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

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.