全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

Soft Vibrational Force on Stock Market Networks

DOI: 10.4236/oalib.1103050, PP. 1-13

Subject Areas: Discrete Mathematics, Applied Statistical Mathematics, Combinatorial Mathematics, Mathematical Economics

Keywords: Network Modelling, Multivariate Analysis, Soft Analysis, Stock Exchange Network, Soft Set Theory

Full-Text   Cite this paper   Add to My Lib

Abstract

Stock market networks commonly involve uncertainty, and the theory of soft sets emerges as a powerful tool to handle it. In this study, we present a soft analogue of the differential of a vibrational potential function acting on a stock market network as vibrational force. For this purpose, we first study the vibrational potential function operating on each vertex by using the Laplacian of the neighborhood graph, then applied the soft approximator for the soft sets where the data points are embedded to Euclidean n space. We used the data of the globally operating leading stock markets of 17 countries and presented the results respect to them.

Cite this paper

Balci, M. A. and Akgüller, Q. (2016). Soft Vibrational Force on Stock Market Networks. Open Access Library Journal, 3, e3050. doi: http://dx.doi.org/10.4236/oalib.1103050.

References

[1]  Mantegna, R.N. (1999) Hierarchical Structure in Financial Markets. The European Physical Journal B-Condensed Matter and Complex Systems, 11, 193-197.
http://dx.doi.org/10.1007/s100510050929
[2]  Avc, E. (2007) Forecasting Daily and Sessional Returns of the ISE-100 Index with Neural Network Models. Journal of Dogus University, 8, 128-142.
[3]  Boyacioglu, M.A. and Avci, D. (2010) An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the Prediction of Stock Market Return: The Case of the Istanbul Stock Exchange. Expert Systems with Applications, 37, 7908-7912.
http://dx.doi.org/10.1016/j.eswa.2010.04.045
[4]  Zhang, Y.D. and Wu, L.N. (2009) Stock Market Prediction of S&P 500 via Combination of Improved BCO Approach and BP Neural Network. Expert Systems with Applications, 36, 8849-8854.
[5]  Shamsuddin, S.M., Jaaman, S.H. and Darus, M. (2009) Neuro-Rough Trading Rules for Mining Kuala Lumpur Composite Index. European Journal of Scientific Research, 28, 278- 286.
[6]  Wang, X.Y. and Wang, Z.O. (2002) Stock Market Time Series Data Mining Based on Regularized Neural Network and Rough Set. Proceedings of International Conference on Machine Learning and Cybernetics, 1, 315-318.
[7]  Molodtsov, D. (1999) Soft Set Theory—First Results. Computers and Mathematics with Applications, 37, 19-31.
http://dx.doi.org/10.1016/S0898-1221(99)00056-5
[8]  Aktas, H. and Cagman, N. (2007) Soft Sets and Soft Groups. Information Sciences, 177, 2726-2735.
http://dx.doi.org/10.1016/j.ins.2006.12.008
[9]  Jun, Y.B. (2008) Soft BCK/BCI-Algebras. Computers and Mathematics with Applications, 56, 1408-1413.
http://dx.doi.org/10.1016/j.camwa.2008.02.035
[10]  Ge, X., Li, Z. and Ge, Y. (2011) Topological Spaces and Soft Sets. Journal of Computational Analysis and Applications, 13, 881-885.
[11]  Tanay, B. and Kandemir, M.B. (2015) Results on Fuzzy Soft Functions. New Trends in Mathematical Sciences, 3, 1-17.
[12]  Feng, F., Li, C., Davvaz, B. and Ali, M.I. (2010) Soft Sets Combined with Fuzzy Sets and Rough Sets: A Tentative Approach. Soft Computing, 14, 899-911.
http://dx.doi.org/10.1007/s00500-009-0465-6
[13]  Xu, W., Ma, J., Wang, S. and Hao, G. (2010) Vague Soft Sets and Their Peoperties. Computers and Mathematics with Applications, 59, 787-794.
http://dx.doi.org/10.1016/j.camwa.2009.10.015
[14]  Zhang, W., Zhong, W. and Guo, X. (2014) An Explicit Length Scale Control Approach in SIMP-Based Topology Optimization. Computer Methods in Applied Mechanics and Engineering, 282, 71-86.
