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In general, the functioning of an neural network can be described as follows: After input layer has received input data from external environment, those information are forwarded to hidden layer neurons, processed, and sent to neurons of output layer. Information then flow backward through the network and the values of connection weights between neurons are adjusted according to desired output. Network continues that process in number of iterations that is necessary to achieve the output closest to desired output. Finally, network output is presented to the user. Neural network learning is basically the process by which the system arrives at the values of connection weights between neurons. Connection weight is the strength of connection between two neurons. If, for example, neuron j is connected to neuron i, wji denotes connection weight from neuron j to neuron i (wij is the weight of reverse connection from neuron i to neuron j). If neuron i is connected to neurons called 1,2,...,n, weights are stored in variables w1i, w2i, ..., wni. A neuron receives as many inputs as the number of neurons with which it is connected, and produces a single output to other neurons each time. The process of neural network design consists of: arranging neurons in various layers, determine the type of connections among neurons (inter-layer and intra-layer connections), determine the way neuron receives input and produces output, determine the learning rule for adjusting the connection weights. According to criteria mentioned above, there are various architectures of neural networks:. two-layered networks: consists of only two active layers, (Types of those architectures are: Perceptron, ADALINE and MADALINE, Kohonen’s self-organizing network, Hopfield’s network, Brain-state-in-a-box network, Instar-outstar networks.) multi-layered networks: consists of three or more active layers. (Types are: Backpropagation network, Counterpropagation network, Recurrent Backpropagation network, Hopfield’s network, ART networks (ART1, ART2, ART3)). Brief overview of existing architectures is presented in Table 1. Table 1. Neural Network Architectures Characteristicstype of connectionlearningArchitecture Nameinter-layerintra-layertypeequationTwo-layered:Perceptronfully connected-supervised EMBED Equation.2 ADALINE / MADALINEfully connected-supervised EMBED Equation.2  min!Kohonen'sfully connectedon-center/ off-surroundunsupervised EMBED Equation.2 Hopfield'sfully connectedrecurrent cross-barunsupervised EMBED Equation.2  min!Multi-layered:Backpropagationhierarchicalrecurrentsupervised EMBED Equation.2 Counter- propagationfully connected non-hierarchicalrecurrent cross-barsupervised EMBED Equation.2  between 1. and 2. layerRecurrent Backpropagationfully connectedrecurrent cross-barsupervised EMBED Equation.2  where  EMBED Equation.2  EMBED Equation.2 ART networksresonance fully connectedon-center/ off-surroundunsuperviseddescribed in Carpenter, 1987 (in Zahedi, F., 1993) 3. Neural Networks in Finance and Investing Neural network method has its large application in several problem areas of finance and investing. Most known are: business failure prediction (bankruptcy prediction), debt risk assessment (bond rating, mortgage underwriting judgements, loan approval assessment), security market applications (stock returns prediction, stock price performance prediction, commodity trading models, etc.), and financial forecasting (time series prediction). There are lots of successful integrations of expert systems and neural networks in financial decision support (Trippi, Turban, 1993, p. 85-102). One example of neural networks effectiveness in finance is statistical-based hybrid neural network forecaster at Chase Manhattan Bank, which is "one of the largest and most successful artificial intelligence applications in the United States" (Marose, 1990). System reduces loses on loans made to public and private corporations by assisting senior loan officers in forecasting the creditworthiness of corporate loan candidates. It also integrates expert systems because of their possibility to explain results and the reasons for certain evaluation. As the regression analysis is the most popular of all quantitative methods used in business and finance (Marquey, Hill, Worthley, Remus, 1991), it was meaningful to compare this method with neural networks applied