App. Sci. Report.

24 (1), 2019: 50-61

© PSCI Publications

Applied Science Reports

www.pscipub.com/ASR

E-ISSN: 2310-9440 / P-ISSN: 2311-0139

Predictionof Stock Price by Fuzzy Artificial Neural Network

Approach Using Markovand ARIMA Model

Mehdi Shanbedi

Corresponding author Email: hendijan.iau@gmail.com

Abstract :Stock price prediction isone of the ways that can be used to utilize investment opportunities and better allocate

resources. Firstly, by providing the necessary warnings, companies can be made alert to falling stock prices so that they

can take appropriate measures with respect to these warnings, and secondly, investors and creditors can distinct favorable

investment opportunities fromunfavorableonesand invest their resources in the proper cases.One of the most important

ways to increase the accuracy of companies’ stock price prediction is to use new data mining techniques forprediction.

Given this fact, there is growing interest in the theoretical development of free dynamic intelligent systems which are

based on empirical data.The backup vector machine is one of these dynamic systems that transmits the knowledge or law

hidden in the data to the algorithm and network structure by processing empirical data. Considering the above issues, the

main issue of this research is to investigate and compare the predictive power of companies' stock prices using Fuzzy

Neural Network compared to ARMA and ARIMA models. In this regard, in this research, 770 years-companieswere

chosen as the final sample. Of the 770 samples selected, 650 were reported as test data and 120 as test data.To ensure

model validity, the sample was divided into a training sample and a test sample.

Keywords: Stock Price, Fuzzy Artificial Neural Network, MarkovModel, ARIMA Model

Introduction

Stock price prediction is an important issue in the financial world, as it contributes to the development of effective

strategies for stock exchange transactions [1].

Predicting stock prices is an important objective in the financial world, since a reasonably accurate predictionhas the

possibility to yield high financial benefits and hedgeagainst market risks [2-4].

The field of energy production, consumption, and price forecasting has been gaining significanceas a current research

theme in the entire energy sectors. For instance, numerous studies investigated for electricity price forecasting such as

Rafał [5], this review article aims to explain and partition the primarymethods of electricity price forecasting.

It is a practically interesting and challenging topic to predict the trends of a stock price. Fundamental and technical

analyses are the first two methods used to forecast stock prices. Various technical, fundamental, and statistical indicators

have been proposed and used with varying results. It is not an easy job due to its nonlinearity and uncertainty; many trials

using various methods have been proposed, for example, artificial neural networks, fuzzy logic, evolutionary algorithms,

statistic learning, Bayesian belief networks, hidden Markov model, granular computing, fractal geometry, and wavelet

analysis [6].

According to the researchdeveloped in the field of stock price prediction, we can classify the methodsused to solve the

stock market prediction problems totwofold.The first category of related work is econometric models thatconsists classical

econometric models for forecasting.Common methods are the autoregressive method (AR), themoving average model

(MA), the autoregressive movingaverage model (ARMA), and the autoregressive integratedmoving average (ARIMA) [7–

10].

In short-term forecasting models, such as ANN and SVM, provide excellentperformance for one-step forecasting task [11].

Stock price prediction models can be divided into two categories: statistical model and artificial intelligence model. The

former models include ANFIS and ARIMA [12].

In [13], they considered the users’ long-term electricity load scheduling problem andmodel the changes of the price

information and load demand as a Markov decision process.

Based on the neural network forecasting model, an intelligent mining system has been developed. Artificial Neural

Network (ANN) approach could forecast the future trend of stock market and it provides stock information signs, for

taking better investment decision of buying and selling of stocks, by the investors [14].

Ardakani et al. [15] proposed optimal artificial neuralnetworks (ANN) models based on improved particle swarm

optimization for long-term electrical energyconsumption.

Dai et al (2014) presents a new forecast method by the combination of improved back-propagation (BP) neural network

and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by

improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the

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51

prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index

prediction, and it could provide a good reference for the investment in stock market [16].

Qiuand Song (2016) compare two basic types of input variables to predict the direction of the daily stock market index.

The main contribution of this study is the ability to predict the direction of the next day's price of the Japanese stock market

index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the

stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify

the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with

prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is

possible to enhance the performance of the optimized ANN model by selecting input variables appropriately [17].

Guan et al (2018) propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based

Back Propagation(HTBP) Neural Network model. First, we compare each value of the historical training data with the

previous day's value to obtain a fluctuation trend time series (FTTS). On this basis, the FTTS blur into fuzzy time series

(FFTS) based on the fluctuation of the increasing, equality, decreasing amplitude and direction [18].

