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

15 pages
Read Article

Loading next page...
 
/lp/springer_journal/optimal-decision-for-the-market-graph-identification-problem-in-a-sign-3Ug1ZCzC7S
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
D.O.I.
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

You’re reading a free preview. Subscribe to read the entire article.

Try 2 weeks free now

DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

or browse the journals available

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create folders to
organize your research

Export folders, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off

Sign up
for free
Start 14 day
Free Trial