stock prices forecast using radial basis function neural network

7/28/2019 Stock Prices Forecast Using Radial Basis Function Neural Network

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(IJCSIS) International Journal of Computer Science and Information Security,Vol.11, No.3, 2013

STOCK PRICES FORECAST USING RADIAL

BASIS FUNCTION NEURAL NETWORK

Julia Fajaryanti

Faculty of Industrial Technology

Gunadarma University

Jalan Margonda Raya 100 Depok - Indonesia

.

Priyo Sarjono Wibowo

Faculty of Industrial Technology

Gunadarma University

Jalan Margonda Raya 100 Depok - Indonesia

Abstract — Neural Network has been implemented in various

applications especially in pattern recognition. This power has

attracted several people to use Neural Network for various systems.

One of the neural network implementation in the field of finance or

investments is forecasting stocks. Assuming that the prediction of

the output system is deterministic, than the suitable Neural Network

model to predict it is Multilayer Network. To get the solution,

Multilayer Neural Network method with supervised algorithm is

applied. The supervised algorithm used for stock price prediction is

Radial Basis Function. This algorithm can supervise the networks

by using previous stock price data, classifying them and putting

weight on the networks. This journal illustrate how Radial Basis

Function Neural Network method can be used to predict stocks.

The result showed that Radial Basis Function Neural Network

method is able to forecast and follow the movement of stock data

used in the experiment.

Keywords: Stock Prices, Multilayer Neural Network, RadialBasis Function, Supervised

I. I NTRODUCTION

The role of capital markets in the Indonesianeconomy began institutionalized. Currently one of the

purchase of shares legitimate capital choices, in addition to

other forms of capital such as money, land, and gold. Rational

factors and various irrational factors be the deciding factor in

purchasing shares. Rational factors commonly associated with

the analysis fundamental. Fundamental analysis does not

consider the pattern of movement of shares in the past but

trying to determine the appropriate value for a stock.

In perfect capital markets and efficient, stock pricesreflect all publicly available information on and information

exchange that can only be obtained from certain groups. High

and low stock prices influenced by many factors such as

conditions and company performance, risk, dividend, interest

rates, conditions economy, government policy, inflation,

supply and demand as well as many more. Because anticipate

possible changes in the factors above, the stock price can rise

or fall. Prediction of the stock price will very useful for

investors to be able to see how the prospects of investing in

the stock of a company come. Stock price prediction can be

used to anticipate the rise and fall of stock prices. With the

prediction of share price, it is very helpful for investors in

decision making. There are two methods that prediction canuse in this implementation, namely: conventional methods and

Artificial Neural Network (ANN). In this journal, authors willimplement Radial Basis Function Neural Network in financial

application to forecast the stock prices.

II. STATE OF THE ART

There are many factors that influence the price of a

stock. Most of these factors are included in the factors related

to the social situation, where it is very difficult to estimate.However, there are things that can be used as a basis for

estimating the price of a stock, one by studying the previous

price of a stock, and then we can estimate the stock price inthe future. This, of course can help decision-makers to the

activities of buying and selling a stock [7]. For that we need a

method to identify and study the movement of the stock price

over time in order to estimate the price of a stock of a

particular company.

It also takes a special device to do it, this is due to the

limitations of human beings in the data processing activities

that are so difficult and numerous in numbers. Thus, we use

the computer to do it. To be able to work on it, the computer requires a learning process. In this process, computers are

faced with a series of data that has been classified and will

study the patterns of the data. Lessons learned include theadjustment to a predetermined pattern, or by studying the

similarity of the pattern. It is accompanied by the development

of computers, the better, in terms of speed, accuracy, and cost

[8].

Neural network is a system that can be relied to do acomplex quick count processing with higher speed compared

to another organs. The use of neural networks requires an

understanding of the way of thinking in the human brain. So

many elements in the human brain, where they are connected

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to each other, working together in order to process

information. An important part of this paradigm is the simple

structure of the information processing system. It consists of alarge number of processing elements (neurons) that are

interconnected and work together in order to solve a particular

problem [9]. As this idea, many researchers start to make the

neural network which is applicable to the computer machine.