Marxia et al (2018) analyses the non-linear movement pattern of the most volatile, top three stocks in terms of market

capitalization, listed in the Bombay Stock Exchange (BSE) in India, namely Reliance Industries Limited (RIL), Tata

Consultancy Services (TCS) Limited and HDFC Bank Limited, using the Artificial Neural Network (ANN)for the study

period from 2008 to 2017. The findings of the study would help the investors, to make rational, well informed investment

decisions, to optimize the stock returns by investing in the most valuable stocks [19].

According to the issues above, the main issue of this research is to investigate and compare the power of artificial fuzzy

neural network models, ARIMA and ARMA in order to predict stock prices of companies accepted in Tehran Stock

Exchange.

Materials andMethods

Research hypotheses

Hypothesis 1: Compared to ARIMA model,an artificial fuzzy artificial neural network model has more power to predict

companies’ stock prices.

Hypothesis 2: Compared toARMA model, an artificial fuzzy artificial neural network model has more power to predict

companies’ stock prices.

Hypothesis 3: Compared toARMA model, ARIMA model has more power to predict companies’ stock prices.

Statistical Society: Since the time domain of this research is from the beginning of 2006 to the end of 2012, the statistical

society includes all companies accepted in Tehran Stock Exchange.The systematic elimination sampling method is applied

by considering the following conditions: 1. The information needed to calculate the operational variables of the research is

available to them; 2. At least, theyhave been accepted from 2005, andactive in the stock exchange until the end of the

research period; 3. The end of their fiscal year is March 20th

; 4. They are nota part of financial and investment institutions,

and banks.

Data collection:The statistical data related to the research hypotheses have been extracted from the financial statements of

the companies using the organizational documents and, the data of the variables are have been calculated from the

information obtained based on the research hypotheses.Given the fact that the collected data are extracted from the audited

documents,andthat we use formulas of the scientific communityin order to convert them, it can therefore be claimed that

measurement instrument will be valid.

Considering that to calculate the indices, we usecertain formulas that are consideredas global standard instruments, and

they are specific for measuring these attributes according to the documents in the literature and the theoretical foundations,

the reliability of the measuring instrument can be ensured in this point of view.Therefore, the data needed for this study

will be collected through computer databases and referring to the library of the Securities and Exchange Organization and

usingRahavardNovin software and the website www.rdis.ir belonging to the Securities and Exchange Organization

(management of Islamic Research, Development, and Studies). Moreover, companies’ financial statements including

balance sheet, cash flow statement, and notes accompanying financial statements at the end of each fiscal year (March 20th)

have been used as research tools.

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Variables Studied In the Form of a Conceptual Model

Independent variables Dependent variable

ARMA Model

Autoregressive Moving Average model which is known as the ARMA Model and sometimes referred to as the Box-

Jenkins model, is a model commonly used to measure time series data.For time series data as Xt, the ARMA model is a

tool for studying and maybe predicting the future values of such series. This model includes two sections of Autoregressive

(AR) and Moving Average (MA).Therefore, in literature,the ARMA model is presented as ARMA (p, q)in which p is the

order of the AR model and q is the order of the MA model.

ARIMA Model

ARMA models also have other more general forms such as Autoregressive Conditional Heteroskedasticity(ARCH) and

Autoregressive Integrated Moving Average (ARIMA). If studying multiple time series, we will have ARIMA or VARIMA

modeling.If the time series mentioned has long-term memory, a Fractional ARIMAmodel (in other words, FARIMA) will

be appropriate. If the data have seasonal effects then we will have the Seasonal ARIMA or SARIMA model.

Multivariate Regression

Multivariate regression is the method by which predictive variables are combined. In this method, a multivariate regression

equation is calculated that summarizes the measured values of prediction in a formula. Equation coefficients for each

variable are calculated and determined based on its importance in predicting the criterion variable. The degree of

correlation between predictor variables in the multivariate regression equation and dependent variable is shown by

coefficients [20].The multivariate regression model is as follows:

Yi = α + β1 X1,i + β2 X2,i + … + βnXn,i + εn,I (1)

Where

Yi= theith observation of dependent variable

α=y-intercept (fixed value)

Xn,I= the ith observation for theindependent variableXn(n=1,2,…,n)

Β = Independent variable coefficient

ε = Infraction component

Diagnostic tests in combined data

To determine the model used in combined data, several tests are used as follows:

