Systematic Zoology, 22(3), 240–249.Google Scholar
Felsenstein, J. (1978). Cases in which parsimony or compatibility methods will be positively misleading. Systematic Zoology, 27(4), 401–410. https://doi.org/10.2307/2412923 Google Scholar
Felsenstein, J. (1981). Evolutionary trees from DNA sequences: A maximum likelihood approach. Journal of Molecular Evolution, 17(6), 368–376. https://doi.org/10.1007/BF01734359 Google Scholar
Felsenstein, J. (1988). Phylogenies from molecular sequences: inference and reliability. Annual Review of Genetics, 22, 521–565. https://doi.org/10.1146/annurev.ge.22.120188.002513 Google Scholar
Fitch, W.M. (1971). Toward defining the course of evolution: minimum change for a specific tree topology. Systematic Biology, 20(4), 406–416.Google Scholar
Ganegoda, A. & Evans, J. (2014). A framework to manage the measurable, immeasurable and the unidentifiable financial risk. Australian Journal of Management, 39(1), 5–34. https://doi.org/10.1177/0312896212461033 Google Scholar
Goldman, N. (1990). Maximum likelihood inference of phylogenetic trees, with special reference to a Poisson process model of DNA substitution and to parsimony analyses. Systematic Biology, 39(4), 345–361. https://doi.org/10.2307/2992355 Google Scholar
Goloboff, P.A., Galvis, A.T. & Arias, J.S. (2018). Parsimony and model-based phylogenetic methods for morphological data: comments on O’Reilly et al. Palaeontology, 61(4), 625–630.Google Scholar
Goloboff, P.A., Torres, A. & Arias, J.S. (2017). Weighted parsimony outperforms other methods of phylogenetic inference under models appropriate for morphology. Cladistics, 34, 407–437. https://doi.org/10.1111/cla.12205 Google Scholar
Griffiths, A.J.F., Wessler, S.R., Lewontin, R.C., Gelbart, W.M., Suzuki, D.T. & Miller, J.H. (2005). Mutation, repair, and recombination. In An Introduction to Genetic Analysis (8th ed., pp. 451–480). W.H. Freeman and Company, New York.Google Scholar
Helfenbein, K.G. & DeSalle, R. (2005). Falsifications and corroborations: Karl Popper’s influence on systematics. Molecular biology and evolution, 35(1), 271–280.Google Scholar
Hillis, D.M. (1995). Approaches for assessing phylogenetic accuracy. Systematic Biology, 44(1), 3–16.Google Scholar
Holland, J.H. (1995). Hidden Order: How Adaptation Builds Complexity. Addison-Wesley, New York.Google Scholar
Holland, J.H. (2014). Complexity: A Very Short Introduction. Oxford University Press, New York.Google Scholar
Kidd, K.K. & Sgaramella-Zonta, L.A. (1971). Phylogenetic analysis: concepts and methods. The American Journal of Human Genetics, 23(3), 235–252.Google Scholar
Kitching, I.J., Forey, P.L., Humphries, C. & Williams, D.M. (1998). Cladistics: The Theory and Practice of Parsimony Analysis (Vol. 11). Oxford University Press, New York.Google Scholar
Kluge, A.G. (2006). What is the rationale for ‘Ockham’s razor’ (a.k.a. parsimony) in phylogenetic inference? In V.A. Albert (Ed.), Parsimony, Phylogeny, and Genomics (pp. 15–42). Oxford University Press, Oxford, New York.Google Scholar
Kluge, A.G. & Farris, J.S. (1969). Quantitative phyletics and the evolution of Anurans. Systematic Zoology, 18(1), 1–32.Google Scholar
Lake, J.A. (1987). A rate-independent technique for analysis of nucleic acid sequences: evolutionary parsimony. Molecular biology and evolution, 4(2), 167–191.Google Scholar
Lande, R. (1976). Natural selection and random genetic drift in phenotypic evolution. Evolution, 30(2), 314–334. https://doi.org/10.1111/j.1558-5646.1976.tb00911.x Google Scholar
Li, Y., Allan, N. & Evans, J. (2017a). An analysis of operational risk events in US and European Banks 2008–2014. Annals of Actuarial Science, 11(2), 315–342. https://doi.org/10.1017/S1748499517000021 Google Scholar
