Providing a Model for Selecting the Optimal Stock Portfolio Using Salp Swarm Algorithm and Multilayer Perceptron Neural Networks
Subject Areas : Financial engineering
Seyed Ali Hoseini
1
,
zahra pourzamani
2
,
Aَzita Jahanshad
3
1 - Department of Accounting, Central Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Department of Accounting, Central Tehran Branch, Islamic Azad University, Tehran, Iran
3 - Department of Accounting, Central Tehran Branch, Islamic Azad University, Tehran, Iran
Keywords: Optimal Stock Portfolio, Multilayer Perceptron Neural Networks, Salp Swarm Algorithm,
Abstract :
The most important courses are the ones that are taught and the one that is taught and the ones that are taught are the ones that work for each other, in order to make the most profit.In our research, it can be seen that all sorts of solutions are one of the solutions, but the concept of skewness should be considered in the future as well. In the first twenty-first of the first fifty years of 2019, the stock market is given as an example..Evolution is also a model in which the future potential of stocks is predicted by the multilayer perceptron neural network with several scenarios, including the prediction of the stock price time series method itself or the prediction of the impact of factors influencing stock price changes. The results show that the models presented in this article, compared to traditional methods, provide investors with and achieve the optimal formation of the portfolio by selecting the appropriate shares of companies.
References:
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D Abbasi, M Ashrafi, SH Ghodsypour.(2020). A multi objective-BSC model for new product development project portfolio selection. Expert Systems with Applications.pp 61-72.
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YT Chen, HQ Yang.(2020). Multi-period mean-variance portfolio selection with practical constraints using heuristic genetic algorithms. International Journal of Computational.pp 11-16.
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terms of means, variances, and higher moments, Review of Economic Studies. Volume 37,
Issue 4, October, pp 537–542.
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terms of means, variances, and higher moments, Review of Economic Studies. Volume 37,
Issue 4, October, pp 537–542.
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terms of means, variances, and higher moments, Review of Economic Studies. Volume 37,
Issue 4, October, pp 537–542.