According to [3] Neural Networks have shown to

better predictive performance in predicting future stock pricesmovements by analyzing the past sequence of the stocks and

Neural Networks can outperform the conventional statisticalmodels [4,5,6]. Then raised some of artificial neural network

models which has been applied in various areas. An artificial

neural network (ANN), usually called neural network (NN), is

a mathematical model or computational model that tries to

simulate the structure and/or functional aspects of biological

neural networks. It consists of an interconnected group of

artificial neurons and processes information using a

connectionist approach to computation. In most cases an ANN

is an adaptive system that changes its structure based on

external or internal information that flows through the network

during the learning phase. Neural networks are non-linear statistical data modeling tools. They can be used to model

complex relationships between inputs and outputs or to find

patterns in data. Artificial neural network determined by three

things. Determining the appropriate network architecture is the

first step in building a well and according to the needs of

artificial neural network. This following some of the network

architecture is often used in the artificial neural network, suchas single layer network, multi layer network, and recurrent

network.

III. METHODOLOGY

Stock market prediction is a world of financial

forecasting area which attracted much attention from variouscircles, especially the investors. Stock price prediction is very

useful for investors to be able to see how the stock of a

company’s investment in the future. By using the prediction, it

is helpful to investors in making decisions. Expected income

perpetrators in the majority of the equity investment is a

capital gain. This causes the process of forecasting the stock

value is very important in stock investing. An attempt to

determine the stock price should have been done by any

financial analysts with the aim to obtain an attractive rate of return, although quite difficult for investors to "beat the

market" and earn profits above normal levels.

The prediction of the stock price is the complex

interaction between unstable market and unknown random processes factor. The data from stock price can be determined

by time series. If we have daily data from a certain period, for

example : Xt(t = 1,2,...) than the stock price for the next period

(t+1) can be predicted (the timing used can be in hourly, daily,weekly, monthly or yearly). To get the good prediction, the

inputs from several aspects of the share prices have to be input

in Neural Network after that the weighing principal can be

adapted to minimize the wrong prediction in the first future

steps.

Figure 1. Artificial Neural Network to Forecast Stocks [11]

By using the final weighing, an action is done to done to

minimize the total error for the next iteration. Due to that, the

risk of Investors decision to sell or buy the stock can be

minimized. The steps to be performed in a simulated stock

price forecasting using Neural Network:• Collecting data stock prices• Determining the structure of the ANN to be built

• Conduct training and testing of neural network which is builtusing existing

Implementing Radial Basis Function to Forecast theStocks using MATLAB

A. Implementing Radial Basis function to Forecast the

Stocks using MATLAB

Before build a network, then we must determine thespread is to be used. Determination of trial and error spread is

obtained. Process of trial and error will produce coatingweights and biases. Spread that will be used RBF is that

producing the greatest weight value of the bias layer.

Furthermore, the process of building the network. We form a

network that will be included in the RBF with the third layer,

where the first layer (input) consists of one neuron with

activation function based on radial radbas and the second layer

(hidden layer) consists of n neurons with activation function

purelin, and output layer consisting of one neuron and isrepresented in Matlab

net = newrbe(P, T, 5);

Weights are determined by Matlab, with the calculation of each weight has been determined through the function of

tissue formation. To see the value of these weights are used

the following command:

Bobot_Input = net.IW{1,1}

Bobot_Bias_Input = net.b{1,1}

Bobot_Lapisan = net.LW{2,1}

Bobot_Bias_Lapisan = net.b{2,1}

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At the output layer, the layer weights (W{2,1}) and the weight

refraction layer (b{2}) was obtained from the simulation

results in each hidden layer (A) which then solved using alinear function (purelin):

[W{2,1}b{2}*A{1};ones] = T

Input on the output layer (A1) and target (T) is linear,

so that the weight of layers and layers of refraction weightscan be calculated by

Wb = T/[P;ones(1,Q)]

Wb containing layer weight and the weight bias, with the

refraction weight lies in the last column.

B. Simulation and output network on training data

From the results in training that can be simulated with thesame input with data input training. Output simulation results

are stored in a vector

a = sim(net,P);H = [(1:size(P,2))’ T’ a’ (T’-a’)];

sprintf(’%5d %11,2f %11,2f %11,2f\n’, H);

sprintf aims to print variable H´with the format listed above.

Variable H will hold

value:• (1: size (P, 2))’ means the loop will be performed

starting from an initial value to the size of input

training data.

• T’ target of the original data.

• a’ is the output network.

