Mapping of stock exchangescontagion from a new perspective

  1. Pablo García Estévez 1
  2. Salvador Roji Ferrari 2
  3. Teresa Corzo Santamaría 3
  1. 1 CUNEF
  2. 2 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

  3. 3 Universidad Pontificia Comillas
    info

    Universidad Pontificia Comillas

    Madrid, España

    ROR https://ror.org/017mdc710

Mostrar afiliaciones +
Revista:
Revista española de financiación y contabilidad

ISSN: 0210-2412

Año de publicación: 2020

Volumen: 49

Número: 1

Páginas: 1-27

Tipo: Artículo

DOI: 10.1080/02102412.2018.1540120 DIALNET GOOGLE SCHOLAR

Otras publicaciones en: Revista española de financiación y contabilidad

Resumen

Este artículo aborda el problema del contagio entre bolsas y utiliza un mapa auto-organizado (SOM) para desarrollar un mapa de dos dimensiones basado en cinco variables del mercado pertenecientes a 48 mercados de todo el mundo durante el periodo 2000 al 2012. El estudio aborda mercados de países con diferentes niveles de desarrollo. Usando cinco variables hemos agrupado las bolsas en diferentes clusters y hemos analizado sus similitudes, diferencias y dinámicas. La técnica aplicada, redes neuronales artificiales (ANNs), constituye una herramienta de visualización no paramétrica para estudiar la dinámica de los mercados mundiales siguiendo una perspectiva no tradicional basada en la formación específica de grupos en su evolución. Cada grupo está definido por las distancias Euclídeas entre los mercados. Hay algunas características comunes, tanto económicas como geográficas, dentro de los mercados estables y de los que migran, que nos ofrecen nuevas ideas. Aplicando la metodología estándar de contagio a los grupos resultantes, estudiamos la influencia de la Crisis Subprime y encontramos evidencia de contagio, además de una alta interdependencia entre los mercados durante todo el periodo considerado.

€ Ver financiación

Información de financiación

This work was supported by the Institute of Business Research, University of Economics Ho Chi Minh City, Vietnam.?Address: 59C Nguyen Dinh Chieu Street, Ward 6, District 3, Ho Chi Minh City, Vietnam. The Authors are grateful to the Editor, Professor Jacobo Gomez-Conde and two anonymous reviewers for their insightful comments and assistance in improving the quality of the paper.

