Modeling the competitive market efficiency of Egyptian companies: A probabilistic neural network analysis

Authors:
Mohamed M. Mostafa
Affiliations:
Auburn University, College of Business, 415 West Magnolia Avenue, 242 Lowder Business Building, Auburn, AL 36849, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 11
Cited 1

Probabilistic neural networks

Neural Networks
Symbolic and Neural Learning Algorithms: An Experimental Comparison

Machine Learning
Academic departments efficiency via DEA

Computers and Operations Research
The unpredictability of standard back propagation neural networks in classification applications

Management Science
Profitability and Marketability of the Top 55 U.S. Commercial Banks

Management Science - Special issue on the performance of financial Institutions
Dynamics of Data Envelopment Analysis: Theory of Systems Efficiency

Dynamics of Data Envelopment Analysis: Theory of Systems Efficiency
Adaptive non-parametric efficiency frontier analysis: a neural-network-based model

Computers and Operations Research
Measuring DEA efficiency in internet companies

Decision Support Systems
Modelling small-business credit scoring by using logistic regression, neural networks and decision trees: Research Articles

International Journal of Intelligent Systems in Accounting and Finance Management
The use of data mining and neural networks for forecasting stock market returns

Expert Systems with Applications: An International Journal
Classification of freeway traffic patterns for incident detection using constructive probabilistic neural networks

IEEE Transactions on Neural Networks

An integrated artificial neural network algorithm for performance assessment and optimization of decision making units

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

Understanding efficiency levels is crucial for understanding the competitive structure of a market and/or segments of a market. This study uses two artificial neural networks (NN) and a traditional statistical classification method to classify the relative efficiency of top listed Egyptian companies. Accuracy indices derived from the application of a non-parametric data envelopment analysis approach are used to assess the classification accuracy of the models. Results indicate that the NN models are superior to the traditional statistical methods. The study shows that the NN models have a great potential for the classification of companies' relative efficiency due to their robustness and flexibility of modeling algorithms. The implications of these results for potential efficiency programs are discussed.