Ranking Influential and Influenced Shares Based on the Transfer Entropy Network
Resumo
Influence is a concept found in nature and society and is related to the interdependency among a set of objects. In the context of a stock market, the variation in price of shares can influence the variation in price of other shares, leading to influential and influenced shares. In this work we leverage the notion of transfer entropy to build a network of shares and pairwise directed influence that is used to rank the most influential and influenced shares. Classical network centrality metrics such as PageRank and HITS are leveraged to rank the nodes. We apply our methodology to the shares in the greater stock market in Brazil, we rank nodes to find source and destination of influence in that market, while also comparing the different rankings and their correlation with traded volume.
Referências
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Atsalakis, G. S. and Valavanis, K. P. (2009). Surveying stock market forecasting techniques - soft computing methods. Expert Systems with Applications, 36(3):5932 – 5941.
Baker, W. E. (1990). Market networks and corporate behavior. American journal of sociology, 96(3):589–625.
Boginski, V., Butenko, S., and Pardalos, P. M. (2005). Statistical analysis of financial networks. Computational Statistics & Data Analysis, 48(2):431 – 443.
Bossomaier, T., Barnett, L., Harré, M., and Lizier, J. T. (2016). An introduction to transfer entropy: information flow in complex systems.
Brin, S. and Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30(1):107 – 117.
Chakrabarti, S., Dom, B., Raghavan, P., Rajagopalan, S., Gibson, D., and Kleinberg, J. (1998). Automatic resource compilation by analyzing hyperlink structure and associated text. 30(1):65–74.
He, J. and Shang, P. (2017). Comparison of transfer entropy methods for financial time series. Physica A: Statistical Mechanics and its Applications, 482:772–785.
Kauê Dal’Maso Peron, T., da Fontoura Costa, L., and Rodrigues, F. A. (2012). The structure and resilience of financial market networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 22(1):013117.
Kleinberg, J. M. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM), 46(5):604–632.
Korbel, J., Jiang, X., and Zheng, B. (2017). Transfer entropy between communities in complex networks. ArXiv e-prints.
Kwon, O. and Yang, J.-S. (2008). Information flow between stock indices. Europhysics Letters, 82(6):68003–p1–68003–p4.
Leahy, S. P., Levy Carciente, S., Stanley, H. E., and Kenett, D. Y. (2014). Structure and dynamics of the brazilian stock market: A correlation analysis.
Lee, G. S. and Djauhari, M. A. (2012). An overall centrality measure: The case of us stock market. International Journal of Electrical & Computer Sciences, 12(6).
Li, L., Shang, Y., and Zhang, W. (2002). Improvement of hits-based algorithms on web documents. In Proc. Int. Conf. on World Wide Web, pages 527–535.
Lyócsa, Š., Vỳrost, T., and Baumöhl, E. (2012). Stock market networks: the dynamic conditional correlation approach. Physica A: Statistical Mechanics and its Applications, 391(16):4147–4158.
Marschinski, R. and Kantz, H. (2002). Analysing the information flow between financial times series. The European Physical Journal B, 30:275–281.
Namaki, A., Shirazi, A., Raei, R., and Jafari, G. (2011). Network analysis of a financial market based on genuine correlation and threshold method. Physica A: Statistical Mechanics and its Applications, 390(21):3835–3841.
Page, L., Brin, S., Motwani, R., and Winograd, T. (1999). The pagerank citation ranking: bringing order to the web. Technical Report 1999-66.
Petelin, D., Šindelàf, J., Přikryl, J., and Kocijan, J. (2011). Financial modeling using gaussian process models. Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems, 2:672–677.
Schreiber, T. (2000). Measuring information transfer. Physical Review Letters, 85:461–464.
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27:379–423.
Shirokikh, O., Pastukhov, G., Boginski, V., and Butenko, S. (2013). Computational study of the us stock market evolution: a rank correlation-based network model. Computational Management Science, 10(2-3):81–103.
Tabak, B. M., Serra, T. R., and Cajueiro, D. O. (2010). Topological properties of stock market networks: The case of brazil. Physica A: Statistical Mechanics and its Applications, 389(16):3240–3249.
Xing, W. and Ghorbani, A. (2004). Weighted pagerank algorithm. In Communication Networks and Services Research, 2004. Proceedings. Second Annual Conference on, pages 305–314. IEEE.
Yang, P., Shang, P., and Lin, A. (2017). Financial time series analysis based on effective phase transfer entropy. Physica A: Statistical Mechanics and its Applications, 468:398–408.
Zhang, X., Yu, H., Zhang, C., and Liu, X. (2007). An improved weighted hits algorithm based on similarity and popularity. In Second International Multi-Symposiums on Computer and Computational Sciences (IMSCCS 2007), pages 477–480.