in this area. Comparative analysis of neural network method with statistical multiple regression method applied on bond ratings (Dutta, Shekar, 1988), shows that neural networks gives higher percent of correctness of prediction (82.4%) than multiple regression (64.7%). Total square error is much less in neural networks than in regression. Difference between those two methods is that regression gives us parameters of a given functional form, but not the correct functional form. Neural network model helps in determining the functional form and parameters. The authors used two layer and three layer backpropagation algorithm and observed that during the learning phase the total squared error decreases for a network with a larger number of layers, but difference in result is not significant. Marquey, Hill, Worthley and Remus (1991) showed that neural network model provide a high degree of robustness and fault tolerance. It has the possibility to find the right transformations for variables and detect weak relationships. The authors compared neural network with regression through three functional forms commonly encountered in regression analysis: linear model, logarithmic model and reciprocal model. The models were evaluated in terms of their forecasting accuracy using a test sample of 100. It was tested how regression and neural networks estimate the true model. The authors used the mean absolute percentage error (MAPE) as the measure of performance. Backpropagation algorithm was used for neural network. The results for linear model show that the neural networks fit true models well. Maximum difference in MAPE was 2% and median difference was 0.62%. Similar results were obtained for logarithmic and reciprocal models. The linear model was estimated best, and the difference was the largest in reciprocal model. The authors concluded that neural network models fit true models when the relationships is not sufficiently defined. Results of an empirical test made by Sharda and Patil (1992) show that neural networks may be used in time series forecasting. They used a backpropagation architecture of neural network and compared it with classical Box-Jenkins forecasting models. Data consisted of 75 series and the methods were evaluated by the mean absolute percent error (MAPE) and median absolute percent error (Me-APE). The mean of the MAPE’s was less for a neural network model than for Box-Jenkins model (for 50 series MAPE was less for neural network and for 22 series for Box-Jenkins). The closeness of errors between those two methods emphasises the need for more investigating of neural networks. The experiences with neural networks in forecasting are not all positive. It depends on specific problem domain. Fishwick (in Sharda, Patil, 1992) reports that the forecasting ability of neural networks was inferior to simple regression methods in some models. It can be concluded from previous research that various methods and architectures has been used in experiments of neural networks applications in finance and investing. Backpropagation algorithm, recurrent backpropagation and Hopfield’s network are most frequently used. Inspite of numerous applications, there is great research potential in neural network applications in this area. Rapid hardware and software development made possible to test various methods and architectures. 4. Neural network system for profit prediction Insufficient use and great potential of neural networks in investing open the possibility of researching in that area. Some tests were made comparing one selected architecture of neural network with selected traditional statistical method. But, in our research we tried to explore and test the efficiency of several different architectures of neural networks methods in profit prediction. Model of profit prediction problem, methodology used and research results are presented below. 