Structure of Investor Networks and Financial Crises

In this paper, we ask whether the structure of investor networks, estimated using shareholder registration data, is abnormal during a financial crises. We answer this question by analyzing the structure of investor networks through several most prominent global network features. The networks are estimated from data on marketplace transactions of all publicly traded securities executed in the Helsinki Stock Exchange by Finnish stock shareholders between 1995 and 2016. We observe that most of the feature distributions were abnormal during the 2008–2009 financial crisis, with statistical significance. This paper provides evidence that the financial crisis was associated with a structural change in investors’ trade time synchronization. This indicates that the way how investors use their private information channels changes depending on the market conditions.

[1]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[2]  J. Kanniainen,et al.  Identification of Information Networks in Stock Markets , 2020, SSRN Electronic Journal.

[3]  Chenchuramaiah T. Bathala The Investment Behavior and Performance of Various Investor Types: A Study of Finland's Unique Data Set , 2000 .

[4]  Giulio Cimini,et al.  The statistical physics of real-world networks , 2018, Nature Reviews Physics.

[5]  Richard H. Thaler,et al.  Do Security Analysts Overreact , 1990 .

[6]  Yongtang Shi,et al.  Fifty years of graph matching, network alignment and network comparison , 2016, Inf. Sci..

[7]  Mark Grinblatt,et al.  The investment behavior and performance of various investor types: a study of Finland's unique data set , 2000 .

[8]  D. Garlaschelli,et al.  Early-warning signals of topological collapse in interbank networks , 2013, Scientific Reports.

[9]  Matthias Dehmer,et al.  Computational Analysis of the structural properties of Economic and Financial Networks , 2017, 1710.04455.

[10]  Chi Xie,et al.  Tail dependence structure of the foreign exchange market: A network view , 2016, Expert Syst. Appl..

[11]  Juho Kanniainen,et al.  Multilayer Aggregation with Statistical Validation: Application to Investor Networks , 2017, Scientific Reports.

[12]  Stephen E. Wilcox Investor Psychology and Security Market Under- and Overreactions , 1999 .

[13]  R. Mantegna Hierarchical structure in financial markets , 1998, cond-mat/9802256.

[14]  G. Caldarelli,et al.  Structural changes in the interbank market across the financial crisis from multiple core-periphery analysis , 2018, 1802.05139.

[15]  Giulio Cimini,et al.  Statistically validated network of portfolio overlaps and systemic risk , 2016, Scientific Reports.

[17]  Christian Decker,et al.  Lightning network: a second path towards centralisation of the Bitcoin economy , 2020, New Journal of Physics.

[18]  Stephen Michael Taylor,et al.  Graph theoretical representations of equity indices and their centrality measures , 2019, Quantitative Finance.

[19]  Changju Lee,et al.  Fractal structure in the S&P500: A correlation-based threshold network approach , 2020 .

[20]  Yongtang Shi,et al.  Properties of graph distance measures by means of discrete inequalities , 2018 .

[21]  F. Lillo,et al.  Clusters of investors around initial public offering , 2019, Palgrave Communications.

[22]  J. Saramäki,et al.  Trading Signatures: Investor Attention Allocation in Stock Markets , 2020 .

[23]  Yongtang Shi,et al.  Interrelations of Graph Distance Measures Based on Topological Indices , 2014, PloS one.

[24]  E. Ben-Jacob,et al.  Dominating Clasp of the Financial Sector Revealed by Partial Correlation Analysis of the Stock Market , 2010, PloS one.

[25]  M. Tumminello,et al.  Statistically Validated Networks in Bipartite Complex Systems , 2010, PloS one.

[26]  T. Perneger What's wrong with Bonferroni adjustments , 1998, BMJ.

[27]  Fabrizio Lillo,et al.  The organization of the interbank network and how ECB unconventional measures affected the e-MID overnight market , 2015, Comput. Manag. Sci..

[28]  Robert J. Shiller,et al.  Irrational exuberance: the national bestseller that will help tou survive in todays stock market , 2004 .

[29]  Mikko Kivelä,et al.  Dynamics of investor spanning trees around dot-com bubble , 2017, PloS one.

[30]  Mark R Schultz,et al.  False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies. , 2014, Journal of clinical epidemiology.

[31]  Panos M. Pardalos,et al.  Statistical analysis of financial networks , 2005, Comput. Stat. Data Anal..

[32]  Fabrizio Lillo,et al.  Identification of clusters of investors from their real trading activity in a financial market , 2011, ArXiv.

[33]  Diego Garlaschelli,et al.  Irreducible network backbones: unbiased graph filtering via maximum entropy , 2017, ArXiv.

[34]  Shlomo Havlin,et al.  Partial correlation analysis: applications for financial markets , 2014 .

[35]  Shouyang Wang,et al.  Granger Causality in Risk and Detection of Extreme Risk Spillover Between Financial Markets , 2009 .

[36]  Damien Challet,et al.  Statistically validated lead-lag Networks and inventory Prediction in the Foreign Exchange Market , 2016, Adv. Complex Syst..

[37]  Fabio Saracco,et al.  Detecting early signs of the 2007–2008 crisis in the world trade , 2015, Scientific Reports.

[38]  M Tumminello,et al.  A tool for filtering information in complex systems. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[39]  Rosario N. Mantegna,et al.  Long-term ecology of investors in a financial market , 2018, Palgrave Communications.

[40]  Johan Walden,et al.  Investor Networks in the Stock Market , 2011 .

[41]  Domenico Di Gangi,et al.  Assessing Systemic Risk Due to Fire Sales Spillover Through Maximum Entropy Network Reconstruction , 2015, Journal of Economic Dynamics and Control.

[42]  Andrea Gabrielli,et al.  Inferring monopartite projections of bipartite networks: an entropy-based approach , 2016 .

[43]  H. Stanley,et al.  Multilayer information spillover networks: measuring interconnectedness of financial institutions , 2020, Quantitative Finance.

[44]  R. Thaler,et al.  Chapter 18 A survey of behavioral finance , 2003 .