http://dx.doi.org/10.1016/j.cma.2014.08.027
[15]  Feng, F., Jun, Y.B., Liu, X. and Li, L. (2010) An Adjustable Approach to Fuzzy Soft Set Based Decision Making. Journal of Computational and Applied Mathematics, 234, 10-20.
http://dx.doi.org/10.1016/j.cam.2009.11.055
[16]  Ali Balci, M. and Akguller, O. (2015) Mathematical Morphology on Soft Sets for Application to Metabolic Networks. In: An Le Thi, H., Nguyen, N.T. and Van Do, T., Eds., Advanced Computational Methods for Knowledge Engineering, Springer International Publishing, Berlin, 209-218.
[17]  Kalayatkankal, S.J. and Suresh Singh, G. (2010) A Fuzzy Soft Flood Alarm Model. Mathematics and Computers in Simulation, 80, 887-893.
http://dx.doi.org/10.1016/j.matcom.2009.10.003
[18]  Herewan, T. and Deris, M.M. (2011) A Soft Set Approach for Association Rules Mining. Knowledge Based Systems, 24, 186-195.
http://dx.doi.org/10.1016/j.knosys.2010.08.005
[19]  Maji, P.K., Biswas, R. and Roy, A.R. (2003) Soft Set Theory. Computers and Mathematics with Applications, 45, 555-562.
http://dx.doi.org/10.1016/S0898-1221(03)00016-6
[20]  West, D.B. (2001) Introduction to Graph Theory. Vol. 2, Prentice Hall, Upper Saddle River.
[21]  Ernesto, E. and Hatano, N. (2010) A Vibrational Approach to Node Centrality and Vulnerability in Complex Networks. Physica A: Statistical Mechanics and Its Applications, 389, 3648-3660.
http://dx.doi.org/10.1016/j.physa.2010.03.030
[22]  Ulrike, V.L. (2007) A Tutorial on Spectral Clustering. Statistics and Computing, 17, 395- 416.
http://dx.doi.org/10.1007/s11222-007-9033-z
[23]  Newman, M. (2010) Networks: An Introduction. Oxford University Press, Oxford.
http://dx.doi.org/10.1093/acprof:oso/9780199206650.001.0001
[24]  Pallaschke, D. and Rolewicz, S. (2013) Foundations of Mathematical Optimization: Convex Analysis without Linearity. Vol. 388, Springer, Berlin.
[25]  Stumme, G. (2009) Formal Concept Analysis. In: Staab, S. and Studer, R., Eds., Handbook on Ontologies, Springer, Berlin, 177-199.
http://dx.doi.org/10.1007/978-3-540-92673-3_8
[26]  Bailey, T.C. and Gatrell, A.C. (1995) Interactive Spatial Data Analysis. Vol. 413, Longman Scientific & Technical, Essex.
[27]  Claude, B. (1973) Graphs and Hypergraphs. Vol. 7, North-Holland Publishing Company, Amsterdam.
[28]  Claude, B. (1984) Hypergraphs: Combinatorics of Finite Sets. Vol. 45, Elsevier, Amsterdam.
[29]  Estrada, E. and Hatano, N. (2010) Topological Atomic Displacements, Kirchhoff and Wiener Indices of Molecules. Chemical Physics Letters, 486, 166-170.
http://dx.doi.org/10.1016/j.cplett.2009.12.090
[30]  Abraham, A., Nath, B. and Mahanti, P.K. (2001) Hybrid Intelligent Systems for Stock Market Analysis. International Conference on Computational Science, San Francisco, 28-30 May 2001, 337-345.
[31]  Qian, M.C., Jiang, Z.Q. and Zhou, W.X. (2010) Universal and Nonuniversal Allometric Scaling Behaviors in the Visibility Graphs of World Stock Market Indices. Journal of Physics A: Mathematical and Theoretical, 43, Article ID: 335002.
http://dx.doi.org/10.1088/1751-8113/43/33/335002
[32]  Naylor, M.J., Rose, L.C. and Moyle, B.J. (2007) Topology of Foreign Exchange Markets Using Hierarchical Structure Methods. Physica A: Statistical Mechanics and Its Applications, 382, 199-208.
http://dx.doi.org/10.1016/j.physa.2007.02.019
[33]  Newman, M.E. and Girvan, M. (2004) Finding and Evaluating Community Structure in Networks. Physical Review E, 69, Article ID: 026113.
http://dx.doi.org/10.1103/physreve.69.026113
[34]  Boccaletti, S., Latora, V., Moreno, Y., Chavez, M. and Hwang, D.U. (2006) Complex Networks: Structure and Dynamics. Physics Reports, 424, 175-308.
http://dx.doi.org/10.1016/j.physrep.2005.10.009

Full-Text


comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133

WeChat 1538708413