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

• Main Authors: Meysam Doaei, Seyed Hashem Davarpanah, Mahdi Sabzi
• Format: Article
• Language: English
• Published: Islamic Azad University of Arak 2017-03-01
• Series: Advances in Mathematical Finance and Applications
• Subjects: Total Product Diversification; International Diversification; Data envelopment analysis; Artificial Neural Networks
• Online Access: http://amfa.iau-arak.ac.ir/article_529058_d31a3e18cb2515e35495972e84f57fe7.pdf

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.