Chow test

Chow test is used to determine the application of the fixed effect model compared to pooled model. The assumptions of

this test are as follows:

H0: Pooled Model

H1: Fixed Effect Model

The first hypothesis is based on the bound values and its opposite hypothesis is based on non-bound values. The Chow test

statistic based on the sum of squares of bound and non-bound models is as follows:

Fuzzy artificial neural

network model

Stock price prediction ARIMA model

ARMA model

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(2)

This statistic has F distribution with degrees of freedom of N-1 and NT-N-K. If the value of the bound F statistic is lower

than the value of Table F, at the significant level determined, the H0 is rejected and there will be a significant effect on the

sections. Therefore, the fixed effect model is chosen, otherwise, the pooled model will be used [21].

Hausman test

Hausman test is used to determine the use of the fixed effect model against a random effect. Hassman test is based on the

existence or non-existence of a correlation between estimated regression error and independent variables of the model. If

there is such a relationship, the model has a fixed effect, and if this relationship is not present, it has a random effect. The

0H indicates that the independent variables and the estimation error are not related and the H1shows the existence of the

relationship [22].

H0: Random Effect

H1: Fixed Effect

For the Hausma test, Madalashows the estimate of the value of variance q with V (q) and presents the M statistic as

follows:

(3)

The Reliability of Variables in Combined Data

The reliability of the research variables means that the mean and variance of variables over time and covariance of the

variables were fixedduring different years. As a result, the use of these variables in the model does not result in creation of

false regression. Thecombined data unit root tests were established by Coah [23] and Briton [24].Wu [25], Oh [26] and

Frankle and Rose [27]have shown in their researches that the use of common unit root tests such as the Dickey Fuller test

and advanced Dickey Fuller tests have lower statistical power than thebined dataunit root tests. Types of reliability tests in

combined data include Levin (LL), IPS test, Fisher test, and Cointegrated AugmentedDickey Fuller (CADF) test [28].

Discussion

Table (1) shows the descriptive statistics of the research variables during the study period. The total observations were 110

companies (770 years-company) after adjusting the bytes of companies that did not qualify as well as the removal of Perth

data. Descriptive statistics of dependent and independent variables that were measured using the data of 110 companies

(770 years-company) during the test period (2006-2012) include the mean, median, standard deviation, minimum and

maximum, as presented in the table below.

/ 1

/

RRSSURSSNchow

URSSNTNK

2qM

v q

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Table 1. Descriptive statistics of the research variables

Variable Description mean median Standard deviation minimum maximum

Pro

fiting ratio

s

Gross profit to sell X1

0.791 0.138 0.403 0.005 0.524

Net profit to sell X2 0.085 0.115 0.400 0.003 0.522

Profit before interest and taxes to total assets X3 0.053 0.112 0.069 0.026 0.113

Net profit to total assets X4 0.121 0.167 1.112 0.000 0.745

Net profit to total current assets X5 0.259 0.863 0.600 1.010 0.720

Net profit to total fixed assets X6 0.080 0.138 0.403 0.005 0.524

Margin of gross profit X7 0.085 0.115 0.400 0.003 0.522

Net profit to total equity X8 0.080 0.085 0.053 0.000 1.259

Activ

ity ratio

s

Circulating received accounts X9 0.138 0.115 0.112 0.000 0.863

Inventory Circulating X10 0.403 0.400 0.069 0.200 0.600

Circulating accounts payable X11 0.005 0.003 0.026 0.000 1.010

Asset Circulating X12 0.524 0.522 0.113 0.330 0.720

Current assets Circulating X13 12.822 12.659 1.342 9.536 19.618

Fixed asset turnover X14 0.133 0.105 0.107 0.000 0.726

Deb

t ratios

Current ratio X15 0.078 0.079 0.042 0.001 0.152

Cash ratio from operating activities to current debt X16 0.138 0.097 0.136 0.002 0.777

Debt ratio to total assets X17 0.389 0.400 0.138 0.200 0.600

The ratio of debt to tangible fixed assets X18 0.004 0.003 0.003 0.000 0.018

Debt ratio to equity market value X19 0.18- 0.095979 0.080784 0.121724 0.335117

Equity ratio to debt X20 2556.503 1598.124 1450.000 1103.056 0.725

Interest cost coverage ratio X21 0.60 0.374599 0.4 0.155391 0.224217

Gro

wth

ratios

Asset growth rate X22 0.54 0.514711 0.6025 0.340388 0.2680

Sales growth rate X23 0.84 0.55049 0.555621 0.180659 0.029851

Stru

ctural ratio

s Current asset ratio to total assets X24 0.25 0.136022 0.118604 0.107449 1.832150

