Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange

Mustafa Jahangoshai Rezaee, Mehrdad Jozmaleki and Mahsa Valipour

Physica A: Statistical Mechanics and its Applications, 2018, vol. 489, issue C, 78-93

Abstract: One of the main features to invest in stock exchange companies is their financial performance. On the other hand, conventional evaluation methods such as data envelopment analysis are not only a retrospective process, but are also a process, which are incomplete and ineffective approaches to evaluate the companies in the future. To remove this problem, it is required to plan an expert system for evaluating organizations when the online data are received from stock exchange market. This paper deals with an approach for predicting the online financial performance of companies when data are received in different time’s intervals. The proposed approach is based on integrating fuzzy C-means (FCM), data envelopment analysis (DEA) and artificial neural network (ANN). The classical FCM method is unable to update the number of clusters and their members when the data are changed or the new data are received. Hence, this method is developed in order to make dynamic features for the number of clusters and clusters members in classical FCM. Then, DEA is used to evaluate DMUs by using financial ratios to provide targets in neural network. Finally, the designed network is trained and prepared for predicting companies’ future performance. The data on Tehran Stock Market companies for six consecutive years (2007–2012) are used to show the abilities of the proposed approach.

Keywords: Prediction; Dynamic fuzzy C-means; Self-organizing map; Data envelopment analysis; Multi-layer perceptron; Stock exchange market performance (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:489:y:2018:i:c:p:78-93

DOI: 10.1016/j.physa.2017.07.017

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