Financiadores

Referencias bibliográficas

  • Alexander, G. J., Eun, C. S., Janakiramanan, S. (1988). International listings and stock returns: Some empirical evidence. Journal of Financial and Quantitative Analysis, 23(2), 135–151. [Crossref], [Web of Science ®], [Google Scholar]
  • Ao, S. I., (2003). Analysis of the interaction of Asian Pacific indices and forecasting opening prices by hybrid VAR and neural network procedure. Proceedings of the International Conference on Computational Intelligence for Modelling Control and Automation 2003(CIMCA2003), Vienna, Austria. Australia: IEEE, Institute of Electrical and Electronics Engineers. [Google Scholar]
  • Ao, S. I., (2003). Incorporating correlated markets’ prices into stock modeling with neural network. Proceedings IASTED International Conference on Modelling and Simulation 2003, Palm Springs, CA, USA, (pp. 353–358). [Google Scholar]
  • Azcarraga, A., Hsieh, M. H., Pan, S. L., Setiono, R. (2008). Improved SOM labeling methodology for data mining applications. In Soft computing for knowledge discovery and data mining (pp. 45–75). Boston, MA: Springer. [Crossref], [Google Scholar]
  • Baker, M., Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129–152. [Crossref], [Web of Science ®], [Google Scholar]
  • Barr, D. S., Mani, G. (1994). Using neural nets to manage investments. AI Expert, 9(2), 16–21. [Google Scholar]
  • Barrera, D., Kayacik, H. G., van Oorschot, P. C., Somayaji, A. (2010, October 4–8). A methodology for empirical analysis of permission-based security models and its application to android. Proceedings of the 17th ACM conference on Computer and communications security, Chicago, IL (pp. 73–84). ACM. doi: 10.1177/1753193409347688 [Google Scholar]
  • Basu, J. K., Bhattacharya, D., Kim, T. H. (2010). Use of artificial neural network in pattern recognition. International Journal of Software Engineering and Its Applications, 4, 2. [Google Scholar]
  • Bloom, J. Z. (2005). Market segmentation: A neural network application. Annals of Tourism Research, 32(1), 93–111. [Crossref], [Web of Science ®], [Google Scholar]
  • Cao, Q., Leggio, K. B., Schniederjans, M. J. (2005). A comparison between Fama and French’s model and artificial neural networks in predicting the Chinese stock market. Computers and Operations Research, 32, 2499–2512. [Crossref], [Web of Science ®], [Google Scholar]
  • Cottrell, M., De Bodt, E., Grégoire, P., (1996). Simulating interest rate structure evolution on a long-term horizon: A kohonen map application. Proceedings of Neural Networks in the Capital Markets. Pasadena, CA: Californian Institute of Technology, World Scientific. [Google Scholar]
  • Davies, D. L., Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1(2), 224–227. [Crossref], [Web of Science ®], [Google Scholar]
  • De la Torre, A., Gozzi, J. C., Schmukler, S. L. (2007). Stock market development under globalization: Whither the gains from reforms? Journal of Banking and Finance, 31(6), 1731–1754. [Crossref], [Web of Science ®], [Google Scholar]
  • Ding, L., Hiltrop, J. (2010). The electronic trading systems and bid-ask spreads in the foreign exchange market. Journal of International Financial Markets, Institutions and Money, 20(4), 323–345. [Crossref], [Web of Science ®], [Google Scholar]
  • Donaldson, R. G., Kamstra, M. (1996). Forecast combining with neural networks. Journal of Forecasting, 15(1), 49–61. [Crossref], [Web of Science ®], [Google Scholar]
  • Ferstl, O. K., Sinz, E. J. (1998). Modeling of business systems using SOM. In Handbook on architectures of information systems (pp. 347–367). Berlin: Springer. [Crossref], [Google Scholar]
  • Forbes, K. J., Rigobon, R. (2002). No contagion, only interdependence: Measuring stock market co-movements. The Journal of Finance, 57(5), 2223–2261. [Crossref], [Web of Science ®], [Google Scholar]
  • Frankel, J., Saravelos, G. (2012). Can leading indicators assess country vulnerability? Evidence from the 2008–09 global financial crisis. Journal of International Economics, 87(2), 216–231. [Crossref], [Web of Science ®], [Google Scholar]
  • García-Estévez, P., Carballo, A. (2015). Qualitative judgement in public credit ratings: A proposed supporting approach using Self-Organising Maps (SOMs). Cuadernos de Economía, 38(108), 181–190. [Crossref], [Web of Science ®], [Google Scholar]
  • Grudnitski, G., Osburn, L. (1993). Forecasting S&P and gold futures prices: An application of neural networks. Journal of Futures Markets, 13(6), 631–643. [Crossref], [Web of Science ®], [Google Scholar]
  • Hagan, M. T., Demuth, H. B., Jesús, O. D. (2002). An introduction to the use of neural networks in control systems. International Journal of Robust and Nonlinear Control, 12(11), 959–985. [Crossref], [Web of Science ®], [Google Scholar]
  • Hertz, J., Krogh, A., Palmer, R. G. (1991). Introduction to the theory of neural computation, volume I of lecture notes of the santa fe institute studies in the science of complexity. Englewood Cliffs, NJ: Prentice Hall. [Google Scholar]
  • Hewitson, B. C., Crane, R. G. (2002). Self-organizing maps: Applications to synoptic climatology. Climate Research, 22(1), 13–26. [Crossref], [Web of Science ®], [Google Scholar]
  • Hopfield, J. J. (1984). Neurons with graded response have collective computational properties like those of two-state neurons. Proceedings of the National Academy of Sciences, 81(10), 3088–3092. [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
  • Jain, A. K., Dubes, R. C. (1988). Algorithms for clustering data (pp. 96–101). Englewood Cliffs, NJ: Prentice Hall. [Google Scholar]