The ratio of fixed assets to total assets X25 13.07 13.17033 13.03148 1.400664 0.788769

Equity to fixed assets X26 6.00 7.600146 7.682836 2.300908 0.02365

Current debt ratio to total debt X27 0.18 0.095979 0.080784 1217240 0.335117

Ratio

s of each

share

Earnings per share X28 2556.503 1598.124 1450.000 1103.056 0.725

Net Assets per share X29 0.60 0.374599 0.4 0.155391 0.224217

Cash from operational activities of each share X30 0.453 0.332 1.403 0.232 0.679

Determining The Appropriate Model For Estimating The Regression Model

Combined data have been used according to the literature available as well as the nature of research hypotheses in this

study. In order to determine the appropriate model (integrated or panel with fixed or random effect), Chow and Hausman

tests have been used to test the hypotheses.

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a) Chow test

The results of the test for the regression model of the present research are shown in Table 2. Regarding the regression

model of the present research, the results of the Chow test show that H0 (integrated model) is not confirmed. In other

words, there are individual or group effects and panel data method should be used to determine the regression model of the

research;Hausman test is used to determine the type of panel model (with random or fixed effects). Table 2. Chow test

Description F test F statistics probability Test result

Regression model of research value 83.021** 0.0000 Panel model

Significance at 95% level**

b) Hausman test

After determining that the y-intercept is not the same for different years, the method used to estimate the model (fixed or

random effects) should be determined; Hausman test is used for this.Hausman test examines H0which is based on the

consistency of random effects estimations against H1 which is based onthe inconsistency of random effects estimations.

Table 3.Hausman test

Description Hausman test statistics probability Test result

Regression model of

research value 47.091** 0.0086 Panel with fixed

effects

Significance at 95% level**

The results of the Hausman test for the research regression model are shown in the table above. The results showed that X2

statistic in the Hausman test is 47.091 which is significant at 95% confidence level, confirmingH1. Therefore, according to

Hausman test, fitting of the regression model of this research will be appropriate using the panel data model with a fixed

effect method.

Classic Regression Hypothesis Test

Before regression models fitting, it is necessary to test linear regression assumptions first.

Research Variables Distribution Normality Test

The Kolmogorov-Smirnov test has been used to test the dependent variables distribution normality. This test is performed

for the dependent variable (stock price). The output table of the K-S test in SPSS software for this variable is as follows: Table 4. Kolmogorov-Smirnov test

Variable name Z Kolmogorov-Smirnov Significance level Result

stock price 1.328 0.124 Distribution is normal

Given the above table and the Z statistic of Kolmogorov-Smirnov, since the significance level for all variables is greater

than 0.05, H0 is confirmed, so with an confidence level of 95%, it can be said that the variables have normal distribution.

2

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Reviewing The Errors Distribution Normality

One of the regression assumptions is that the errors of the equation have a normal distribution with a zero mean. In order to

check the normality of equation errors, the error components curve is drawn in the regression model. As shown in Diagram

1, in the regression model of the research, the mean error distribution is almost zero, and its standard deviation is close to 1

(0.994), hence the distribution of errors in the regression model is normal.

Diagram 1. Errors components curve - Model (1)

Fuzzy Artificial Neural Network Model

In this research, artificial fuzzy neural networkhas been used to predict stock prices. For this purpose, MATLAB software

has been used to design a fuzzy artificial neural network. It should be noted that the same data used in logistic linear

regression were used for the neural network. The total data under study consist of information such as profitability ratios,

activity ratios, share ratios, etc.of 110 (770 years) companies from the beginning of 2006 to the end of 2012.Data samples

have been divided into two experimental and training groups. The training set which is used to build the model includes

650 years-company from2006 to 2011.The experimental set consisting of 120 companies in 2012, is used to examine the

model’s validity and generalizability. Training data are needed for designing the artificial fuzzy artificial neural network.

The number of times that the training phase is repeated is considered to be 1000 as default.