4.1. Model of profit prediction problem Profit prediction is observed as simple time series prediction. Time series can be defined as sequences of values taken in equal intervals. Neural network usage in this area is very frequent. However, they need not to be used as a replacement for standard prognostic methods, but as their addition. Their usage is valuable in cases of: 1) difficulty to define exact forecasting model, 2) great amount of uncertain data and presence of chaotic components. The simplest structure of such network is in the form of black box with one or more input neurons and one output neuron. The network is expected to predict one point ahead in a single time series. The sample for training phase consists of current point, number of historical points and the following point. In our experiment, we tried to predict week profit of firm based on previous profit values. Model of data can be presented in following form: InputsOutput (Predicted profit)p1 p2 ... pmpm+1p2 p3 ... pm+1pm+2......pn-m pn-(m-1) ... pn-1 pn where: pi - profit in point (week) i, i=1,..,n m - number of input nodes in each training set n - number of last point (week) in training set Two series of data were used in our model. First seria has 60 points (weeks) (m=9, n=60), and second seria has 110 points (m=9, n=110). Another seria of additional 20 sets are used for testing the network. 4.2. Methodology used in experiment During data preparation it was necessary to analyse the trend influence on data. Since there was no large-scale deterministic components, trend elimination was not needed. Elimination of trend free network to concentrate on finer details. It is also important to decide between keeping or removing the seasonal variations. In some rare cases, it is better to keep seasonal variations because of some large and small values that certain local patter may have. Neural network can solve this problem by setting its bias levels in such a way that different sets of neuron can ignore law or large inputs. 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In case where certain absolute values are important for some actions, it is better to use absolute values in analysis. The neural network backpropagation algorithm is used in the system, with different architectures tested. Backpropagation neural network is multilayer network with delta learning rule. It will minimize the least squared error if two conditions are satisfied (Marquey, Hill, Worthley, Remus, 1991): 1) the model does not trapped in a local minimum or local optimum 2) there are an adequate number of nodes in the hidden layer Network consists of three or more layers, with hierarchical structure, full inter-layer and no intra-layer connection among the neurons. Input in each neuron is computed according to simple summation:  EMBED Equation.2  (1) where outputj is the output from the neuron in the previous layer. Output0 is the bias factor of the third layer neurons, settled to value 1. The weight of the bias factor w0i changes. Output of second and third layers neurons are computed according to sigmoid transfer function:  EMBED Equation.2  (2) where  EMBED Equation.2 , and T is a treshold value. This function produces a continuous value in the 0 to 1 range. The learning equation in the backpropagation network is the delta rule, in the following form:  EMBED Equation.2  (3) where neuron i is located in the hidden layer, and neuron k is in the output layer. Errork is the difference of the actual output of the neuron k from its desired output (errork = desoutputk - outputk) . In the practice, more frequently is in use generalized delta rule in the form of:  EMBED Equation.2  (4) Between hidden and input layer, learning equation is different, since the desired output is not known. In that case the errori is calculated as the weighted sum of all neurons it is connected to on the output layer:  EMBED Equation.2  (5) The error is then used in generalized delta rule for the hidden layer. If the network has more than three layers, the process of learning is repeating according the above equations for each hidden layer. The limitations of network are similar like in Hopfield’s and MADALINE networks; it may lead to local minimum (finding the solution that is the best answer only for a local part of function). That disadvantage can be solved by different starting values for the interconnection weights (Zahedi, F., 1993) The WinNN software was used for testing the network. This software enables the use of backpropagation algorithm with different architectures (desired number of layers and neurons), with several learning rules and learning parameters. After testing the network with numerous methods, best results are given with following networks: Number of layersNumber of neurons (input layer-hidden layer-output layer)Learning parametersNumber of setsRMS test39-5-1(=auto, (=0.5, input noise=0, weight noise=0910.4572139-5-1(=0.2, (=0.5, input