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

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.

[1]  H. David Sherman,et al.  Do bank mergers have hidden or foregone value? Realized and unrealized operating synergies in one bank merger , 2006, Eur. J. Oper. Res..

[2]  Allen N. Berger,et al.  Consistency Conditions for Regulatory Analysis of Financial Institutions: A Comparison of Frontier Efficiency Methods , 1998 .

[3]  Scott B. Wilson.  Algorithm architectures for patient dependent seizure detection , 2006, Clinical Neurophysiology.

[4]  R. Hecht-Nielsen,et al.  Theory of the Back Propagation Neural Network , 1989 .

[5]  John C.S. Tang,et al.  The evaluation of bank branch performance using data envelopment analysis: A framework , 2002 .

[6]  Kevin Cullinane,et al.  The technical efficiency of container ports: Comparing data envelopment analysis and stochastic frontier analysis , 2006 .

[7]  Gary D. Ferrier,et al.  Consistency Conditions for Regulatory Analysis of Financial Institutions: A Comparison of Frontier Efficiency Methods , 1998 .

[8]  Judith H Hibbard,et al.  Does publicizing hospital performance stimulate quality improvement efforts? , 2003, Health affairs.

[9]  Xueming Luo,et al.  Assessing advertising media spending inefficiencies in generating sales , 2005 .

[10]  D. U. A. Galagedera,et al.  Experimental evidence on robustness of data envelopment analysis , 2003, J. Oper. Res. Soc..

[11]  Mohamed M. Mostafa,et al.  Forecasting the Suez Canal traffic: a neural network analysis , 2004 .

[12]  Wen-Min Lu,et al.  Does Size Matter? Finding the Profitability and Marketability Benchmark of Financial Holding Companies , 2006, Asia Pac. J. Oper. Res..

[13]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[14]  Guowang Xu,et al.  Application of probabilistic neural network in the clinical diagnosis of cancers based on clinical chemistry data , 2002 .

[15]  Göran Bergendahl,et al.  DEA and benchmarks – an application to Nordic banks , 1998, Ann. Oper. Res..

[16]  A. J. Englande,et al.  Prediction of swimmability in a brackish water body , 2006 .

[17]  B. Casu,et al.  A comparative study of efficiency in European banking , 2003 .

[18]  Xin Jin,et al.  Classification of freeway traffic patterns for incident detection using constructive probabilistic neural networks , 2001, IEEE Trans. Neural Networks.

[19]  Chenn-Jung Huang,et al.  CLASS PREDICTION OF CANCER USING PROBABILISTIC NEURAL NETWORKS AND RELATIVE CORRELATION METRIC , 2004, Appl. Artif. Intell..

[20]  Miika Linna,et al.  The association between quality of care and technical efficiency in long-term care. , 2005, International journal for quality in health care : journal of the International Society for Quality in Health Care.

[21]  A. U.S.,et al.  Measuring the efficiency of decision making units , 2003 .

[22]  Peter Jones,et al.  Productivity in hotels: a stepwise data envelopment analysis of hotels' rooms division processes , 2005 .

[23]  M. Porter.  The Competitive Advantage Of Nations , 1990 .

[24]  S. Miller,et al.  The technical efficiency of large bank production , 1996 .

[25]  Joseph Sarkis.  An analysis of the operational efficiency of major airports in the United States , 2000 .

[26]  C. Lovell,et al.  The impact of liberalization on the productive efficiency of Indian commercial banks , 1997 .

[27]  Carlos Pestana Barros,et al.  Hypermarket retail store efficiency in Portugal , 2003 .

[28]  Paul W. Wilson,et al.  Detecting influential observations in data envelopment analysis , 1995 .

[29]  Antreas D. Athanassopoulos,et al.  A Comparison of Data Envelopment Analysis and Artificial Neural Networks as Tools for Assessing the Efficiency of Decision Making Units , 1996 .

[30]  Rakesh Gupta,et al.  Comparing SFA and DEA methods on measuring production efficiency for forest and paper companies , 2005 .