The Results Of ARIMA Model Fitting

After testing the assumptions of regression and ensuring their existence, the results of fitting of the regression equation

above are presented in Table 5. The amount of chi-square statistic (8.654) also indicates the significance of the whole

regression model. As shown in the bottom of Table 5, the coefficient of determination and the adjusted coefficient of

determination of the above model are 76.8% and 70.2%, respectively.Therefore, it can be concluded that in the regression

equation above, only about 70.2% of the stock price of the companies investigated areexplained by independent and

control variables. In this table, the positive (negative) numbers in the column of the coefficient valuerepresent the the direct

(reverse) effect of each variable on the stock price of the companies under study.

Judgment method: If the amount of sig calculated by the software is considered to be less than the confidence level (in this

research is 5%), the significance of the intended variable is confirmed. Also, with regard to the value of the parent statistic,

if it is greater than its equivalent in the student parent index with the same level of confidence (5%), then the variable will

3 2 1 0 -1 -2 -3 Fre

qu

en

cy

40

30

20

10

0

Dependent Variable: Y

Mean =-2.05E-12 Std. Dev. =0.994

Regression Standardized Residual

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be confirmed. The results of the significance of the coefficients of the variables indicate whether the variable is useful in

predicting stock prices. Table 5. The results of regression equation fitting

Variable name Variable coefficient value of coefficient statisticst The significance level

constant number α0 1.561 3.154 0.002

Gross profit to sell X1

α1 1.421 2.381 0.018

Net profit to sell X2 α2 1.527 2.619 0.009

Profit before interest and taxes to total assets X3 α3 0.376- 2.002- 0.046

Net profit to total assets X4 α4 0.651- 0.223- 0.823

Net profit to total current assets X5 α5 0.452 2.451 0.015

Net profit to total fixed assets X6 α6 0.781 2.479 0.014

Margin of gross profit X7 α7 1.034 5.073 0.000

Net profit to equity X8 α8 1.081 3.838 0.000

Circulating the received accounts X9 α9 0.631 2.388 0.002

Inventory Circulating X10 α10 0.753- 2.141- 0.003

Circulating accounts payable X11 α11 2.894- 2.601- 0.0037

Asset Circulating X12 α12 0.711 0.671 0.541

Current assets Circulating X13 α13 0.967 0.576 0.121

Fixed asset Circulating X14 α14 0.892 2.311 0.034

Current ratio X15 α15 0.453 3.073 0.0067

Cash ratio from operating activities to current debt X16 α16 1.235 2.673 0.027

Debt ratio to total assets X17 α17 0.947 2.388 0.035

The ratio of debt to tangible fixed assets X18 α18 1.263- 2.141- 0.043

Debt ratio to equity market value X19 α19 2.034 1.052 0.058

Equity ratio to debt X20 α20 3.467 3.838 0.069

Interest cost coverage ratio X21 α21 1.091 0.897 0.321

Asset growth rate X22 α22 0.818- 0.458- 0.281

Sales growth rate X23 α23 1.501 2.987 0.0037

Current asset ratio to total assets X24 α24 0.811- 2.847- 0.014

The ratio of fixed assets to total assets X25 α25 3.641 2.873 0.004

Equity to fixed assets X26 α26 2.589 5.073 0.000

Current debt ratio to total debt X27 α27 3.098 3.838 0.000

Earnings per share X28 α28 1.098 2.388 0.002

Net Assets per share X29 α29 3.215- 2.141- 0.003

Cash from operational activities of each share X30 α30 1.982- 2.762 0.001

The coefficient of determination 0.768 Chi-square statistics 8.654

Modified coefficient of determination 0.702

significance(P-Value) 0.017

Durbin-Watson statistics 1.942

The Results Of The Neural Network

Mean Squared Error (MSE) diagram

As shown in the diagram below, in the 1000-time repeat, which is selected by the system as a default, the mean squared

error (MSE) in the training and experimental data is 10 -20 and 10 -2, respectively; these results are appropriate and

desirable.

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Diagram 2. MSE Changes

Checking Data Prediction Error

As shown in Diagram 3, the errors created by the stock price prediction in the statistical society are minimal and negligible,

which is indicative of the high predictive power of the fuzzy neural network designed.

Diagram 3. Data prediction error

Investigating and comparing the predictive power of fuzzy neural network models, ARIMA and ARMA models

Table 6 shows the ability of the three models (Fuzzy Neural Network, ARIMA and ARIMA Models) to predict companies’

stock price. The first row in each group shows the correct number of predictions from 60 actual items. In order to compare

the ability (overall accuracy) of the three models in the prediction of companies’ stock prices, the societies’ mean

comparison (F statistic) has been used.The results of this test are presented in the table below. In the F test, H0 and H1 are

as follows:

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Table 6. Comparison of the prediction power of different models

group name ARIMA model Fuzzy Neural Network ARMA model

The overall accuracy of

the model

100 112 98

85.00% 93.33% 96.66%

H0: The means of prediction accuracy of fuzzy neural network, ARIMA and ARMA modelshave no significant difference

with each other.