noise=0.1, weight noise=0.01910.8015439-9-1(=auto, (=0.5, input noise=0.1, weight noise=0.01510.0215439-9-1(=0.2, (=0.5, input noise=0, weight noise=0510.00827 where ( is learning parameter that improves learning by correcting the weights according to detected error. Automatic change of( ranges is between 0.96 and 1.02 values. ( is momentum. Input noise adds a random input noise to each input node (it makes the network less sensitive to changes in the input values, and can help avoid local minima problem). Weight noise adds random input noise to each connection weight that also helps avoid local minima. Figure 1. presents plot of network and target output functions after training phase of the fourth tested architecture. As can be seen on the figure, these two functions are almost the same. Deviations are expressed by the RMS. Target-I denotes target output function, net-I denotes network output function. Fig. 1. Plot of target and network output functions  EMBED Word.Picture.6  4.4. Evaluation of network After the training phase, we evaluated the network on collected new 20 sets of data for test series using root mean square error (RMS error) as the measure. According to some authors (Masters, T., 1993), RMS error is important measure of neural network performance. RMS error is computed by following equation:  EMBED Equation.2  (6) where ti is target value of prediction and oi is network output value of prediction. According to RMS error calcualting by WinNN software, the best performance is achieved in architecture: 51 sets of data, 9 neurons in input layer, 9 neurons in hidden layer and 1 neuron in output layer, ( learning parameter=0.2, (=0.5, input noise=0, weight noise=0, signum transfer function. RMS error is an effective measure, but has disadvantages that it cannot explain the frequency of misclassification in classification networks. It also does not distinguish between minor and serious errors because it is mean error. Some additional measures closely related to RMS error are: mean absolute error (individual errors are not squared), maximum absoulte error, median error. They are especially suitable for some problems (more in Masters, T., 1993). 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Artificial intelligence with neural networks gives the possibility to improve classical methods with its capability of learning, higher degree of robustness and fault tolerance. The paper is concerned on usage of neural networks in domain of finance and investing. Various authors are compared and their results in neural network application in area of finance and investing are presented. In our research, we tried to test and evaluate several different architectures of backpropagation neural network algorithm on profit prediction problem. Given results shows that the architecture of neural networks that best predicts the future values of profits with minimum root mean square error is three layer network with 9 neurons in input and hidden layer and one neuron in output layer, with learning parameter of 0.2. Future research can concentrate on other evaluating measures and types of networks. Keywords: neural networks, backpropagation algorithm, time series prediction, profit 1. 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Various authors are compared and their results in neural network application in area of finance and investing are presented. In our research, we tried to test and evaluate several different architectures of backpropagation neural network algorithm on profit prediction problem. Given results shows that the architecture of neural networks that best predicts the future values of profits with minimum root mean square error is three layer network with 9 neurons in input and hidden layer and one neuron in output layer, with learning parameter of 0.2. Future research can concentrate on other evaluating measures and types of networks. Keywords: neural networks, backpropagation algorithm, time series prediction, profit 1. Introduction - Advantages 1   Р&џџџџРџЈџ€H & MathTypepћРўTimes New RomanŒ- 2 Р@wе 2 Рewе2 Рм output  Z  Z2 РPerrorzz zћрўTimes New Roman-№ 2 JikQ 2 00newР 2 J<ikQ 2 0NoldQ 2  iQ 2 тkћРўSymbol-№ 2 Рj-Џ 2 РQ=Џ 2 Р^ зU 2 РЪ зUћРўSymbol-№ 2 РGaЪ & џџџџћ М"System-№џџџџџаЯрЁБс;ўџ ўџ џџџџРFMicrosoft Equation 2.0 DS Equation Equation.2аЯрЁБс;ўџ аЯрЁБс;ўџ Йг ‡4и‡3р$‡3 ƒw ƒiƒkƒnƒeƒw of Neural Networks Methods Neural networks are artificial