ANN-DEA Approach of Corporate Diversification and Efficiency in Bursa Malaysia

Subject Areas : Financial Mathematics

Meysam Doaei ¹ , Seyed Hashem Davarpanah ² , Mahdi Sabzi ³

1 - Assistant Professor, Department of Management, Esfarayen Branch, Islamic Azad University, Esfarayen, Iran
2 - School of Information and Communication Technology, Griffith University, Brisbane, Queensland, Australia
3 - Senior Expert at Securities and Exchange Organization, Tehran, Iran

Received: 2016-06-12 Accepted : 2016-12-28 Published : 2017-03-01

Keywords: Data envelopment analysis, Artificial Neural Networks, Total Product Diversification, International Diversification,

Abstract :

There is little consensus on the corporate diversification-efficiency relationship in the diversification literature. According to the corporate diversification, firms have a tendency to get more market share with diversifying in the local segment or in the international market. Theoretically, a contradictory exists between the profitable strategy and the value reducing strategy in the diversification strategy. In this paper, we measure firm’s efficiency by applying Data Envelopment Analysis (DEA) in manufacturing firms listed in Bursa Malaysia for five years. Meanwhile, a feed forward multilayer perceptron neural network is applied to model the mapping function between the input and output data to the efficiency score. Back propagation (BP) learning algorithm is applied to update network’s weights through minimizing the cost function, and the best topology of the network is conducted. The result of this study shows that there is a negative relationship between total product diversification and efficiency, and international diversification has a non-linear effect on the efficiency.

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