• (T’ - a’) is the error on each training data, that is the

result of a reduction in the output target value

network.network output and the target were analyzed by linear

regression using Postreg :

[m1,c1,r1] = postreg(a,T);

its produce

line gradient reversed (m1): m1 = 0.96005

constants (c1): C1 = 343.07

For the best fit line equation: m1 + c1 = 0.96005+343.07

connection coefficients (r1): r1 = 0.97982If coefficient value (approaching 1), it showed good

results for a match with

the target network output.

C. Simulation and output network on testing data

From the results in training that can be simulated withthe same input with data input training. Output simulation

results are stored in a vector

b = sim(net,Q);

L = [(1:size(Q,2))’ TQ’ b’ (TQ’ - b’)];

sprintf(’%5d %11,2f %11,2f %11,2f\n’, L);

sprintf aims to print variable L´with the format listed above.

Variable L will hold

value:• (1: size (Q, 2))’ means the loop will be performed

starting from an initial value

to the size of input training data.

• TQ’ target of the original data.

• b’ is the output network.

• (TQ’ - b’) is the error on each training data, that isthe result of a reduction

in the output target value network.\network output and the target were analyzed by linear

regression using

Postreg :

[m2,c2,r2] = postreg(b,TQ);

its produce

line gradient reversed (m2): m2 = 0.9423

constants (c2): C2 = 423.98

For the best fit line equation: m2 + c2 = 0.9423+423.98

connection coefficients (r2): r1 = 0.87207if coefficient value (approaching 1), it showed good results for

a match with the target network output.

IV. RESULTS AND DISCUSSION

In this section, authors analyzes the results of output

program. Before analyze the result, authors test the system to

get the result of prediction data. Please note that this test takesa sample of 247 data of stocks, as mentioned before, the data

used in this journal is Telkom share data in the period August

3, 2009 until August 26, 2010. Then authors divide the datainto two parts, the first 187 data means the data from period

August 3, 2009 until May 31, 2010 used for training the data

in system. And for the 60 data are from June 1, 2010 until

August 26, 2010. As shown in the table, for the data training

there is the biggest error that is equal to 3.92% in the data

numbers 125. And for the data testing there is the biggest error

that is equal to 7.5% in the first data. However, overall resultsshow the accuracy of predictive data above 90%.

A. Result of Data Training

To train the data for the system, authors used 187

data. As shown in the table bellow, for the data training there

is the biggest error that is equal to 3.92% in the data numbers125. However, overall results show that the accuracy of

predictive data is above 90%.


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Table 1. Table Result of Sum of Data Training Table 1 (Cont.). Table Result of Sum of Data Training


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Table 1 (Cont.). Table Result of Sum of Data Training Table 1 (Cont.). Table Result of Sum of Data Training


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Table 1 (Cont.). Table Result of Sum of Data Training

Figure 2. Performance of Linear Regression of target and output data training

Figure 3. Comparison of target and output data training

B. Result of Data Testing

To train the data for the system, authors used only 60

data. The usage of data in data testing is less than in data

training, because for train the data system must be learned

more data to produce a more precise accuracy when testing the

data. As shown in the table bellow, for the data testing there is

the biggest error that is equal to 7.5% in the first data.However, overall results show the accuracy of predictive data

above 90%.

Table 2. Table Result of Sum of Data Testing


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Table 2 (Cont). Table Result of Sum of Data Testing

Figure 4. Performance of Linear Regression of target and output data testing

Figure 5. Comparison of target and output data training


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AUTHORS PROFILE

Julia Fajaryanti received the Graduation

in Information Techniques (ST) and Master

(MMSI) degree in Gunadarma University,

Jakarta 2009 and 2010, respectively.

Presently working as Lecturer (Information

Technology) in Gunadarma University,

Jakarta. Interested field of research areArtificial Neural Network, Android

programming, and Image Processing.

Authored many research papers in International / national

journals/conferences in the field of computer.

Priyo Sarjono Wibowo received the Graduation in

Information (S. Kom.) and Master

(MMSI) degree in Gunadarma

University, Jakarta 2003 and 2009,

respectively. Presently working asLecturer (Information Technology) in

Gunadarma University, Jakarta.

Interested field of research are Artificial

Neural Network, Android programming, Java programming, and Visual Basic.


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stock prices forecast using radial basis function neural network

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