Combining data envelopment analysis with neural networks: application to analysis of stock prices

2004 ◽  
Vol 25 (3) ◽  
pp. 589-611 ◽  
Author(s):  
Alexander Vaninsky
Keyword(s):  
Neural Networks ◽  
Data Envelopment Analysis ◽  
Stock Prices ◽  
Data Envelopment ◽  
Combining Data

Sustainability efficiency assessment of the electricity mix of the 28 EU member countries combining data envelopment analysis and optimized projections

Energy Policy ◽  
2019 ◽  
Vol 134 ◽  
pp. 110921 ◽  
Author(s):  
Patricia Zurano-Cervelló ◽  
Carlos Pozo ◽  
Josep María Mateo-Sanz ◽  
Laureano Jiménez ◽  
Gonzalo Guillén-Gosálbez
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Efficiency Assessment ◽  
Combining Data ◽  
Electricity Mix

scholarly journals Road Safety Risk Evaluation Using GIS-Based Data Envelopment Analysis—Artificial Neural Networks Approach

2017 ◽  
Vol 7 (9) ◽  
pp. 886 ◽  
Author(s):  
Syyed Shah ◽  
Tom Brijs ◽  
Naveed Ahmad ◽  
Ali Pirdavani ◽  
Yongjun Shen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Data Envelopment Analysis ◽  
Road Safety ◽  
Risk Evaluation ◽  
Data Envelopment ◽  
Safety Risk ◽  
Artificial Neural

scholarly journals Efficiency Evaluation of State Transport Undertakings of India using DEA-NN approach

2015 ◽  
Vol 10 (5) ◽  
pp. 2189-2198
Author(s):  
Dr. Punita Saxena ◽  
Dr. Amita Kapoor
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Data Envelopment Analysis ◽  
Network Model ◽  
Neural Network Model ◽  
Transport Sector ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Data Set ◽  
Policy Makers

The economy of any nation depends on the structure and functioning of its various sectors. Transport sector is one of the vital sectors for the financial system of any developing country. All other sectors are dependent on it either directly or indirectly. Thus improving the efficiency of this sector has become a major concern for the operators and the policy makers. The present paper presents an amalgamation of the two non-parametric techniques, Data Envelopment Analysis (DEA) and Neural Networks (NNs) to compute the efficiency scores of State Transport Undertakings of India. DEA is used to compute the efficiency scores of 27 DMUs. These scores are used to train a neural network model, namely the BPN model. The algorithm is developed and used for predicting the efficiency scores of other units of the data set. The results obtained are comparable and it has been shown that this approach helps in improving the discriminatory power of DEA.

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