The present paper has the objective to inform the public regarding the use of new techniques for the modeling, simulate and forecast of system from different field of activity. One of those techniques is Artificial Neural Network, one of the artificial inartificial neural network, economics, simulate, artificial intelligence

Constantin Ilie

Margareta Udrescu

English

Research Papers in Economics

1080 
NEW TECHNIQUES APPLIED IN ECONOMICS. ARTIFICIAL NEURAL NETWORK 
 
Udrescu Margareta 
Ovidius  University  from  Constanta,  economical  Sciences  Faculty,  University  Road,  no.  1, 
udrescu_marga@yahoo.com, +40 721 983139 
Ilie Constantin 
Ovidius  University  from  Constanta,  Mechanical,  Industrial  and  Maritime  Faculty,  Mamaia  Ave.,  no.  124, 
ilie.ctin@yhoo.com, +40 721 205325 
 
The present paper has the objective to inform the public regarding the use of new techniques for the modeling, simulate and 
forecast of system from different field of activity. One of those techniques is Artificial Neural Network, one of the artificial 
intelligence branch, is widely use in several field and the researchers are trying to extend these fields, considering new types of 
networks. Considering the financial crisis the authors concentrate the attention on specific field from the economical domain. 
After a short presentation, several types of ANN are presented and also a few examples are revealed.  
 
Keywords: artificial neural network, economics, simulate, artificial intelligence 
 
C51 
 
1. Introduction 
1.1. Artificial neural network (ANN) 
First researched in 1943, the real use of ANN starts in 1959 with the development of computers and the incipient 
models ADALINE and MADALINE (Multiple ADAptive LINear Elements, Bernard Widrow and Marcian Hoff - 
Stanford). Even with the setbacks determined by not trusting the artificial intelligence, the ANN gained step by step 
their place between the new techniques of simulating and forecast certain problems that involve a rate amount of 
heterogeneous data characterized by the nonlinearity [1].  
The ANN tries to copy the human brain capacities and behavior considering the smallest unit: the neuron (figure 1) 
who is replaced by mathematical relations characterized by different function (figure 2) [1]. 
 
   
 
Figure 1. Human neuron.       Figure 2. Artificial neuron. 
 
Were:  xi – input vectors; 
- wji –  weights vectors (synaptic); 
- wjo = +b – bias (tendencies) – constant weight; 
- Θ = -b – firing value; 
- V – artificial neuron potential; 
- F(v) – activation function. 
 
å å
= =
= - = + =
n
0 i
j j
n
1 i
j j j x w Θ x w b u v
 
The activation function can vary depending on the algorithm used for training. 
The  ANN  consists  in  several  layers  build  from  many  artificial  neurons  connected  by  weights  in  different 
combinations determined by the ANN type and the data considered (figures 3.). Thus the ANN is trained in order to 
learn or to find the relations between the input data and the output data and to use those results for the future sets of 
data in order to simulate or forecasts [1]. 
 1081 
 
 
Figure 3. ANN example. 
 
Whatever the objectives of ANN use is the next phases are always presented: 
a. data analysis – represents the process that organizes the data in conformity with the user specifications based on 
the data characteristics and problem type; 
b. preprocessing of data – consist in the use of several methods that prepare the data in order to be much easily 
used by the ANN in the training process;\ 
c. ANN structure – the structure of ANN is defined: the number hidden neurons and hidden layers are calculated or 
simulated in order to determine the best training; 
d. training process – process in which the ANN determines the internal elements and values of structures like 
weights. 
e. testing – the last phase consist in validating and testing the result of training using new sets of data. 
ANN types are determined by various criteria but the most common clusters are: feedforward ANN, recurrent – 
feedback ANN and cellular ANN. Another important way to divide the ANN’s type is based on the algorithm that 
is used. The most used algorithm is Levenberg- Marquardt’s backpropagation used for the feedforward ANN. 
 
1.2. The economical applications of ANN 
For the last few years the ANN is mostly used in patter recognition and forecasting especially in the fields of 
economics (financial) and medicine, but also in other fields. A better presentation of ANN usage follows: 
- pattern recognition and identification: oil extraction, imaging, identification of fingerprint and car number; 
-  classification  and  appreciation:  medical  diagnostic,  credit  risks,  fruits  classification,  nondestructive  testing, 
product price sensitivity analysis, quality control for stocks exchange; 
- monitoring and control: medical instruments, dynamic processes, chemical manufacturing, bioprocesses control; 
- forecast and prevision: stock exchange dynamics, prevision of holyday preferences, forecast of future business 
requests; 
- sensors and visual analysis: automatic industrial inspection, postal envelope sorting, visual inspection of railway 
or bridge structure. 
Current, ANN demonstrates more and more the superiority of ANN over the classic methods and techniques used 
for forecast and simulation of economical activities. The huge number of data that can be processed and the 
synthesis quality that offer the possibility that ANN trains itself even in the presence of incomplete data or the 
presence of noise. 
In the economical field the ANN are present in activities such as: 
- tendencies of the market; 
- market exchange dynamics; 
- decision making based on the forecast of the clients demand or the market tendencies; 
- price evolution for certain products; 
- the risks regarding the offer of credits and loans; 
- financial forecast; 
- other economical activities with major impact over the company’s activities. 
In the following some examples will be shown. 
 
