PL EN
RESEARCH PAPER
PageRank and Regression as a Two-Step Approach to Analysing a Network of Nasdaq Firms During a Recession: Insights from Minimum Spanning Tree Topology
 
More details
Hide details
1
Collegium of World Economy, SGH Warsaw School of Economics, Poland
 
 
Submission date: 2023-11-28
 
 
Final revision date: 2024-03-19
 
 
Acceptance date: 2024-04-18
 
 
Publication date: 2024-09-30
 
 
Corresponding author
Artur F. Tomeczek   

Collegium of World Economy, SGH Warsaw School of Economics, Poland
 
 
GNPJE 2024;319(3):56-69
 
KEYWORDS
JEL CLASSIFICATION CODES
ABSTRACT
The presence of focal firms driving entire stock markets has been proven by a series of existing studies that relied on the topological properties of minimum spanning trees. Historically, central firms have been identified primarily based on the degree centrality of nodes. This article proposes an alternative selection method, combining PageRank scores and modularity classes, which does away with the problem of ties in rankings when selecting a specific number of nodes. We use PageRank-based network analysis along with regression analysis to identify focal firms in the Nasdaq-100 index during the three most significant recent recessions in the United States. This approach validates and robustly supports our two-step method, showing that the combination of minimum spanning trees and our selection method explains over 90% of the Nasdaq-100 index’s dynamics. The analysis identified significant topological changes during the global financial crisis (with CSCO emerging as the star firm) and the COVID-19 pandemic (exhibiting strong market co-movements).
 
REFERENCES (64)
1.
Andrews D. W. K. [1991], Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation, Econometrica, 59 (3): 817–858, https://doi.org/10.2307/293822....
 
2.
Balcı M. A., Akgüller Ö., Can Güzel S. [2021], Hierarchies in communities of UK stock market from the perspective of Brexit, Journal of Applied Statistics, 48 (13–15): 2607–2625, https://doi.org/10.1080/026647....
 
3.
Bastian M., Heymann S., Jacomy M. [2009], Gephi: An open source software for exploring and manipulating networks, Proceed¬ings of the International AAAI Conference on Web and Social Media, 3 (1): 361–362, https://doi.org/10.1609/icwsm.....
 
4.
Blondel V. D., Guillaume J. L., Lambiotte R., Lefebvre E. [2008], Fast unfolding of communities in large networks, Journal of Statistical Mechanics: Theory and Experiment, 2008 (10): P10008, https://doi.org/10.1088/1742-5....
 
5.
Bonanno G., Caldarelli G., Lillo F., Mantegna R. N. [2003], Topology of correlation-based minimal spanning trees in real and model markets, Physical Review E, 68 (4): 046130, https://doi.org/10.1103/PhysRe....
 
6.
Bonanno G., Caldarelli G., Lillo F., Miccichè S., Vandewalle N., Mantegna R. N. [2004], Networks of equities in financial markets, The European Physical Journal B – Condensed Matter, 38 (2): 363–371, https://doi.org/10.1140/epjb/e....
 
7.
Bonanno G., Lillo F., Mantegna R. N. [2001], High-frequency cross-correlation in a set of stocks, Quantitative Finance, 1 (1): 96–104, https://doi.org/10.1080/713665....
 
8.
Bonanno G., Vandewalle N., Mantegna R. N. [2000], Taxonomy of stock market indices, Physical Review E, 62 (6): R7615–R7618, https://doi.org/10.1103/PhysRe....
 
9.
Borůvka O. [1926], O jistém problému minimálním, Práce Moravské Přírodovědecké Společnosti, 3: 37–58.
 
10.
Bouhlal F., Brahim Sedra M. [2022], The impact of COVID-19 on the topological properties of the Moroccan stock market network, Investment Management and Financial Innovations, 19 (2): 238–249, https://doi.org/10.21511/imfi.....
 
11.
Brandes U. [2001], A faster algorithm for betweenness centrality, The Journal of Mathematical Sociology, 25 (2): 163–177, https://doi.org/10.1080/002225....
 