H1: The means of prediction accuracy of fuzzy neural network, ARIMA and ARMA models have significant difference

with each other. Table 7. Accuracy mean of predicting the models

Method ARIMA model Fuzzy Neural

Network ARMA model

Mean of overall accuracy

85.00% 93.33% 81.66%

The statistics F 9.451

Significance (P-Value) 0.0021

According to the table, the results of the F test for the comparison of prediction accuracy meansin the three models are

presented. These results show that at the confidence level of 95%, the prediction accuracy meansin the three models are

significantly different,because the value of the F statistic in this test (9.4516) is greater than the minimum acceptable value

for the 95% confidence level. As a result, at an acceptable error level of 5%, the statistical assumption of significance of

the difference between the prediction accuracy means in the three models is not rejected, and H1 indicating that the

prediction accuracy meansin the three models have a significant difference with each other isconfirmed.

Hypotheses Test

Hypothesis 1

Compared to ARIMA model, the artificial fuzzy artificial neural network model has more powerto predict company stock

prices.

Test results:The results of the F test are presented to compare the prediction accuracy mean of the three models, showing

that at the confidence level of 95%, the prediction accuracy means of the three models are significantly different,

therefore,since the accuracy of the artificial fuzzy artificial neural network model (93.33%) is more than ARIMA model

(85.00%), the first hypothesis of the research suggesting that the fuzzy artificial neural network model has more power

than ARIMA model in the prediction ofcompanies’ stock price is confirmed.

Hypothesis 2

Compared to ARMA model, the artificial fuzzy artificial neural network model has more power to predict company stock

prices.

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Test results:The results of the F test are presented to compare the prediction accuracy mean of the three models, showing

that at the confidence level of 95%, the prediction accuracy means of the three models are significantly different, therefore,

since the accuracy of the artificial fuzzy artificial neural network model (93.33%) is more than ARMA model (81.66%),

the secondhypothesis of the research suggesting that the fuzzy artificial neural network model has more power than ARMA

model in the prediction of companies’ stock price is confirmed.

Hypothesis 3

Compared to ARMA model, ARIMA model has more power to predict company stock prices.

Test results:The results of the F test are presented to compare the prediction accuracy mean of the three models, showing

that at the confidence level of 95%, the prediction accuracy means of the three models are significantly different, therefore,

since the accuracy of ARIMA model (85.00%) is more than ARMA model (81.66%), the thirdhypothesis of the research

suggesting that ARIMA model has more power than ARMA model in the prediction of companies’ stock price is

confirmed.

Conclusion

Hypothesis 1

Compared to ARIMA model, the artificial fuzzy artificial neural network model has more power to predict company stock

prices.

Test results:The results of the F test are presented to compare the prediction accuracy mean of the three models, showing

that at the confidence level of 95%, the prediction accuracy means of the three models are significantly different, therefore,

since the accuracy of the artificial fuzzy artificial neural network model (93.33%) is more than ARIMA model (85.00%),

the first hypothesis of the research suggesting that the fuzzy artificial neural network model has more power than ARIMA

model in the prediction of companies’ stock price is confirmed.

Hypothesis 2

Compared to ARMA model, the artificial fuzzy artificial neural network model has more power to predict company stock

prices.

Test results:The results of the F test are presented to compare the prediction accuracy mean of the three models, showing

that at the confidence level of 95%, the prediction accuracy means of the three models are significantly different, therefore,

since the accuracy of the artificial fuzzy artificial neural network model (93.33%) is more than ARMA model (81.66%),

the second hypothesis of the research suggesting that the fuzzy artificial neural network model has more power than

ARMA model in the prediction of companies’ stock price is confirmed.

Hypothesis 3

Compared to ARMA model, ARIMA model has more power to predict company stock prices.

Test results:The results of the F test are presented to compare the prediction accuracy mean of the three models, showing

that at the confidence level of 95%, the prediction accuracy means of the three models are significantly different, therefore,

since the accuracy of ARIMA model (85.00%) is more than ARMA model (81.66%), the third hypothesis of the research

suggesting that ARIMA model has more power than ARMA model in the prediction of companies’ stock price is

confirmed.

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