intelligence methods that are modelled according to structure of human brain. Dual role of neural networks; biological and technical, caused two main directions in neural networks research. The focus of this paper is on the technological aspect, which evaluates neural networks according to their practical application. According to Simon's classification of decisions, artificial intelligence methods are appropriate in supporting unstructured, nonprogramming decisions. Areas where neural networks work best are: classifying data, modelling, forecasting and signal processing. Classification capability has its wide usage in areas of targeted marketing, credit approval, stock picking, problem diagnosing, etc. Modelling and forecasting deals with developing mathematical relationships between several continuous input variables and one or more output variables. Neural networks are specially applicable in forecasting chaotic time series (Masters, 1993, p.Oleџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџ џџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџzџџџџPICџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџ (+џџџџџџџџџџџџџџџџџџџџџџџџ{LџџџџMETAџџџџџџџџџџџџџџџџџџџџџџџџџџџџe˜.l3˜..Є.Є.Ќ. џџџџџџџџџџџџt3Ќ3t3и.3}HТCompObjG!Ј'G!р3h3Ќ.3EDFL3щ.Œ/\3#G!с*,џџџџ.4 ‹ZL‚ƒ„…†‡ˆ‰ŠўџџџŒўџџџўџџџўџџџўџџџ“ўџџџ•–—˜™š›œžŸ ЁЂўџџџЄўџџџўџџџЇЈЉўџџџўџџџЌўџџџЎЏАўџџџВўџџџўџџџўџџџўџџџЗўџџџЙКЛМНОПРСТУФХЦЧШЩўџџџЫўџџџўџџџЮЯаўџџџўџџџгўџџџежзийклмнопрстўџџџфўџџџўџџџчшщўџџџўџџџьўџџџюя№ёђѓєѕіїјљњўџџџќўџџџўџџџџ 2 Р@wе 2 Рewе2 РЎ extinputZZ   Z 2 РwећрўTimes New Romanм‘-№ 2 PjiQQ 2 00newР 2 ujiQQ 2 0NoldQ 2 jiQQ 2 0ёoldQћРўSymbol-№ 2 Рj-Џ 2 РQ=Џ 2 Р -ЏћРўSymbol-№ 2 РGaЪћРўTimes New Roman-№ 2 Р- (j 2 РЌ)j & џџџџћ М"System-№аЯрЁБс;ўџ ўџ џџџџРFMicrosoft Equation 2.0 DS Equation Equation.2аЯрЁБс;ўџ аЯрЁБс;ўџ Йг ‡4l'‡3 +‡3 ƒw ƒjƒiƒnƒeƒw †-ƒw ƒjƒiƒoƒlƒd †=„a‚(ƒeƒxƒtƒiƒnƒpƒuƒ8). The advantages of neural networks are in their abilities to analyse incorrect, noisy data, to deal with problems that have no clear cut solution, and to learn.They also have some limitations, like possibility to lead at local minimum and impossibility to explain the calculated output. Integration of neural network with expert systems and other methods of artificial intelligence give the opportunity to resolve those disadvantages. 2. Basic Concept of Neural Networks The term neuron denotes basic unit of neural network model attended for data processing. Neurons are connected into a network in the way that output of each neuron represents input for other neuron. According to its direction, connection between neurons can be either one-directional or bidirectional, and due to its intensity connection can be excitatory or inhibitory. Neurons are grouped in layers. 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Areas where neural networks work best are: classifying data, modelling, forecasting and signal processing. Classification capability has its wide usage in areas of targeted marketing, credit approval, stock picking, problem diagnosing, etc. Modelling and forecasting deals with developing mathematical relationships between several continuous input variables and one or more output variables. Neural networks are specially applicable in forecasting chaotic time series (Masters, 1993, p.ЊіfW ' џџџ.1  €`&џџџџРџЏџ / & MathTypeћРўTimes New Roman- 2 €@rz 2 €RfZ2 €мinputZ   Z2 €! output  Z  Z 2 €Ÿwе 2 €ФrzћрўTimes New Roman_P-№ 2 љщjiQQ 2 №щtQ 2 љˆiQ 2 №ŸtQ 2 љІ jQ 2 №„ tQ 2 …kiQ 2 №oldQ 2 ;kiQ 2 №mtQћРўSymbol-№ 2 €=Џ 2 ‚ЂP 2 €^+ЏћрўSymbol-№ 2 №я -ž 2 №и-žћ€ўSymbol-№ 2 ЉMхћРўTimes New Roman_P-№ 2 €c(j 2 €>)(jj 2 €S)jћрўTimes New Roman-№ 2 №} 1 2 №f1 & џџџџћ М"System-№џџџџџџџџџџџџџџџџаЯрЁБсўџ џџџџРFMicrosoft Equation 2.0 DS Equation Equation.2аЯрЁБс;ўџ аЯрЁБс;ўџ Йгр‡4ф(‡3d)‡3 ƒr ƒjƒiƒt †=2ƒf‚(ƒiƒnƒpƒuƒt ƒiƒt ‚)‚(ƒoƒuƒtƒpƒuƒt ƒjƒt†-ˆ1 †+ƒw ƒkƒiƒoƒlƒd ‚)ƒr$џўџўџ$џўџўџ$џшџџ№џ№џџџ №џ№џ№№џђџшџ№џџ№џџ№џџџ№џ№џ№џ№џ№ђџшџ№џџ№џџ№џџџ№џ№џ№џ№іџшџ№џџ№џџ№џ№џџ№џ№џ№џ№ђџшџ№џџ№џџ№џџџ№џ№џ№џ№џ№ђџшџ№џ№џџ№џџ№џџџ№џ №џ№№џ№ђџшџ №џ№џ№џ№џ№џ№џџ№№№џђџшџ№џ№џ№џ№џўџџшџ№џ№џ№џ№џўџџшџџ№џџ№ўџџўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џ ƒkƒiƒt†-ˆ1 †хP 2 €^+ЏаЯраЯрЁБс;ўџ Lрвј МшшаЯрЁБс;ўџ рв>M Ф џџџ.1  `Р&џџџџРџЗџ€ & MathTypeћРўTimes New Romank-2 `8inputZ   Z 2 `Swе2 `мouOleObjџџи<Д?tи<Д?џџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџ џџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџбџџџџPICnfoџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџ DGџџџџџџџџџџџџџџџџџџџџџџџџвLџџџџMETAon Nativeџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџ џџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџдЈџџџџCompObj8џџџџџџџџџџџџџџџџџџџџџџFHџџџџРFуZџџџџtput  Z  Z 2 `wе2 `Јoutput  Z  ZћрўTimes New Romanе-№ 2 АмiQ 2 АcjiQQ 2 вюjQ 2 !Pn 2 АY jQ 2 А№oiQћРўSymbol-№ 2 `­=Џ 2 `^зU 2 ` +Џ 2 `*зUћрўSymbol-№ 2 вb=žћ€ўSymbol-№ 2 ‰ хћрўTimes New Romank-№ 2 в1 2 Аѓ0 & џџџџћ М"System-№ƒeƒcƒtƒeƒd  аЯрЁБсўџ џџџџРFMicrosoft Equation 2.0 DS Equation Equation.2аЯрЁБс;ўџ аЯрЁБс;ўџ ЙгР‡4ш‡3x ‡3 ƒiƒnƒpƒuƒt ƒi †=ƒw ƒjƒiƒj†=ˆ1ƒn †х †зƒoƒuƒtƒp8). The advantages of neural networks are in their abilities to analyse incorrect, noisy data, to deal with problems that have no clear cut solution, and to learn.They also have some limitations, like possibility to lead at local minimum and impossibility to explain the calculated output. Integration of neural network with expert systems and other methods of artificial intelligence give the opportunity to resolve those disadvantages. 2. 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0NoldQ 2  iQ 2 тkћРўSymbol-№ 2 Рj-Џ 2 РQ=Џ 2 Р^ зU 2 РЪ зUћРўSymbol-№ 2 РGaЪ & џџџџћ М"System-№Romank- 2 Jgain  Z  аЯрЁБс;ўџ ўџ џџџџРFMicrosoft Equation 2.0 DS Equation Equation.2аЯрЁБс;ўџ аЯрЁБс;ўџ Йг ‡4и‡3р$‡3 ƒw ƒiƒkƒnƒeƒw †-ƒw ƒiƒkƒoƒlƒd †=„a†зƒoƒuƒtƒpƒuƒt ƒi †зƒeƒrƒrƒoƒr ƒkаЯр$џўџўџ$џўџўџ$џшџџ№џ№џџџ №џ№џ№№џђџшџ№џџ№џџ№џџџ№џ№џ№џ№џ№ђџшџ№џџ№џџ№џџџ№џ№џ№џ№іџшџ№џџ№џџ№џ№џџ№џ№џ№џ№ђџшџ№џџ№џџ№џџџ№џ№џ№џ№џ№ђџшџ№џ№џџ№џџ№џџџ№џ №џ№№џ№ђџшџ №џ№џ№џ№џ№џ№џџ№№№џђџшџ№џ№џ№џ№џўџџшџ№џ№џ№џ№џўџџшџџ№џџ№ўџџўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џOleўџџџ    ўџџџўџџџўџџџ џџџџџџџџџџџџўџџџ( PICўџџџўџџџ&'ўџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџ `cџџџџџџџџџџџџџџџџџџџџџџџџ)LџџџџMETAџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџ џџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџ+ШџџџџCompObjџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџbdџџџџџџџџџџџџџџџџџџџџџџџџ;ZџџџџаЯрЁБс;ўџ LоС€шшаЯрЁБс;ўџ оСОM Ь џџџ.1  €&џџџџРџЄџР$ & MathTypePћ€ўTimes New Romanь“- 2 р@w 2 ржw2 р outputРРlРРl2 рНinputlРРРl2 р 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іўџџџјїџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџмЅe3Р eŠ^МЬєZ ‚Р‚РР Р ŒРŒРŒР€ФдСjTХTХTХTХ(|ХL€ФЌЫhШХ$ьЦьЦьЦьЦ№Ц№Ц№Ц7Щ9Щ9Щ9ЩQЩсЩqЪЬThЬTЪŒР№Ц05 ьЦ№Ц№Ц№ЦЪ№ЦŒРŒРьЦШХ№Ц№Ц№Ц№ЦŒРьЦŒРьЦ7Щ Р,ЬРJР,BРJŒРŒР№Ц7Щ№ЦG№Ц Neural Networks for Time-Series Predictions in Finance and Investing Marijana Zeki} ( According to research of many authors, almost all problems can be solved more efficient by neural networks than by traditional modelling and statistical methods. Artificial intelligence with neural networks gives the possibility to improve classical methods with its capability of learning, higher degree of robustness and fault tolerance. The paper is concerned on usage of neural networks in domain of finance and investing. Various authors are compared and their results in neural network application in area of finance and investing are presented. In our research, we tried to test and evaluate several different architectures of backpropagation neural network algorithm on profit prediction problem. Given results shows that the architecture of neural networks that best predicts the future values of profits with minimum root mean square error is three layer network with 9 neurons in input and hidden layer and one neuron in output layer, with learning parameter of 0.2. Future research can concentrate on other evaluating measures and types of networks. Keywords: neural networks, backpropagation algorithm, time series prediction, profit 1. Introduction - Advantages of Neural Networks Methods Neural networks are artificial intelligence methods that are modelled according to structure of human brain. Dual role of neural networks; biological and technical, caused two main directions in neural networks research. The focus of this paper is on the technological aspect, which evaluates neural networks according to their practical application. According to Simon's classification of decisions, artificial intelligence methods are appropriate in supporting unstructured, nonprogramming decisions. Areas where neural networks work best are: classifying data, modelling, forecasting and signal processing. Classification capability has its wide usage in areas of targeted marketing, credit approval, stock picking, problem diagnosing, etc. Modelling