School of Computing, Dublin City University, Ireland

MODELLING & SCIENTIFIC COMPUTING


  
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Research:    Social Systems // Environmental and Transport // Financial and Socioeconomic Modelling // Assisted Living // Biocomputation // Data Science
  

   

Social Systems:
Financial and Socioeconomic Modelling

Financial Markets are hierarchical systems, with layers of complexity. Complementary statistical measures and diverse models are necessary to sift the available evidence on bubbles, crashes and other market movements. In the group, we explore the potential for prediction of a multi-layered approach, incorporating three principal strands, (using sourced data). We aim

  • To identify statistical measures of market co-movement by analysis of eigenvalues of the variance-covariance matrix of daily price indices and associated time series.
  • To identify noise elements of this matrix, by use of a novel fractional calculus approach. This will provide a decision-tool to pinpoint future real market changes.
  • To explore the impact of preliminary indications on movement, by means of statistical physics models, which emphasise connected (or co-operative) behaviour
These may be described in terms of agent interaction or herd influence in market trading but, while some limited exposure has been given to these ideas, our focus is on the granularity of the market response to measurable change in coherence.

The approach proposed is highly inter-disciplinary and will provide for analysis of comparative market behaviour across different industrial sectors both nationally and internationally. As a prototype method for early risk assessment, we anticipate consequent benefit to informed and strategic decision-making, together with potential for wider applicability to other complex social systems. Furthermore, the integrated nature of the approach will facilitate assessment of intervention strategies under negative market co-movements.

Selected Group Publications:

  • Al-Sharkasi, A., Ruskin, H.J., Crane, M., Matos, J. and Gama, S.M.A., A Wavelet-Based Method to Measure Stock Market Development, Open Journal of Statistics, vol. 4 (1), pp. 89 - 96, 2014. DOI: 10.4236/ojs.2014.41009.

  • Daly, J., Ruskin, H. J., and Crane, M., Can random matrix filters be used for trading in the foreign exchange market? - A comparison of foreign exchange and stock portfolio filtering. Journal of Dynamics of Socio-Economic Systems, 2:2, 2011

  • Conlon, T., Ruskin, H.J., Crane, M. Multiscaled Cross-Correlation Dynamics in Financial Time Series, Advances in Complex Systems, 12(4-5), 439-454, 2009.


    Researchers: Martin Crane, Heather J. Ruskin, Na Li, Adel Al-Sharkasi (Assoc.), Jose-Abilio Matos (Assoc.)


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    Contact: +353 1 700 6747 / modsci@computing.dcu.ie