[31]  Srinivas Talluri,et al.  Supplier Evaluation and Rationalization via Data Envelopment Analysis: An Empirical Examination , 2001 .

[32]  Jeffrey A. Clark,et al.  Off-site monitoring systems for predicting bank underperformance: a comparison of neural networks, discriminant analysis, and professional human judgment , 2001, Intell. Syst. Account. Finance Manag..

[33]  Zilla Sinuany-Stern,et al.  Academic departments efficiency via DEA , 1994, Comput. Oper. Res..

[34]  D. Galagedera,et al.  Australian mutual fund performance appraisal using data envelopment analysis , 2002 .

[35]  Marjorie B. Platt,et al.  Probabilistic Neural Networks in Bankruptcy Prediction , 1999 .

[36]  Daniel Santín,et al.  The measurement of technical efficiency: a neural network approach , 2004 .

[37]  An-Sing Chen,et al.  Application of Neural Networks to an Emerging Financial Market: Forecasting and Trading the Taiwan Stock Index , 2001, Comput. Oper. Res..

[38]  H. Sherman.  Hospital Efficiency Measurement and Evaluation: Empirical Test of a New Technique , 1984, Medical care.

[39]  Randy I. Anderson,et al.  The efficiency of franchising in the residential real estate brokerage market , 1998 .

[40]  R. Raab,et al.  Identifying Subareas that Comprise a Greater Metropolitan Area: The Criterion of County Relative Efficiency , 2002 .

[41]  Jatikumar K. Sengupta,et al.  Dynamics of Data Envelopment Analysis: Theory of Systems Efficiency , 2010 .

[42]  K. Field,et al.  Measuring the efficiency of British universities: a multi‐period data envelopment analysis , 2004 .

[43]  Carlos Pestana Barros,et al.  Measuring performance in defense‐sector companies in a small NATO member country , 2004 .

[44]  Cecilio Mar-Molinero,et al.  Measuring DEA efficiency in Internet companies , 2005, Decis. Support Syst..

[45]  María Jesús Mancebón,et al.  Performance in primary schools , 2000, J. Oper. Res. Soc..

[46]  M. N. Vrahatisa,et al.  Studying the performance of artificial neural networks on problems related to cryptography , 2005 .

[47]  J. Cummins,et al.  Evidence From the Spanish Insurance Industry By , 2001 .

[48]  K. P. Pandey,et al.  Improving energy productivity in paddy production through benchmarking—An application of data envelopment analysis , 2006 .

[49]  Hian Chye Koh,et al.  A neural network approach to the prediction of going concern status , 1999 .

[50]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[51]  Murugan Anandarajan,et al.  Bankruptcy prediction of financially stressed firms: an examination of the predictive accuracy of artificial neural networks , 2001, Intell. Syst. Account. Finance Manag..

[52]  Geeta Krishnasamy,et al.  Malaysian post merger banks’ productivity: application of Malmquist productivity index , 2004 .

[53]  Donald A. Singer,et al.  Use of a Probabilistic Neural Network to Reduce Costs of Selecting Construction Rock , 2003 .

[54]  Jin-Li Hu,et al.  Technical Efficiencies in Large Hospitals: A Managerial Perspective , 2004 .

[55]  C. M. Tam,et al.  Selection of vertical formwork system by probabilistic neural networks models , 2005 .

[56]  David Enke,et al.  The use of data mining and neural networks for forecasting stock market returns , 2005, Expert Syst. Appl..

[57]  M Hajmeer,et al.  A probabilistic neural network approach for modeling and classification of bacterial growth/no-growth data. , 2002, Journal of microbiological methods.

[58]  Wen-Min Lu,et al.  Evaluating the Performance of the Taiwanese Hotel Industry using a Weight slacks-Based Measure , 2005, Asia Pac. J. Oper. Res..

[59]  Thomas G. Calderon,et al.  A roadmap for future neural networks research in auditing and risk assessment , 2002, Int. J. Account. Inf. Syst..