and forecasting deals with developing mathematical relationships between several continuous input variables and one or more output variables. Neural networks are specially applicable in forecasting chaotic time series (Masters, 1993, p.8). The advantages of neural networks are in their abilities to analyse incorrect, noisy data, to deal with problems that have no clear cut solution, and to learn.They also have some limitations, like possibility to lead at local minimum and impossibility to explain the calculated output. Integration of neural network with expert systems and other methods of artificial intelligence give the opportunity to resolve those disadvantages. 2. Basic Concept of Neural Networks The term neuron denotes basic unit of neural network model attended for data processing. Neurons are connected into a network in the way that output of each neuron represents input for other neuron. According to its direction, connection between neurons can be either one-directional or bidirectional, and due to its intensity connection can be excitatory or inhibitory. Neurons are grouped in layers. There are three main types of layers: input layer (which receives input data from external environment), one or more hidden layers, output ln additional measures. 5. Conclusion It is difficult to make clear decisions about neural network methods after several research experiments. According to many authors, neural networks have large potentials in area of finance and investing. Its possibility to learn on previous data, to deal with uncertainty and chaotic data make those methods of great interest for researchers. Many problems connected with preparing of data sets, normalizing data, trend eliminating and others need to be investigated more detailed. If the model has local minima problem, simulated annealing and genetic algorithms methods are proposed (Masters, T., 1993). In our research, acceptable value of root mean square error is given for an architecture of backpropagation network, so it could be proposed as an effective method for time series predictions. Testing the data on different networks (Kohonen’s, Hopfield’s, etc.), evaluating by additional measures and dealing with above mentioned problems, could improve the application of neural networks in many domains. References: Journal articles: Arinze, B., Selecting Appropriate Forecasting Models Using Rule Induction, Omega, Volume 22., No. 6, 1994, pp. 647-658 Canarelli, P., Analyzing the Past and Managing the Future using Neural Networks, Futures, Vol.27, No. 3, 1995, pp. 325-338 Marose, R., A., A Financial Neural Network Application, AI Expert, May, 1990, pp. 50-53. Moisl, H., Artificial Neural Networks and Natural Language Processing, Encyclopedia of Library and Information Science, Volume 55, Supplement 18, 1995., pp. 1-42 Books, research reports: Badiru, A., B., The Role of Artificial Intelligence and Expert Systems in New Technologies, in Madu, C. N., Management of New Technologies for Global Competitiveness, Quorum Books, Westport, Connecticut, London, 1993., pp. 301-317. Brooks, R., A., Intelligence Without Reason, A.I. Memo, No. 1293, prepared for Computers and Thought, IJCAI-91, Massachusetts Institute of Technology, Artificial Intelligence Laboratory, April 1991. Krіse, B. J.A, Van der Smagt, Patrick, P., An Introduction to Neural Networks, Fourth Edition, University of Amsterdam, Faculty of Mathematics and Computer Science, September, 1991 Marquez, L., Hill, T., Worthley, R., Remus, W., Neural Network Models as an Alternative to Regression, in Neural Networks in Finance and Investing, editors Trippi, R.R., Turban, E., Probus Publishing Company, 1993., pages 435-493 Masters, T., Practical Neural Network Recipes in C++, Academic Press, Inc., 1993 Sharda, R., Patil, R.B., A Connectionist Approach to time Series Prediction: An Empirical Test, in Neural Networks in Finance and Investing, editors Trippi, R.R., Turban, E., Probus Publishing Company, 1993., pages 451-464 Trippi, R.R., Turban, E., Neural Networks in Finance and Investing, Probus Publishing Company, 1993 Zahedi, F., Intelligent Systems for Business, Expert Systems with Neural Networks, Wadsworth Publishing Co., 1993 Paper in a bound collection: Dutta, S. Shekar, S., Bond Rating: A Non-Conservative Application of Neural Networks, Proceedings of the IEEE International Conference on Neural Networks, July, 1988, pp. II443-450. Leao, B. de F., Guazzelli, A., Mendonca, E.A., HYCONES II: a tool to build Hybrid Connectionist Expert Systems, Proceedings of the Eighteenth Annual Symposium on Computer Applications in Medical Care, Journal of the American Medical Informatics Association, Hanley & Belfus, Inc. Philadelphia, Nov. 1994, Symposium Suplement, pp. 742-747. ( Department of Informatics, University of J.J. Strossmayer in Osijek, Faculty of Economics Osijek, Gajev trg 7, 31000 Osijek, E-mail: marijana@oliver.efos.hr Єƒ.