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Evaluating the performances of over-the-counter companies in developing countries using a stochastic dominance criterion and a PSO-ANN hybrid optimization model.

Nasseri, A., Jamshidi, S., Yazdifar, H., Percy, D. and Alam, M.A., 2020. Evaluating the performances of over-the-counter companies in developing countries using a stochastic dominance criterion and a PSO-ANN hybrid optimization model. Journal of Applied Accounting Research. (In Press)

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DOI: 10.1108/JAAR-09-2019-0137

Abstract

With suitable optimization criteria, hybrid models have proven to be efficient for preparing portfolios in capital markets of developed countries. This study adapts and investigates these methods for a developing country, so providing a novel approach to the application of banking and finance. Our specific objectives are to employ a stochastic dominance criterion to evaluate the performances of over-the-counter (OTC) companies in a developing country and to analyse them with a hybrid model involving particle swarm optimization and artificial neural networks. In order to achieve these aims, we conduct a case study of OTC companies in Iran. Weekly and daily returns of 36 companies listed in this market are calculated for one year during 2014-2015. The hybrid model is particularly interesting and our results identify first, second and third-order stochastic dominances among these companies. Our chosen model uses the best performing combination of activation functions in our analysis, corresponding to TPT where T represents hyperbolic tangent transfers and P represents linear transfers. Our portfolios are based on the shares of companies ranked with respect to the stochastic dominance criterion. Considering the minimum and maximum numbers of shares to be 2 and 10 for each portfolio, an eight-share portfolio is determined to be optimal. Compared with the index of Iran OTC during the research period of this study, our selected portfolio achieves a significantly better performance. Moreover, the methods used in this analysis are shown to be as efficient as they were in the capital markets of developed countries.

Item Type:Article
ISSN:0967-5426
Uncontrolled Keywords:Developing countries; portfolio optimization; stochastic dominance; particle storm optimization; artificial neural networks
Group:Faculty of Management
ID Code:33385
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:11 Feb 2020 10:26
Last Modified:11 May 2020 12:03

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