12.
Brandes U., Erlebach T. (eds.) [2005], Network analysis: Methodological foundations, https://doi.org/10.1007/b10645....
 
13.
Brin S., Page L. [1998], The anatomy of a large-scale hypertextual Web search engine, Computer Networks and ISDN Systems, 30 (1–7): 107–117, https://doi.org/10.1016/S0169-....
 
14.
Claessens S., Van Horen N. [2015], The impact of the global financial crisis on banking globalization, IMF Economic Review, 63 (4): 868–918, https://doi.org/10.1057/imfer.....
 
15.
Coelho R., Gilmore C. G., Lucey B., Richmond P., Hutzler S. [2007], The evolution of interdependence in world equity mar¬kets – Evidence from minimum spanning trees, Physica A: Statistical Mechanics and Its Applications, 376: 455–466, https://doi.org/10.1016/j.phys....
 
16.
Danko J., Šoltés V. [2018], Portfolio creation using graph characteristics, Investment Management and Financial Innovations, 15 (1): 180–189, https://doi.org/10.21511/imfi.....
 
17.
Dias J. [2012], Sovereign debt crisis in the European Union: A minimum spanning tree approach, Physica A: Statistical Mechanics and Its Applications, 391 (5): 2046–2055, https://doi.org/10.1016/j.phys....
 
18.
Filed A. [2013], Discovering statistics using IBM SPSS Statistics, SAGE Publications, London.
 
19.
Gałązka M. [2011], Characteristics of the Polish Stock Market Correlations, International Review of Financial Analysis, 20 (1): 1–5, https://doi.org/10.1016/j.irfa....
 
20.
Gilmore C. G., Lucey B. M., Boscia M. W. [2010], Comovements in government bond markets: A minimum spanning tree analysis, Physica A: Statistical Mechanics and Its Applications, 389 (21): 4875–4886, https://doi.org/10.1016/j.phys....
 
21.
Gleich D. F. [2015], PageRank beyond the web, SIAM Review, 57 (3): 321–363, https://doi.org/10.1137/140976....
 
22.
Graham R. L., Hell, P. [1985], On the history of the minimum spanning tree problem, IEEE Annals of the History of Computing, 7 (1): 43–57, https://doi.org/10.1109/MAHC.1....
 
23.
Hamilton J. D. [2022], Dates of U. S. recessions as inferred by GDP-based recession indicator, https://fred.stlouisfed.org/se... (accessed on 4.12.2022).
 
24.
Huang C., Zhao X., Su R., Yang X., Yang X. [2022], Dynamic network topology and market performance: A case of the Chinese stock market, International Journal of Finance & Economics, 27 (2): 1962–1978, https://doi.org/10.1002/ijfe.2....
 
25.
Jung W. S., Chae S., Yang J. S., Moon H. T. [2006], Characteristics of the Korean stock market correlations, Physica A: Statistical Mechanics and Its Applications, 361 (1): 263–271, https://doi.org/10.1016/j.phys....
 
26.
Kanno M. [2021], Risk contagion of COVID-19 in Japanese firms: A network approach, Research in International Business and Finance, 58: 101491, https://doi.org/10.1016/j.riba....
 
27.
Kruskal J. B. [1956], On the shortest spanning subtree of a graph and the traveling salesman problem, Proceedings of the American Mathematical Society, 7 (1): 48–48, https://doi.org/10.1090/S0002-....
 
28.
Kwon O., Yang J. S. [2008], Information flow between stock indices, EPL (Europhysics Letters), 82 (6): 68003, https://doi.org/10.1209/0295-5....
 
29.
Lambiotte R., Delvenne J. C., Barahona M. [2014], Random walks, Markov processes and the multiscale modular organiza¬tion of complex networks, IEEE Transactions on Network Science and Engineering, 1 (2): 76–90, https://doi.org/10.1109/TNSE.2....
 
30.
Legenzova R., Gaigalienė A., Jurakovaitė O. [2019], Evaluation of the post-crisis EU banking network connectedness in the global context, Oeconomia Copernicana, 10 (1): 37–53, https://doi.org/10.24136/oc.20....
 
31.
Lü L., Chen D., Ren X. L., Zhang Q. M., Zhang Y. C., Zhou T. [2016], Vital nodes identification in complex networks, Physics Reports, 650: 1–63, https://doi.org/10.1016/j.phys....
 
32.
Mantegna R. N. [1999], Hierarchical structure in financial markets, The European Physical Journal B, 11 (1): 193–197, https://doi.org/10.1007/s10051....
 
33.
Mbatha V. M., Alovokpinhou S. A. [2022], The structure of the South African stock market network during COVID-19 hard lockdown, Physica A: Statistical Mechanics and Its Applications, 590: 126770, https://doi.org/10.1016/j.phys....
 
34.
Memon B. A., Yao H., Tahir R. [2020], General election effect on the network topology of Pakistan’s stock market: network-based study of a political event, Financial Innovation, 6 (1): 2, https://doi.org/10.1186/s40854....
 
35.
Minoiu C., Reyes J. A. [2013], A network analysis of global banking: 1978–2010, Journal of Financial Stability, 9 (2): 168– 184, https://doi.org/10.1016/j.jfs.....
 
36.
Mooi E., Sarstedt M. [2011], A concise guide to market research. The process, data, and methods using IBM SPSS Statistics, Springer, Berlin–Heidelberg.
 
37.
Napiórkowski T. [2022], Praktyczna analiza danych za pomocą metod ilościowych, Oficyna Wydawnicza SGH, Warszawa.
 
38.
Nasdaq [2022], Quotes for Nasdaq-100 index, https://www.nasdaq.com (accessed on 28.11.2022).
 
39.
Nie C. X., Song F. T. [2023], Stable versus fragile community structures in the correlation dynamics of Chinese industry indices, Chaos, Solitons & Fractals, 167: 113044, https://doi.org/10.1016/j.chao....
 
40.
Okafor A., Adeleye B. N., Adusei M. [2021], Corporate social responsibility and financial performance: Evidence from U. S tech firms, Journal of Cleaner Production, 292: 126078, https://doi.org/10.1016/j.jcle....
 
41.
Onnela J. P., Chakraborti A., Kaski K., Kertész J. [2003], Dynamic asset trees and Black Monday, Physica A: Statistical Mechanics and Its Applications, 324 (1–2): 247–252, https://doi.org/10.1016/S0378-....
 
42.
Onnela J. P., Chakraborti A., Kaski K., Kertész J., Kanto A. [2003], Dynamics of market correlations: Taxonomy and portfolio analysis, Physical Review E, 68 (5): 056110, https://doi.org/10.1103/PhysRe....
 
43.
Papana A., Kyrtsou C., Kugiumtzis D., Diks C. [2017], Financial networks based on Granger causality: A case study, Physica A: Statistical Mechanics and Its Applications, 482: 65–73, https://doi.org/10.1016/j.phys....
 
44.
Pereira E. J. D. A. L., Ferreira P. J. S., Silva M. F. D., Miranda J. G. V., Pereira H. B. B. [2019], Multiscale network for 20 stock markets using DCCA, Physica A: Statistical Mechanics and Its Applications, 529: 121542, https://doi.org/10.1016/j.phys....
 
45.
Pindyck R. S., Rubinfeld D. L. [1998], Econometric models and econometric forecasts, Irwin/McGraw-Hill, New York.
 
46.
Prim R. C. [1957], Shortest connection networks and some generalizations, Bell System Technical Journal, 36 (6): 1389–1401, https://doi.org/10.1002/j.1538....
 
47.
Siudak D. [2022a], A network analysis of the value migration process on the financial market. The effect of value migration net¬work structure on stock returns, Expert Systems with Applications, 191: 116129, https://doi.org/10.1016/j.eswa....
 