ЅШAІŠЇŠЈŠЉŠdeаЯрЁБс;ўџ 1Net1, Net-Input denotes network input values  џџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџGWXZ[\суК о IKcdghjk‘“ŸЁЂЄІжиту<=?BDLNdexyz{ЎЏТУФХ#ќїєэєќшуќшќшќуќуќуниќуќуќуниќуќуќуќунунунќаЬСЗаќЏќЄšЏќЏќuD0"ž5]ceфџKuD0"ž5]acvKuD]cuDŽ!ž5]ceђџKuDŽ!ž5]acvK]cuD]c]ch V]chV]cU]c J*PV]V]U]c]c8#$%&efyz{|ЩЪнопр78KLMNВГЦЧШЩабфхцшћќєътотогЩтотоОДтотоЉŸто—“ˆ~—“тоsiто^uDŸ'ž5]acvKuDт&ž5]ceіџKuDт&ž5]acvKuDd'ž5]ceђџKuDd'ž5]acvK]cuD]cuDŒ%ž5]ceђџKuDŒ%ž5]acvKuDй$ž5]ceђџKuDй$ž5]acvKuD~#ž5]ceрџKuD~#ž5]acvK]cuD]cuDШ"ž5]ceђџKuDШ"ž5]acvK#ќ§ўƒГцч{S/ƒ/R5U5[5\5c5o5r5s5u5x5{5|5ƒ5„5‹5Œ5–5™5›5ž5­5А5Д5Л5Ц5Ъ5Ь5Э5и5й5S=T=g=h=i=j=˜=Ÿ= =Ђ=и=о=п=р=B>C>E>G>А>Б>Ф>Х>Ц>Ч>??ѕэщфщтщфщфщнщнщнщнщнщнщнщнщнщнщнщнщнщнщнщэщвШэщУНнщУНУщУНнщэщВЈэщэuD!я5]ceшџKuD!я5]acvK V]chV]cuDœъ5]ceрџKuDœъ5]acvK]ch]U]c]cuD]cuDŸ'ž5]ceрџKB?(?)?*?+?1?2?ч?ш?ћ?ќ?§?ў?C@D@q@r@@‘@Ч@Ш@т@ч@ш@є@ѕ@џ@AVAWAjAkAlAmAB BcBdBwBxByBzBxFyF€FFЛFМFТFУFGG GGKGќёчпќкќпќЯХпќкќкќПќкќкПкПкПќпќДЊпќПќпќŸ•пќќ‰ќќ‰ќќ‰ќ Jр]c Jц]cuDќѕ5]ceмџKuDќѕ5]acvKuD)є5]ceіџKuD)є5]acvK V]chuDЄё5]ceђџKuDЄё5]acvKV]cuD]cuDпя5]ceьџKuDпя5]acvK]c6KGLGRGSGŠG‹GHH.H/HЛJМJгJдJеJжJ,L-L@LALBLCL—L™LšL›LМLОLСLчLшLДMЕMЮMЯMP'PЧVW1YYY­YўYbZŠZнZоZд[Š\х]ц]щ]ъ]ы]x^y^‡^‰^Š^с^т^љѕяѕљѕљѕяѕцсжЬцѕФѕЙЏФѕЊЄЊѕЊЄѕЊѕљѕяѕсѕЊѕЊѕЂѕЊѕЂѕЂѕЂѕЂ›Ђѕ™Њu]J*P] V]chV]cuDЏž5]ceрџKuDЏž5]acvKuD]cuD‡tŸ5U]cKuD‡tŸ5]acvKU]cuD(^U]c Jр]c]c Jц]c<-GZ[\‰‹Œсту И Й К о п T™мј/}jп6‘§њњp#=њp#%§§p#цѕ§§p#ц§p#ц§p#ц§p#ц§p#ц§p#ц§ p#цѕ§§p#ц§p#ц§p#цѕ§ггp#ѕгp#ѕ§§p#цѕѕp#цББp#ѕ! уџ 4џуЗ! хў 4џЗЊ‘ЯoЉmЎUV˜™ПРСбвгцяооp#ѕМЙоЙЙp#цоЙЙp#цЙp#цЙp#цЙp#цЙp#цЙp#цЕБ–ЕББ8цИlЛ О”џ3p#џџ ! qџ 4џqЗ! уџ 4џуЗя№()6789:;FMWYd|}—п8цлззXцзЅцзЛ цЊЛ цлззXцзЅцзЛ ц}Л цлл%цл%цлXцлЅцлџЛ }џЛ лл%ц,ИlЛ О@”џ3/ aпp# ,ИlЛ О@”џ3/ aпp#џџџџџџ џџ џџ џџ џџ  ИlЛ О(”џ3ap#џџџџ џџ ш]щ]ˆ^‰^Š^______§p#цћљ§p#ц§p#ц§p#ц§p#ц§p#ц§p#цљ K*@ёџ*Normal Љ ]a c"A@ђџЁ"Default Paragraph Font"@ђ" Footnote Textc &@Ђ Footnote ReferencehZ::98$ рsƒЁ  ћс  ] сC Ь] ] ( ]>9&&&KKKqqq444YYY€€€РРРLLL–––тттhhhВВВџџџўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џшџџ№џ№џџџ №џ№џ№№џђџшџ№џџ№џџ№џџџ№џ№џ№џ№џ№ђџшџ№џџ№џџ№џџџ№џ№џ№џ№іџшџ№џџ№џџ№џ№џџ№џ№џ№џ№ђџшџ№џџ№џџ№џџџ№џ№џ№џ№џ№ђџшџ№џ№џџ№џџ№џџџ№џ №џ№№џ№ђџшџ №џ№џ№џ№џ№џ№џџ№№№џђџшџ№џ№џ№џ№џўџџшџ№џ№џ№џ№џўџџшџџ№џџ№ўџџўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џўџўџ$џpџ№џ№џ№џ№џџџџ №№џ№џџџ№џ№џџ№џџџ№vџpџ№џ№џ№џ№џ№џџ№џџ№џ№џ№џџ№џ№џ№џџ№џџ№џ№vџpџ№џ№џ№џ№џџџ џ№џџ№џ№џ№џџџ№џ№џџџџ№vџpџ№џ№џ№џ№џџџ №џ№џџџ№џ№џџџ№џ№џџ№џџ№vџpџ№џ№џ№џ№џ№џџ№џџ№џ№џ №№џџџ№џ№џџ№џџ№џ№vџpџ№џџ№џ№џ№џ№џџџ№џџџ№џџ№џ №џ№vџpџ№џџ№џ№џ№џ№џџџ№џџџ№џџ№џ№џ№vџpџ№џџџ№џ№№џ№џџ№џџ№џџ№џџ№џџ№џ№џ№vџpџ№џџ№џ№џ№џ№џџџџ№џџџџ№џџ№џ №№vџџџ№џ№џџ№џџ№џџџ№zџhџ&џџ№џ№џџ№џџ№џџџ№zџfџ№џ$џџ№џ№џџ№џџ№џџџ№zџfџ№џ$џџ№џ№џџ№џџ№џџџ№zџfџ№џ$џџ№џ№џџ№џџ№џџџ№zџfџ№џџ№ў6zџfџ№џџ№џџ№џ№џџ№џџ№џџџ№zџfџ№џџ№џџ№џ№џџ№џџ№џџџ№zџfџ№џџ№џџ№џ№џџ№џџ№џџџ№zџhџџ№џџ№џ№џџ№џџ№џџџ№zџpџ№џџ№џ№џџ№џџ№џџџ№zџpџ№ўџАџpџ№ўџАџpџ№ўџАџpџ№ўџАџpџ№ўџАџpџ№ўџАџpџ№ўџАџpџ№ўџАџpџ№ўџАџpџ№ўџАџpџ№ўџАџpџ№ўџАџpџ№ўџАџpџ№ўџАџpџ№ўџАџpџ№ўџАџpџ№ўџАџpџ№ўџАџpџ№ObjInfoџџџџeџџџџ=Equation Native џџџџџџџџџџџџ>м_899544572tive_hРF†Кm`ЖxЛ†Кm`ЖxЛМOle55888 џџџџџџџџџџџџРFBCompObjy[]џџџџ РF"ZObjInfoџџџџ^џџџџ$Equation Native џџџџџџџџџџџџ%М_899544105џџџџџџџџaРF†Кm`ЖxЛ†Кm`ЖxЛ_899543460Д?tи<Д?џџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџJ ZРF†Кm`ЖxЛ†Кm`ЖxЛбџџџџOlenfoџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџ џџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџPICon Nativeџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџ Y\џџџџџџџџџџџџџџџџџџџџџџџџLџџџџMETAbj8џџџџџџџџџџџџџџџџџџџџџџ џџџџџџџџџџџџРFџџџџEquation Native џџџџџџџџџџџџўœ_899543007tiveџџџџџџџџSРF†Кm`ЖxЛ†Кm`ЖxЛŽМOle57220tive џџџџџџџџџџџџРFPIC55888 RUџџџџРFLObjInfo9џџџџџџџџџџџџџџџџџџџџ 1Ч1`0Ч1џџџџџџIџџџџРFхџџџџEquation Nativeџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџ џџџџџџџџџџџџџџџџџџџџџџџџџџџџџцмџџџџ_899542817џџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџCQLРF†Кm`ЖxЛ†Кm`ЖxЛeLџџџџOleџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџ џџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџџъџџџџ