Optimal decision for the market graph identification problem in a sign similarity network

Optimal decision for the market graph identification problem in a sign similarity network Research into the market graph is attracting increasing attention in stock market analysis. One of the important problems connected with the market graph is its identification from observations. The standard way of identifying the market graph is to use a simple procedure based on statistical estimations of Pearson correlations between pairs of stocks. Recently a new class of statistical procedures for market graph identification was introduced and the optimality of these procedures in the Pearson correlation Gaussian network was proved. However, the procedures obtained have a high reliability only for Gaussian multivariate distributions of stock attributes. One of the ways to correct this problem is to consider different networks generated by different measures of pairwise similarity of stocks. A new and promising model in this context is the sign similarity network. In this paper the market graph identification problem in the sign similarity network is reviewed. A new class of statistical procedures for the market graph identification is introduced and the optimality of these procedures is proved. Numerical experiments reveal an essential difference in the quality between optimal procedures in sign similarity and Pearson correlation networks. In particular, it is observed that the quality of the optimal identification procedure in the sign similarity network is not sensitive to the assumptions on the distribution of stock attributes. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Operations Research Springer Journals

Optimal decision for the market graph identification problem in a sign similarity network

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Business and Management; Operations Research/Decision Theory; Combinatorics; Theory of Computation
ISSN
0254-5330
eISSN
1572-9338
DOI
10.1007/s10479-017-2491-6
Publisher site
See Article on Publisher Site

Abstract

Research into the market graph is attracting increasing attention in stock market analysis. One of the important problems connected with the market graph is its identification from observations. The standard way of identifying the market graph is to use a simple procedure based on statistical estimations of Pearson correlations between pairs of stocks. Recently a new class of statistical procedures for market graph identification was introduced and the optimality of these procedures in the Pearson correlation Gaussian network was proved. However, the procedures obtained have a high reliability only for Gaussian multivariate distributions of stock attributes. One of the ways to correct this problem is to consider different networks generated by different measures of pairwise similarity of stocks. A new and promising model in this context is the sign similarity network. In this paper the market graph identification problem in the sign similarity network is reviewed. A new class of statistical procedures for the market graph identification is introduced and the optimality of these procedures is proved. Numerical experiments reveal an essential difference in the quality between optimal procedures in sign similarity and Pearson correlation networks. In particular, it is observed that the quality of the optimal identification procedure in the sign similarity network is not sensitive to the assumptions on the distribution of stock attributes.

Journal

Annals of Operations Research – Springer Journals

Published: Apr 3, 2017

References

  • Simple measure of similarity for the market graph construction
    Bautin, GA; Kalyagin, VA; Koldanov, AP; Koldanov, PA; Pardalos, PM
  • Innovations in financial and economic networks
    Boginsky, V; Butenko, S; Pardalos, PM
  • Network model of massive data sets
    Boginsky, V; Butenko, S; Pardalos, PM
  • Statistical analysis of financial networks
    Boginsky, V; Butenko, S; Pardalos, PM
  • Mining market data: a network approach
    Boginsky, V; Butenko, S; Pardalos, PM
  • A network-based data mining approach to portfolio selection via weighted clique relaxations
    Boginski, V; Butenko, S; Shirokikh, O; Trukhanov, S; Lafuente, JG
  • A new method for mean-variance portfolio optimization with cardinality constraints
    Cesarone, F; Scozzari, A; Tardella, F
  • Correlation study of the Athens stock exchange
    Garas, F; Argyrakis, P
  • Elliptically contoured models in statistics and portfolio theory
    Gupta, FK; Varga, T; Bodnar, T
  • Hub discovery in partial correlation graphs
    Hero, A; Rajaratnam, B
  • A network analysis of the Chinese stock market
    Huang, WQ; Zhuang, XT; Yao, SA
  • Robust identification in random variables networks
    Kalyagin, VA; Koldanov, AP; Koldanov, PA
  • Dominating clasp of the financial sector revealed by partial correlation analysis of the stock market
    Kenett, DY; Tumminello, M; Madi, A; Gur-Gershgoren, G; Mantegna, RN; Ben-Jacob, E
  • Statistical procedures for the market graph construction
    Koldanov, AP; Koldanov, PA; Kalyagin, VA; Pardalos, PM
  • Mathematical methods of statistics
    Kramer, H
  • A theory of some multiple decision procedures 1
    Lehmann, EL
  • Testing statistical hypotheses
    Lehmann, EL; Romano, JP
  • Hierarchical structure in financial markets
    Mantegna, RN
  • Network analysis of a financial market based on genuine correlation and threshold method
    Namaki, A; Shirazi, AH; Raei, R; Jafari, GR
  • Clustering and information in correlation based financial networks
    Onella, J-P; Kaski, K; Kertesz, J
  • Computational study of the US stock market evolution: A rank correlation-based network model
    Shirokikh, J; Pastukhov, G; Boginski, V; Butenko, S
  • A network perspective of the stock market
    Tse, CK; Liu, J; Lau, FCM
  • Spanning trees and bootstrap reliability estimation in correlation-based network
    Tumminello, M; Coronello, C; Lillo, F; Micciche, S; Mantegna, R
  • Network approach for the Russian stock market
    Vizgunov, AN; Goldengorin, B; Kalyagin, VA; Koldanov, AP; Koldanov, PA; Pardalos, PM
  • Statistical decision function
    Wald, A
  • Similarity measure and topology evolution of foreign exchange markets using dynamic time warping method: Evidence from minimal spanning tree
    Wang, GJ; Chi, X; Han, F; Sun, B
  • Likelihood robust optimization for data-driven problems
    Wang, Z; Glynn, PW; Ye, Y

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