Abstract
We consider a market graph model of the Russian stock market. To study the peculiarity of the Russian market we construct the market graphs for different time periods from 2007 to 2011. As characteristics of constructed market graphs we use the distribution of correlations, size and structure of maximum cliques, and relationship between return and volume of stocks. Our main finding is that for the Russian market there is a strong connection between the volume of stocks and the structure of maximum cliques for all periods of observations. Namely, the most attractive Russian stocks have a strongest correlation between their returns. At the same time as far as we are aware this phenomenon is not related to the well developed USA stock market.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Boginski V, Butenko S, Pardalos PM (2003) On structural properties of the market graph. In: Nagurney A (ed) Innovations in financial and economic networks. Edward Elgar Publishing, UK, pp 29–45.
Boginski V, Butenko S, Pardalos PM (2005) Statistical analysis of financial networks. Comput Stat Data Anal 48:431–443
Boginski V, Butenko S, Pardalos PM (2006) Mining market data: a network approach. Comput Oper Res 33:3171–3184
Bron C, Kerbosh J (1973) Algorithm 457—finding all cliques of an undirected graph. Commun of ACM 16:575–577
Carragan R, Pardalos PM (1990) An exact algorithm for the maximum clique problem. Oper Res Lett 9:375–382
Huang W-Q, Zhuang X-T, Shuang Y (2009) A network analysis of the Chinese stock market. Phys A 388:2956–2964
Jallo D, Budai D, Boginski V, Goldengorin B, Pardalos PM (2013) Network-based representation of stock market dynamics: an application to American and Swedish stock markets. In: Goldengorin B, Kalyagin V, Pardalos PM (eds) Models, algorithms, and technologies for network analysis Springer proceedings in mathematics and statistics. Springer, Berlin, pp 93–106
Mantegna RN, Stanley HE (2000) An introduction to econophysics: corrleations and complexity in finance. Cambridge Universiy Press, Cambridge
Namaki A, Shirazi A, Raei R, Jafari J (2011) Network analysis of a financial market based on genuine correlation and threshold method. Phys A 390:3835–3841
Salter-Townshend M, White A, Gollini I, Murphy T (2012) Review of statistical network analysis: models, algorithms, and software. Stat Anal Data Min 5(4):243–264
Schweitzer F, Fagiolo G, Sornette D, Vega-Redondo F, Vespignani A, White DR (2009) Economic networks: the new challenges. Science 325:422–425
Acknowledgments
We would like to thank the anonymous referees for their useful comments for improving the quality and the presentation of the paper. The authors are partially supported by LATNA Laboratory, NRU HSE, RF, government grant ag. 11.G34.31.0057.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Vizgunov, A., Goldengorin, B., Kalyagin, V. et al. Network approach for the Russian stock market. Comput Manag Sci 11, 45–55 (2014). https://doi.org/10.1007/s10287-013-0165-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10287-013-0165-7