  • Jasic, T., Wood, D. (2004). The profitability of daily stock market indices trades based on neural network predictions: Case study for the sandP 500, the DAX, the TOPIX and the FTSE in the period 1965–1999. Applied Financial Economics, 14(4), 285–297. [Taylor & Francis Online], [Google Scholar]
  • Jegede, O., Awodele, O., Ajayi, A., Ndong, M. (2007, November 16–18). Development of a microcontroller based robotic arm. Computer Science and IT Education Conference 2007 (CSITEd 2007), Mauritius. [Google Scholar]
  • Kalteh, A. M., Hjorth, P., Berndtsson, R. (2008). Review of the self-organizing map (SOM) approach in water resources: Analysis, modelling and application. Environmental Modelling and Software, 23(7), 835–845. [Crossref], [Web of Science ®], [Google Scholar]
  • Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, (43), 59–69. [Crossref], [Web of Science ®], [Google Scholar]
  • Kohonen, T. (1989). Self-organization and associative memory. Berlín: Springer-Verlag. [Crossref], [Google Scholar]
  • Kohonen, T. (1990). The self-organizing map. Proceeding of the IEEE, 9(78), 1464–1480. [Crossref], [Google Scholar]
  • Kohonen, T. (1997). Introduction to SOM toolbox. Helsinki: EET DST. [Google Scholar]
  • Kolarun, J. (2009). Multivariate comparative analysis of stock exchanges: The European perspective, in fink. In Advances in data analysis, data handling and business intelligence. studies in classification, data analysis, and knowledge organization (pp. 461–469). Springer-Verlag Berlin Heidelberg. [Google Scholar]
  • Kourtit, K., Nijkamp, P., Arribas, D. (2012). Smart cities in perspective–A comparative European study by means of self-organizing maps. Innovation: the European Journal of Social Science Research, 25(2), 229–246. [Taylor & Francis Online], [Web of Science ®], [Google Scholar]
  • Kryzanowski, L., Galler, M., Wright, D. W. (1993). Using artificial neural networks to pick stocks. Financial Analysts Journal, 49(4), 21–27. [Taylor & Francis Online], [Google Scholar]
  • Lawrence, R. D., Almasi, G. S., Rushmeier, H. E. (1999). A scalable parallel algorithm for self-organizing maps with applications to sparse data mining problems. Data Mining and Knowledge Discovery, 3(2), 171–195. [Crossref], [Web of Science ®], [Google Scholar]
  • Lee, S. C., Gu, J. C., Suh, Y. H. (2006). A comparative analysis of clustering methodology and application for market segmentation: K-means, SOM and a two-level SOM. International Symposium on Methodologies for Intelligent Systems (pp. 435–444). Springer, Berlin, Heidelberg. [Google Scholar]
  • Levine, R., Schmukler, S. L. (2006). Internationalization and stock market liquidity. Review of Finance, 10(1), 153–187. [Crossref], [Google Scholar]
  • Mantegna, R. N. (1999). Hierarchical structure in financial markets. The European Physical Journal B-Condensed Matter and Complex Systems, 11(1), 193–197. [Crossref], [Google Scholar]
  • Martín, B., Serrano-Cinca, C. (1994). Self-organizing neural networks: The financial state of spanish companies. In A. P. Refenes (Ed.), Neural networks in the capital markets (pp. 341–357). London: Wiley. [Google Scholar]
  • Martín, B., Sanz, A. (1997). Redes Neuronales y Sistemas Borrosos. Madrid: Ra-MA. [Google Scholar]
  • McConnell, J. M., Dybevik, H. J., Haushalter, D., Lie, E., (1996). A survey of evidence on domestic and international stock exchange listings with implications for markets and managers purdue CIBER working papers. Paper 108. http://docs.lib.purdue.edu/ciberwp/108. [Google Scholar]
  • Minsky, M., Papert, S. (1969). Perceptrons: A introduction to computational geometry. MIT press. [Google Scholar]
  • Pagano, M., Randl, O., Röell, A. A., & Zechner, J. (2001). What makes stock exchanges succeed? Evidence from cross-listing decisions. European Economic Review, 45(4–6), 770–782. [Crossref], [Web of Science ®], [Google Scholar]
  • Quinn, D., Voth, H. J. (2008). A century of global equity market correlations. American Economic Review: Papers and Proceedings, 98(2), 535–540. [Crossref], [Web of Science ®], [Google Scholar]
  • Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386. [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
  • Sarlin, P., Peltonen, T. A. (2013). Mapping the state of financial stability. Journal of International Financial Markets, Institutions and Money, 26, 46–76. [Crossref], [Web of Science ®], [Google Scholar]
  • Saudaragan, S. M., Biddle, G. C. (1995). Foreign listing location: A study of multinationals and stock exchanges in eight countries. Journal of International Business Studies, (second quarter), 319–341. [Crossref], [Web of Science ®], [Google Scholar]
  • Serrano-Cinca, C. (1996). Self-organizing neural networks for financial diagnosis. Decision Support Systems, 17(3), 227–238. [Crossref], [Web of Science ®], [Google Scholar]
  • Vesanto, J. (1999). SOM-based data visualization methods. Intelligent Data Analysis, 3(2), 111–126. [Crossref], [Google Scholar]
  • Vesanto, J., Alhoniemil, E. (2000). Clustering of the self-organizing map. IEEE Transactions on Neural Networks, 11(3), 586–600. [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
  • Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J. (2000). SOM toolbox for matlab 5. Helsinky University of Technology. [Google Scholar]
  • Willshaw, D. J., Von Der Malsburg, C. (1976). How patterned neural connections can be set up by self-organization. Proceedings of the Royal Society B, London, 194(1117), 431–445. [Crossref], [Google Scholar]
Fuente de los datos: Dialnet