Selection of Investment Basis Using Neural Networks in Stock Exchange

Ebrahim  Kalani; Reza Baradaran  Kazem-Zadeh; Alireza  Elhami; Ehsan  Kamrani

doi:10.4236/ajibm.2018.83036

Ebrahim Kalani^1,2, Alireza Elhami², Reza Baradaran Kazem-Zadeh¹, Ehsan Kamrani³

Abstract: A generalization is considered on the standard Marvowitz mean-variance model, which includes some limitative limbs. These restrictions guarantee the investment in a certain number of assets and limit the amount of capital that must be invested in any asset (stock). When the Markovitz mean-variance model is considered, the basket selection problem is a quadratic programming problem. But if this model is generalized with limitations, then the basket selection problem will be transformed into a quadratic programming and numerical design. In this recent model, there is no algorithm and method that can solve the basket selection problem optimally. In this case, the use of the heuristic algorithm is essential. Here in this paper a special neural network model has been used. The Hopfield network has been used to optimize some of the other optimization problems and it is used to solve the portfolio selection problem.

Keywords: Information Technology, Supply Chain, Integrity, Performance, Flexibility

Cite this paper: Kalani, E. , Elhami, A. , Kazem-Zadeh, R. and Kamrani, E. (2018) Selection of Investment Basis Using Neural Networks in Stock Exchange. American Journal of Industrial and Business Management, 8, 548-562. doi: 10.4236/ajibm.2018.83036.

References

[1] Fernández, A. and Gómez, S. (2007) Portfolio Selection Using Neural Networks. Computers & Operations Research.

[2] Chang, T.-J., Meade, N., Beasley, J. and Sharaiha, Y. (2000) Heuristics for Cardinality Constrained Portfolio Optimization. Computers & Operations Research, 27, 1271-1302.
https://doi.org/10.1016/S0305-0548(99)00074-X

[3] Fieldsend, J., Matatko, J. and Peng, M. Cardinality Constrained Portfolio Optimization.

[4] Gilli, M. and Këllezi, E. (2000) Heuristic Approaches for Portfolio Optimization. Sixth International Conference on Computing in Economics and Finance of the Society for Computational Economics, Barcelona, 6-8 July 2000.

[5] Kellerer, H. and Maringer, D. (2001) Optimization of Cardinality Constrained Portfolios with an Hybrid Local Search Algortihm. MIC’2001—4th Methaheuristics International Conference, Porto, 16-20 July 2001.

[6] Lin, D., Wang, S. and Yan, H. (2001) A Multiobjective Genetic Algorithm for Portfolio Selection Problem. Proceedings of ICOTA 2001, Hong Kong, 15-17 December 2001.

[7] Markowitz, H. (1952) Portfolio Selection. Journal of Finance, 7, 77-91.

[8] Sawaragi, Y., Nakayama, H. and Tanino, T. (1985) Theory of Multiobjective Optimization. In: Bellman, R., Ed., Mathematics in Science and Engineering, Vol. 176. Academic Press Inc.

[9] Schaerf, A. (2002) Local Search Techniques for Constrained Portfolio Selection Problems. Computational Economics, 20, 177-190.
https://doi.org/10.1023/A:1020920706534

[10] Streichert, F, Ulmer, H. and Zell, A. Evolutionary Algorithms and the Cardinality Constrained Portfolio Optimization Problem.

[11] Xia, Y., Liu, B., Wang, S. and Lai, K. (2000) A Model for Portfolio Selection with Order of Expected Returns. Computers & Operations Research, 27, 409-422.
https://doi.org/10.1016/S0305-0548(99)00059-3

[12] James, T. (1965) Money and Economic Growth. Econometrica, 33, 671-684.

[13] James, T. (1965) The Theory of Portfolio Selection. In: Hann, F.H. and Brechling, F.P.R., Eds., The Theory of Interest Rates, Macmillan, New York.

[14] James, T. (1958) Liquidity Preference as Behavior towards RISK. Review of Economic Studies, 65-86.

[15] Strong, R., (2008) Portfolio Construction, Management, and Protection. Nelson Education, 10 October 2008.

[16] Aaron, K. (2016) The Effects of Global Mergers and Acquisitions on Corporations’ Profitability: A Longitudinal Econometric Study. Ph.D. Diss., University of Phoenix.