Li, Y., Allan, N. & Evans, J. (2017b). A nonlinear analysis of operational risk events in Australian banks. Journal of Operational Risk, 12(1), 1–22. https://doi.org/10.21314/JOP.2017.185 Google Scholar
Li, Y., Shi, L., Allan, N. & Evans, J. (2018). An analysis of power law distributions and tipping points during the global financial crisis. Annals of Actuarial Science, 1–12. https://doi.org/10.1017/S1748499518000088 Google Scholar
Matthews, L.J., Edmonds, J., Wildman, W.J. & Nunn, C.L. (2013). Cultural inheritance or cultural diffusion of religious violence? A quantitative case study of the Radical Reformation. Religion, Brain & Behavior, 3(1), 3–15. https://doi.org/10.1080/2153599X.2012.707388 Google Scholar
McCarthy, I.P., Tsinopoulos, C., Allen, P. & Rose-Anderssen, C. (2006). New product development as a complex adaptive system of decisions. The Journal of Product Innovation Management, 23(5), 437–456. https://doi.org/10.1111/j.1540-5885.2006.00215.x Google Scholar
Mickevich, M. & Johnson, M.S. (1976). Congruence between morphological and allozyme data in evolutionary inference and character evolution. Systematic Zoology, 25(3), 260–270.Google Scholar
Mitchell, M. (2009). Complexity: A Guided Tour. Oxford University Press, New York.Google Scholar
Mitleton-Kelly, E., (Ed.) (2003). Complex Systems and Evolutionary Perspective on Organisations: The Application of Complexity Theory to Organisations. Elsevier Science, Oxford.Google Scholar
O’Reilly, J.E., Puttick, M.N., Parry, L., Tanner, A.R., Tarver, J.E., Fleming, J. & Donoghue, P.C.J. (2016). Bayesian methods outperform parsimony but at the expense of precision in the estimation of phylogeny from discrete morphological data. Biology Letters, 12(4), 20160081. https://doi.org/10.1098/rsbl.2016.0081 Google Scholar
O’Reilly, J.E., Puttick, M.N., Pisani, D. & Donoghue, P.C.J. (2017). Probabilistic methods surpass parsimony when assessing clade support in phylogenetic analyses of discrete morphological data. Palaeontology, 61(1), 105–118. https://doi.org/10.1111/pala.12330 Google Scholar
Pleijel, F. (1995). On character coding for phylogeny reconstruction. Cladistics, 11(3), 309–315. https://doi.org/10.1016/0748-3007(95)90018-7 Google Scholar
Popper, K. (1959). The Logic of Scientific Discovery. Haper and Row, New York.Google Scholar
Popper, K. (2002). The Logic of Scientific Discovery. Routledge, London and Vew York.Google Scholar
Popper, K.R. (1983). Realism and the Aim of Science. Hutchinson, London.Google Scholar
Puttick, M.N., O’Reilly, J.E., Tanner, A.R., Fleming, J.F., Clark, J., Holloway, L. & Donoghue, P.C.J. (2017). Uncertain-tree: discriminating among competing approaches to the phylogenetic analysis of phenotype data. Proceedings of the Royal Society B: Biological Sciences, 284(1846). https://doi.org/10.1098/rspb.2016.2290 Google Scholar
Queiroz, K.D. (1988). Systematics and the Darwinian Revolution. Philosophy of Science, 55(2), 238–259.Google Scholar
Queiroz, K.D. (2004). The measurement of test severity, significance tests for resolution, and a unified philosophy of phylogenetic inference. Zoologica Scripta, 33(5), 463–473. https://doi.org/10.1111/j.0300-3256.2004.00160.x Google Scholar
Queiroz, K.D. (2014). Popperian corroboration and phylogenetics. Systematic Biology, 63(6), 1018–1022. https://doi.org/10.1093/sysbio/syu064 Google Scholar
Queiroz, K.D. & Poe, S. (2001). Philosophy and phylogenetic inference: a comparison of likelihood and parsimony methods in the context of Karl Popper’s writing on corroboration. Systematic Biology, 50(3), 305–321.Google Scholar
Rannala, B. & Yang, Z. (1996). Probability distribution of molecular evolutionary trees: A new method of phylogenetic inference. Journal of Molecular Evolution, 43(3), 304–311. https://doi.org/10.1007/BF02338839 Google Scholar
