László Nagy
​Department of Finance, Budapest University of Technology and Economics, Magyar tudósok körútja 2, Budapest H-1117, Hungary
Mihály Ormos
Department of Finance and Accounting, Eötvös Loránd University, Szép utca 2. Budapest H-1053, Hungary
and Department of Economics, J. Selye University, Bratislavská cesta 3322, SK-94501 Komárno, Slovakia
​​DOI: https://doi.org/10.31410/EMAN.2018.181


2nd International Scientific Conference – EMAN 2018 – Economics and Management: How to Cope With Disrupted Times, Ljubljana – Slovenia, March 22, 2018, CONFERENCE PROCEEDINGS published by: Association of Economists and Managers of the Balkans, Belgrade, Serbia; Faculty of Management Koper, Slovenia; Doba Business School – Maribor, Slovenia; Integrated Business Faculty – Skopje, Macedonia; Faculty of Management – Zajecar, Serbia, ISBN 978-86-80194-11-0


Abstract​

We introduce a spectral clustering-based method to show that stock prices contain not only firm, but also network level information. We cluster different stock indices and reconstruct the equity index graph from historical daily closing prices. We find that tail events have a minor effect on the equity index structure. Gaussian clusters can explain a substantial part of the total variance. Thus, mean-variance analysis with Gaussian clusters gives significant regression estimations. In addition, cluster-wise regressions also provide significant and stationer results.

Key words

cluster analysis, equity index networks, machine learning


References

  1. B. Engelmann, E. Hayden and D. Tasche.(2003). Measuring the Discriminative Power of Rating Systems. Banking and Financial Supervision No 01/2003
  2. G. Leibon, S. D. Pauls, D Rockmore and R. Savell. (2008). Topological Structures in the Equities Market Network. PNAS, Vol. 105, 20589-20594.
  3. J. Shi and J. Malik. (2000). Normalized cuts and image segmentation. Pattern Analysis and Machine Intelligence IEEE, Transactions, N.J., 888-905.
  4. M. Bolla. (2011). Penalized version of Newman-Girvan modularity and their relation to normalized cuts and k-means clustering. Physical review Vol. 84.
  5. M. Bolla. (2013). Spectral Clustering and Biclustering. Learning Large Graphs and Contingency Tables. Wiley
  6. M. Filippone, F Camastra, F. Masulli and S. Rovetta. (2007). A survey of kernel and spectral methods for clustering. Pattern recognition Vol. 41, 176-190.
  7. M. Ormos and D. Zibriczky. (2014). Entropy-Based Financial Asset Pricing. PLoS ONE 9(12): e115742.
  8. R. Yalamova. (2009). Correlations in Financial Time Series during Extreme Events. Spectral Clustering and Partition Decoupling Method. Proc. of World Congress on Eng.
  9. U. von Luxburg. (2007). Tutorial on Spectral Clustering. Statistics and Computing. Vol. 17, 395-416.
  10. Y. Zhao. (2015). R and Data Mining: Examples and Case Study. Elsevir Inc.

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