48.
Siudak D. [2022b], The effect of self-organizing map architecture based on the value migration network centrality measures on stock return, Evidence from the US market, PLOS ONE, 17 (11): e0276567, https://doi.org/10.1371/journa....
 
49.
Tang Y., Xiong J. J., Luo Y., Zhang Y. C. [2019], How do the global stock markets influence one another? Evidence from finance big data and Granger causality directed network, International Journal of Electronic Commerce, 23 (1): 85–109, https://doi.org/10.1080/108644....
 
50.
Tomeczek A. F. [2021], A financial network analysis of the equity linkages in Poland, Research Papers of Wrocław University of Economics, 65 (4): 129–143, https://doi.org/10.15611/pn.20....
 
51.
Tomeczek A. F. [2022], A minimum spanning tree analysis of the Polish stock market, Journal of Economics and Management, 44: 420–445, https://doi.org/10.22367/jem.2....
 
52.
Tsai H. J., Wu Y. [2022], Changes in corporate social responsibility and stock performance, Journal of Business Ethics, 178 (3): 735–755, https://doi.org/10.1007/s10551....
 
53.
Valls Martínez M. D. C., Soriano Román R., Mart´ín-Cervantes P. A. [2022], Should risk-averse investors target the portfolios of socially responsible companies?, Oeconomia Copernicana, 13 (2): 439–474, https://doi.org/10.24136/oc.20....
 
54.
Výrost T., Lyócsa Š., Baumöhl E. [2015], Granger causality stock market networks: Temporal proximity and preferential attachment, Physica A: Statistical Mechanics and Its Applications, 427: 262–276, https://doi.org/10.1016/j.phys....
 
55.
Wang G.‑J., Si H.‑B., Chen Y.‑Y., Xie C., Chevallier J. [2021], Time domain and frequency domain Granger causality networks: Application to China’s financial institutions, Finance Research Letters, 39: 101662, https://doi.org/10.1016/j.frl.....
 
56.
Wang G.‑J., Xie C. [2016], Tail dependence structure of the foreign exchange market: A network view, Expert Systems with Applications, 46: 164–179, https://doi.org/10.1016/j.eswa....
 
57.
Wang G.‑J., Xie C., Han F., Sun B. [2012], Similarity measure and topology evolution of foreign exchange markets using dynamic time warping method: Evidence from minimal spanning tree, Physica A: Statistical Mechanics and Its Applications, 391 (16): 4136–4146, https://doi.org/10.1016/j.phys....
 
58.
Wang G.‑J., Xie C., Stanley H. E. [2018], Correlation structure and evolution of world stock markets: Evidence from Pearson and partial correlation-based networks, Computational Economics, 51 (3): 607–635, https://doi.org/10.1007/s10614....
 
59.
Wang G.‑J., Yi S., Xie C., Stanley H. E. [2021], Multilayer information spillover networks: Measuring interconnectedness of financial institutions, Quantitative Finance, 21 (7): 1163–1185, https://doi.org/10.1080/146976....
 
60.
Yahoo Finance [2022], Yahoo Finance database, https://finance.yahoo.com (accessed on 28.11.2022).
 
61.
Yun T.‑S., Jeong D., Park S. [2019], “Too central to fail” systemic risk measure using PageRank algorithm, Journal of Economic Behavior & Organization, 162: 251–272, https://doi.org/10.1016/j.jebo....
 
62.
Zeileis A. [2004], Econometric Computing with HC and HAC Covariance Matrix Estimators, Journal of Statistical Software, 11 (10): 1–17, https://doi.org/10.18637/jss.v....
 
63.
Zhao B., Yang W., Wen J., Zhang W. [2022], The Financial Market in China under the COVID-19, Emerging Markets Finance and Trade, 58 (13): 3726–3738, https://doi.org/10.1080/154049....
 
64.
Wooldridge J. M. [2009], Introduction to econometrics. A modern approach, CENGAGE Learning, Mason, South-Western.
 
eISSN:2300-5238
Journals System - logo
Scroll to top