2. Examples 
Use of ANN in the credit card fraud. 
The ANN was used starting from the early 90’s in order to determine the possible credit card fraud. After the usage 
of the ANN software (Falcon Fraud Manager) the frauds were in significance reducing as shown in figure 4 [1]. 
 1082 
 
 
Figure 4. Card  fraud evolution. 
 
Forecast of the closing stock exchange. 
Using a simple feedforward ANN (NeuroXl – AnalyzerXL LLC) and the backpropagation algorithm the next day 
stock exchange value were provisioned with a error of 3.28 units (points) or 6.21% of the real value. The results of 
the training are presented in figure 5 and the difference between the simulated value and the real value in figure 6 
[1]. 
 
Use of ANN for the sales analysis [2] 
The ANN demonstrates to be useful for the management decision considering the dynamics of market when it is 
necessary to react to the sales reduction. The manger must consider facts and information like prognoses and 
predictions, what-if analyses, calculations and optimizations. The ANN is capable to learn from the past evolution 
of business’ indexes patterns, rules and models, to compare them with the actual situation, to find the proper one 
and to offer to the manager a possible future evolution and even to give him a possible and virtual decision that can 
be implemented. 
ANN can be, in some cases, the ideal tool for a manager trying to solve a business problem. 
 
 
Figure 5. Target vs. Output.      Figure 6. Target vs. Output - Values. 
 
Consumer choice prediction using ANN [3] 
The subject of the prediction was the consumers’ banking choices between electronic banking and non-electronic 
banking. Starting with an analysis obtained through a mail survey sent to 1,960 household in New Zealand the 
ANN was trained using as input data as service quality dimensions, perceived risk factors, user input factors, price 
factors,  service  product  characteristics,  and  individual  factors,  in  addition  to  those  the  demographic  variables 
including age, gender, marital status, ethnic background, educational qualification, employment, income, and area 
of residence are considered were considered. The result of the prevision is shown in table 1. 
 
Table 1: Out-of-Sample Forecast 
  Non-electronic 
banking users 
Electronic 
banking users 
Overall 
(n = 105) 
Correct (%)  96.00  100.00  99.05 
Incorrect (%)  4.00  0.00  0.95 
 
Since neither the consumers’ choices are always binary nor the neural network is limited to the binary choice 
classification  problem,  the  research  on  the  predictive  power  of  the  neural  networks  on  the  multiple  level 
classifications would be an area for further research, particularly on the consumers’ choice prediction 1083 
 
Consumer Loan Classification Using ANN [4] 
The network was used as a “second level” filter, by supplying it with data of loans that already had been approved 
by  the  bank  officers.  To  train  an  ANN  were  presented  400  loan  applications  cases  and  their  corresponding 
repayment history. The ANN thus configured is then used for the evaluation of the risk of 600 loans, where the 
prediction of the ANN is compared with the loan repayment history. The ANN classifies 597 of those loans 
correctly detecting 35 of them (that the bank had granted) as having problems in their repayment schedule. In 
figure 7 and figure 8 are shown the input and output variables and respectively the loan classification by ANN. 
 
 
 
Figure 7. Input and output variables.    Figure 8. Loan classification by ANN. 
 
This research showed that an ANN can be a valid tool to discriminate among loan applications. 
While the purpose was that of providing support to bank officers, the results were so promising that the ANN could 
be used autonomously in cases. By leaving a sufficiently wide band of uncertainty, one can make sure that human 
intervention is required only for borderline cases. In this case, the ANN would provide a first level filter, granting 
or denying most loan applications autonomously. Since the ANN evaluation (once it has been trained) is not 
computationally very expensive, the entire process could be carried out on a computer at the store location where 
the customer is submitting his application, resulting in immediate approval or denial of most cases, with only a 
fraction of the applications having to be referred to the bank officers for more in-depth analysis.   
 
Computation of the daily of bank share [5] 
The goodness and the accuracy of techniques based solely on the neural network estimates of the conditional 
quintile is illustrated throughout the computation of the daily VaR (Value at risk) of a holding consisting of one 
share  of  DEUTSCHE  Bank.  As  explanatory  variables,  we  use  the  daily  closing  prices  of  BASF,  SIEMENS, 
COMMERZBANK and DAX30 traded on the stock exchange of Frankfurt. Beside the fact, the simulation takes 
relatively longer time before delivering the ANN VaR estimate, the proposed method provides better back testing 
results compared to the historical simulation VaR approach. 
 