Atsalakis, G. S. and Valavanis, K. P. (2009). Surveying stock market forecasting techniques - soft computing methods. Expert Systems with Applications, 36(3):5932 – 5941.
Baker, W. E. (1990). Market networks and corporate behavior. American journal of sociology, 96(3):589–625.
Boginski, V., Butenko, S., and Pardalos, P. M. (2005). Statistical analysis of financial networks. Computational Statistics & Data Analysis, 48(2):431 – 443.
Bossomaier, T., Barnett, L., Harré, M., and Lizier, J. T. (2016). An introduction to transfer entropy: information flow in complex systems.
Brin, S. and Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30(1):107 – 117.
Chakrabarti, S., Dom, B., Raghavan, P., Rajagopalan, S., Gibson, D., and Kleinberg, J. (1998). Automatic resource compilation by analyzing hyperlink structure and associated text. 30(1):65–74.
He, J. and Shang, P. (2017). Comparison of transfer entropy methods for financial time series. Physica A: Statistical Mechanics and its Applications, 482:772–785.
Kauê Dal’Maso Peron, T., da Fontoura Costa, L., and Rodrigues, F. A. (2012). The structure and resilience of financial market networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 22(1):013117.
Kleinberg, J. M. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM), 46(5):604–632.
Korbel, J., Jiang, X., and Zheng, B. (2017). Transfer entropy between communities in complex networks. ArXiv e-prints.
Kwon, O. and Yang, J.-S. (2008). Information flow between stock indices. Europhysics Letters, 82(6):68003–p1–68003–p4.
Leahy, S. P., Levy Carciente, S., Stanley, H. E., and Kenett, D. Y. (2014). Structure and dynamics of the brazilian stock market: A correlation analysis.
Lee, G. S. and Djauhari, M. A. (2012). An overall centrality measure: The case of us stock market. International Journal of Electrical & Computer Sciences, 12(6).
Li, L., Shang, Y., and Zhang, W. (2002). Improvement of hits-based algorithms on web documents. In Proc. Int. Conf. on World Wide Web, pages 527–535.
Lyócsa, Š., Vỳrost, T., and Baumöhl, E. (2012). Stock market networks: the dynamic conditional correlation approach. Physica A: Statistical Mechanics and its Applications, 391(16):4147–4158.
Marschinski, R. and Kantz, H. (2002). Analysing the information flow between financial times series. The European Physical Journal B, 30:275–281.
Namaki, A., Shirazi, A., Raei, R., and Jafari, G. (2011). Network analysis of a financial market based on genuine correlation and threshold method. Physica A: Statistical Mechanics and its Applications, 390(21):3835–3841.
Page, L., Brin, S., Motwani, R., and Winograd, T. (1999). The pagerank citation ranking: bringing order to the web. Technical Report 1999-66.
Petelin, D., Šindelàf, J., Přikryl, J., and Kocijan, J. (2011). Financial modeling using gaussian process models. Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems, 2:672–677.
Schreiber, T. (2000). Measuring information transfer. Physical Review Letters, 85:461–464.
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27:379–423.
Shirokikh, O., Pastukhov, G., Boginski, V., and Butenko, S. (2013). Computational study of the us stock market evolution: a rank correlation-based network model. Computational Management Science, 10(2-3):81–103.
Tabak, B. M., Serra, T. R., and Cajueiro, D. O. (2010). Topological properties of stock market networks: The case of brazil. Physica A: Statistical Mechanics and its Applications, 389(16):3240–3249.
Xing, W. and Ghorbani, A. (2004). Weighted pagerank algorithm. In Communication Networks and Services Research, 2004. Proceedings. Second Annual Conference on, pages 305–314. IEEE.
Yang, P., Shang, P., and Lin, A. (2017). Financial time series analysis based on effective phase transfer entropy. Physica A: Statistical Mechanics and its Applications, 468:398–408.
Zhang, X., Yu, H., Zhang, C., and Liu, X. (2007). An improved weighted hits algorithm based on similarity and popularity. In Second International Multi-Symposiums on Computer and Computational Sciences (IMSCCS 2007), pages 477–480.
Publicado
22/07/2018
Como Citar
NEVES NETO, José de Paula; FIGUEIREDO, Daniel Ratton.
Ranking Influential and Influenced Shares Based on the Transfer Entropy Network. In: WORKSHOP EM DESEMPENHO DE SISTEMAS COMPUTACIONAIS E DE COMUNICAÇÃO (WPERFORMANCE), 17. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 151-164.
ISSN 2595-6167.
DOI: https://doi.org/10.5753/wperformance.2018.3324.