Rastogi, S.C., Mendiratta, N. & Rastogi, P. (2008). Bioinformatics Methods and Applications: Genomics, Proteomics and Drug Discovery. PHI Learning Private Limited, New Delhi.Google Scholar
Rzhetsky, A. & Nei, M. (1993). Theoretical foundation of the minimum-evolution method of phylogenetic inference. Molecular Biology and Evolution, 10(5), 1073–1095. https://doi.org/10.1093/oxfordjournals.molbev.a040056 Google Scholar
Saitou, N. & Nei, M. (1987). The neighbor-joining method: a new method for reconstructing phylogenetic trees. Molecular Biology and Evolution, 4(4), 406–425.Google Scholar
Schrago, C.G., Aguiar, B.O. & Mello, B. (2018). Comparative evaluation of maximum parsimony and Bayesian phylogenetic reconstruction using empirical morphological data. Journal of Evolutionary Biology, 31(10), 1477–1484. https://doi.org/10.1111/jeb.13344 Google Scholar
Schweitzer, F., Fagiolo, G., Sornette, D., Vega-Redondo, F., Vespignani, A. & White, D.R. (2009). Economic networks: the new challenges. Science, 325, 422–425.Google Scholar
Shi, L., Allan, N., Evans, J. & Yun, Y. (2018a). Significance of controllable and uncontrollable drivers in credit defaults. Economic Papers: A Journal of Applied Economics and Policy, 37(1), 30–41. https://doi.org/10.1111/1759-3441.12200 Google Scholar
Shi, L., Evans, J. & Li, Y. (2018b). A systems analysis of drivers of individual bankruptcies. Economic Papers: A Journal of Applied Economics and Policy, published online August 2018. https://doi.org/10.1111/1759-3441.12229 Google Scholar
Siddall, M.E. & Kluge, A.G. (1997). Probabilism and phylogenetic inference. Cladistics, 13(4), 313–336. https://doi.org/10.1111/j.1096-0031.1997.tb00322.x Google Scholar
Smith, A. (1776). An Inquiry into the Nature and Causes of the Wealth of Nations. The Electric Book Co, London.Google Scholar
Sober, E. (1975). Simplicity. Clarendon Press, Oxford.Google Scholar
Sokal, R.R. & Michener, C.D. (1958). A statistical method for evaluating systematic relationships. The University of Kansas Science Bulletin, 38, 1409–1438.Google Scholar
Song, F. & Thakor, A.V. (2010). Financial system architecture and the co-evolution of banks and capital markets. The Economic Journal, 120(547), 1021–1055.Google Scholar
Studier, J.A. & Keppler, K.J. (1988). A note on the neighbor-joining algorithm of Saitou and Nei. Molecular Biology and Evolution, 5(6), 729–731. https://doi.org/10.1093/oxfordjournals.molbev.a040527 Google Scholar
Thiele, K. (1993). The Holy Grail of the perfect character: the cladistic treatment of morphometric data. Cladistics, 9(3), 275–304. https://doi.org/10.1111/j.1096-0031.1993.tb00226.x Google Scholar
ul-Haq, R. (2005). Alliances and Co-Evolution: Insights from the Banking Sector. Palgrave Macmillan, New York.Google Scholar
Wiens, J.J. (2001). Character analysis in morphological phylogenetics: problems and solutions. Systematic Biology, 50(5), 689–699.Google Scholar
Wiens, J.J., (Ed.) (2000). Phylogenetic Analysis of Morphological Data. Smithsonian Institution Press, Washington and London.Google Scholar
Wiley, E.O. & Lieberman, B.S. (2011). Phylogenetics: Theory and Practice of Phylogenetic Systematics. John Wiley & Sons, New Jersey.Google Scholar
Willson, S.J. (1999). A higher order parsimony method to reduce long-branch attraction. Molecular Biology and Evolution, 16(5), 694–705. https://doi.org/10.1093/oxfordjournals.molbev.a026152 Google Scholar
Wright, A.M. & Hillis, D.M. (2014). Bayesian analysis using a simple likelihood model outperforms parsimony for estimation of phylogeny from discrete morphological sata. PLoS ONE, 9(10), e109210. https://doi.org/10.1371/journal.pone.0109210 Google Scholar
Yang, Z. & Rannala, B. (1997). Bayesian phylogenetic inference using DNA sequences: a Markov Chain Monte Carlo Method. Molecular Biology and Evolution, 14(7), 717–724. https://doi.org/10.1093/oxfordjournals.molbev.a025811 Google Scholar