3. Conclusion 
The examples provided above are just an insignificant number from the real economical applications that ANN are 
implemented in, but offer information about the importance given to new more efficient and cheaper than other 
techniques or software generally, and to ANN importance especially. The effectiveness of implementation and 
accuracy of results are also good reasons for the continuous research and implementation of the ANN in various 
activities and several fields.  
Working  with  ANN  made  as  understand  the  future  possibilities  and  applications,  but  also the  need  of  future 
research in order to find new types of ANN, new training algorithms and even new artificial neurons for a better 
implementation and for new perspectives of use. 
 
4. References 
1. ILIE Constantin – “The use of ANN for the simulation of the industrial systems and processes”, Ph. D. exam 
no.1, Politehnica University from Bucharest, Coordonator – Prof. dr. eng. Neagu Corneliu, April 2004; 
2. Lucica Barbes, Corneliu Neagu, Lucia Melnic, Constantin Ilie, Mirela Velicu –“The Use of Artificial Neural 
Network (ANN) for Prediction of Some Airborne Pollutants Concentration in Urban Areas”, Revista de Chimie, 
volume 60, no. 3, Bucharest, 2009; 
3.  Gan,  C.,V.  Limsombunchai,  M.  Clemes  and  A.  Weng  –  “Consumer  Choice  Prediction:  Artificial  Neural 
Networks versus Logistic Model”, Journal of Social Sciences 1 (4): 211-219, 2005; 1084 
4. Fabrizio De Nittis, Giampietro Tecchiolli, Alessandro Zorat – “Consumer Loan Classification Using Artificial 
Neural Networks”, ICSC EIS’98 conference, Tenerife (Spain) in February 1998; 
5.  MABOUBA  DIAGNE  –  “Financial  Risk  Management  and  Portfolio  Optimization  Using  Artifcial  Neural 
Networks  and  Extreme  Value  Theory”,  Ph.  D.  Thesis,  University  of  Kaiserslautern,  Mathematics 
Department/Financial Mathematics, 10th October 2002, Supervisor: Prof.Dr Juergen Franke; 
6. Neagu Corneliu, Ioni ă Cristian – “Re ele Neuronale. Teorie şi aplica ii în modelarea proceselor şi sistemelor de 
produc ie”. Editura MatrixRom, Bucureşti, 2004; 
7.  Sorin  Zăhan,  Aurelian Maga  –  “Re ele  Neuronale  –  aplica ii  în  telecomunica ii”,  Editura    Alabastră,  Cluj-
Napoca, 1999; 
8. Toderan, Cr., Coşteiu, M., - “Re ele neuronale”, Ed. Microinformatica, Cluj-Napoca, 1994; 
9. T.J. Moody, C.J. Darken – “Fast learning in networks of locally tuned processing units”, Neural Computation, 
vol. 1 1989 www.brainstorm.co.uk; 
10. Zenon WASZCZYSZYN – “Fundamentals Of Artificial Neuronal Networks”, Institute of Computer Methods 
in Civil Engineering. 2000. 
11. Lucica Barbes, Corneliu Neagu, Lucia Melnic, Constantin Ilie, Mirela Velicu – “The Use of Artificial Neural 
Network (ANN) for Prediction of Some Airborne Pollutants Concentration in Urban Areas”, Revista de Chimie, 
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Aurelian Maga – “Re ele Neuronale – aplica ii în telecomunica ii”, Editura Alabastră,

Consumer Choice Prediction: Artificial Neural Networks versus Logistic Model”,

Corneliu Neagu, Lucia Melnic, Constantin Ilie, Mirela Velicu – “The Use of Artificial Neural Network (ANN) for Prediction of Some Airborne Pollutants Concentration in Urban Areas”, Revista de Chimie,

Corneliu Neagu, Lucia Melnic, Constantin Ilie, Mirela Velicu –“The Use of Artificial Neural Network (ANN) for Prediction of Some Airborne Pollutants Concentration in Urban Areas”, Revista de Chimie,

Darken – “Fast learning in networks of locally tuned processing units”,

Financial Risk Management and Portfolio Optimization Using Artifcial Neural Networks and Extreme Value Theory”,

Ioni ă Cristian – “Re ele Neuronale. Teorie şi aplica ii în modelarea proceselor şi sistemelor de produc ie”. Editura MatrixRom,

Nittis, Giampietro Tecchiolli, Alessandro Zorat – “Consumer Loan Classification Using Artificial Neural Networks”,

Re ele neuronale”,

The use of ANN for the simulation of the industrial systems and processes”,

NEW TECHNIQUES APPLIED IN ECONOMICS. ARTIFICIAL NEURAL NETWORK

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NEW TECHNIQUES APPLIED IN ECONOMICS. ARTIFICIAL NEURAL NETWORK

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