[60]  Joseph Sarkis,et al.  Using data envelopment analysis to evaluate environmentally conscious waste treatment technology , 2001 .

[61]  Peter Smith,et al.  Model misspecification in Data Envelopment Analysis , 1997, Ann. Oper. Res..

[62]  Mary E. Edwards,et al.  Leviathan vs. Lilliputian: A Data Envelopment Analysis of Government Efficiency , 2000 .

[63]  L. Seiford,et al.  Profitability and Marketability of the Top 55 U.S. Commercial Banks , 1999 .

[64]  F. Gubina,et al.  Allocation of the load profiles to consumers using probabilistic neural networks , 2005, IEEE Transactions on Power Systems.

[65]  Marijana Zekic-Susac,et al.  Modelling small-business credit scoring by using logistic regression, neural networks and decision trees , 2005, Intell. Syst. Account. Finance Manag..

[66]  Shouhong Wang,et al.  Adaptive non-parametric efficiency frontier analysis: a neural-network-based model , 2003, Comput. Oper. Res..

[67]  Mahendra Gupta,et al.  Production Efficiency and the Pricing of Audit Services , 2003 .

[68]  Raymond J. Mooney,et al.  Symbolic and neural learning algorithms: An experimental comparison , 1991, Machine Learning.

[69]  B T Fan,et al.  Study of probabilistic neural networks to classify the active compounds in medicinal plants. , 2005, Journal of pharmaceutical and biomedical analysis.

[70]  M. Audibert,et al.  Social and health determinants of the efficiency of cotton farmers in Northern Côte d'Ivoire. , 2003, Social science & medicine.

[71]  Kürşat Demiryürek,et al.  The cost efficiency of trout farms in the Black Sea Region, Turkey , 2006 .

[72]  Y A Ozcan,et al.  A national study of the efficiency of hospitals in urban markets. , 1993, Health services research.

[73]  Desheng Dash Wu,et al.  Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank , 2006, Expert Syst. Appl..

[74]  Emmanuel Thanassoulis,et al.  Negative data in DEA: a directional distance approach applied to bank branches , 2004, J. Oper. Res. Soc..

[75]  Marijana Zekić-Sušac,et al.  Modelling small-business credit scoring by using logistic regression, neural networks and decision trees , 2005, Intell. Syst. Account. Finance Manag..

[76]  Andrew Hunter,et al.  Feature Selection Using Probabilistic Neural Networks , 2000, Neural Computing & Applications.

[77]  Afschin Gandjour,et al.  An Evidence‐Based Evaluation of Quality and Efficiency Indicators , 2002, Quality management in health care.

[78]  Matthias Sutter,et al.  Measuring efficiency of German football teams by Data Envelopment Analysis , 2004 .

[79]  Liam O'Neill,et al.  Multifactor efficiency in Data Envelopment Analysis with an application to urban hospitals , 1998, Health care management science.

[80]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[81]  Robert C. Rickards,et al.  Setting benchmarks and evaluating balanced scorecards with data envelopment analysis , 2003 .

[82]  C. Barros,et al.  Efficiency in European Seaports with DEA: Evidence from Greece and Portugal , 2004 .

[83]  Jesper Munksgaard,et al.  Efficiency gains in Danish district heating. Is there anything to learn from benchmarking , 2005 .

[84]  Shouhong Wang.  The unpredictability of standard back propagation neural networks in classification applications , 1995 .

[85]  Man Gyun Na,et al.  ESTIMATION OF BREAK LOCATION AND SIZE FOR LOSS OF COOLANT ACCIDENTS USING NEURAL NETWORKS , 2004 .

[86]  Matthias Staat,et al.  The Effect of Sample Size on the Mean Efficiency in DEA: Comment , 2001 .

[87]  Ramakrishnan Ramanathan,et al.  Evaluating the comparative performance of countries of the Middle East and North Africa: A DEA application , 2006 .