Approach based on linear regression for

International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 7, No. 1, January 2016
DOI : 10.5121/ijaia.2016.7103 21
APPROACH BASED ON LINEAR REGRESSION FOR
STOCK EXCHANGE PREDICTION – CASE STUDY OF
PETR4 PETROBRÁS, BRAZIL
Nadson S. Timbó, Sofiane Labidi, Thiago P. do Nascimento, Milson L. Lima,
Gilberto Nunes Neto and Rodrigo C. Matos
Post-graduation Program in Electrical Engineering, Federal University of Maranhão, São
Luís, Brazil 65080–805
ABSTRACT
The stock exchange is an important apparatus for economic growth as it is an opportunity for investors to
acquire equity and, at the same time, provide resources for organizations expansions. On the other hand, a
major concern regarding entering this market is related with the dynamic in which deals are made since
the pricing of shares happens in a smart and oscillatory way. Due to this context, several researchers are
studying techniques in order to predict the stock exchange, maximize profits and reduce risks. Thus, this
study proposes a linear regression model for stock exchange prediction which, combined with financial
indicators, provides support decision-making by investors.
KEYWORDS
Stock Exchange Prediction, Autoregressive Models & Linear Models
1. INTRODUCTION
According to Nascimento [1], the necessity for economic growth is a consensus in any country. A
path for its achievement is through the capital markets, which directs the investments to
alternatives that provides the highest returns. This is defined as a system of transferable securities
distribution capable of providing liquidity to issuance of securities that at the same time makes
the capitalization process viable. It occurs through negotiations between organizations and
investors, up buying and selling shares in the stock exchange. The Bovespa is the main stock
exchange in operation in Brazil. It is considered the largest stock market in Latin America and the
eighth worldwide. In recent years, this stock exchange was the target of huge investments, closing
in 2012, with over one trillion dollars. The main explanation for this is the increase of domestic
markets, the reduction of interest rates and, specially, the currency appreciation, which
encourages new companies and investors to seek, in this market, a new manner of growing
through raising capital and increasing equity [2][3].
While it may be considered a very gainful way of investment, the stock exchange is also based
upon losses, especially when transactions are made without the proper data analysis to support it.
According to Rocha and Macêdo [4], once entered in the market, the goal is to always buy shares
when their prices are the lowest and, consequently, selling at the highest price. This explains why
predicting shifts in the market’s behavior means maximizing profits and reducing risks. Such
anticipation, also called prediction, increase the profitability of investments.

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Currently, there are several techniques used by financial experts that help with the stock market
analysis. Two of them stand out, the Technical Indicators and Fundamental Indicators, that
although are the most widespread, barely predict, with accuracy, the real market’s behaviour, as it
is a complex task and it relies on variables capable of expressing from the investor’s intuition to
natural disasters [1].
This study proposes the application of the autoregressive linear model of stock exchange
prediction using, as parameters, data collection and technical indicators. In addition, in its
conclusion, it is presented a comparative study between this study and other related work.
2. PROBLEM DESCRIPTION AND STATE-OF-THE-ART
The global trend is focused on the economy, aiming that investments be directed towards
alternatives that generate higher profits. The Capital Markets allows such goals with superior
flexibility and reliability in transactions, through the stock exchange.
According to Benjamin Graham [5], investing in the stock exchange requires intelligence. The
investor desiring join the market, must obtain a safety margin on their investments. Obtain such
margin is not a simple task, as a result of market volatility.
There are traditional methods that assists investors making the best decision about how much,
how and when invest. These methods are commonly known as: Technical Analysis and
Fundamental Analysis. The objective of the Fundamental Analysis is to predict the future
behavior of a share, for instance, while taking into account the marketing situation of the
company, that trades these shares or the sector in which this share belongs to [6]. The Technical
Analysis seeks anticipate the market’s comportment or a share, through statistics and probabilities
linked to the manners of the referred market or share. Is worth noting that, both the Fundamental
Analysis and Technical Analysis, even being widely used by financial experts, are sometimes
unable to predict the actual market state, owing to the comportment of the financial environment,
which is dynamic and oscillatory. Pereira, Turrioni and Pamplona [7] argue that traditional
methods of analysis (ROI, IRR, etc) have not evolved with information technology and no longer
are as effective. If there is a demand for the technology, It is more present in the financial aspects
[8][9].
Several studies discuss the predictive methods applied to financial markets. The field of Artificial
Intelligence, for example, triggered the development of specialized computational tools for the
stock market. These tools aim to use market variables to obtain predicted values increasingly
closer to the actual values of these shares [10]. The Regressive Models, the Artificial Neural
Networks, the Fuzzy Logic, Genetic Algorithms and Hybrid Models of prediction are
emphasized.
Marques [11], in his work, used the Genetic Algorithm technique, with the Fuzzy Logic, to find
optimal settings for the MACD indicator performing predictions about future trend of Bovespa
shares. As a result, the aforementioned author, reached a precision rate of 53.75% and 49.5% for
Petr4 and Vale5 shares, respectively.
In another research field, the work developed by Almeida [12] is available, entitled “A Market
Prediction Model Stock Based on Fuzzy Logic”. In this study, the fuzzy control process was
applied to assist the investor making the decision of keeping, buying or selling shares. Such as the
model Almeida [12] used, as input data, was the quotation of Petr4 and Vale5 from years 2007 to
2014. Almeida [12] reported that the results obtained can propose coherent decisions, however, a

23
percentage of success/mistakes was not found, decreasing the credibility the decision-making
supporting.
Nascimento [1], in his work “A System Based on Genetic Algorithms as a Decision Making
Support for the Purchase and Sale of Assets at Bovespa”, used evolutionary techniques known as
Genetic Algorithms to perform predictions on closing prices in shares traded in Bovespa.
Therefore, he used, as parameters, stock market quotations from 01/01/13 to 12/31/13, totalizing
258 samples for data comparison. In this study, 100 days of prior quotations were used to predict
the 101th day. As the best results of this approach, it obtained 55.8% of predictions closest to real
values. It is relevant noting that this study took into account the closest predictions which the
difference to the actual closing price was inferior to 0.50 cents.
Finally, the work of Fagner, entitled “Modeling via artificial neural networks the Capital Market
to the share value forecast and improve steering accuracy rate – Case Study Petr4 – Petrobras,
Brazil” [13], objected the prediction of future trends regarding share prices of Petr4. Among the
evaluated studies, this was the one that succeeded the best results, with a success rate of 91.48%.
Fagner used the Artificial Neural Networks in his research, which consists of non-linear
prediction models and, therefore, considered compelling methods of stock exchange predictions
[13].
3. METHODOLOGY
Meaning prediction model of stock exchange assets, the opted methodology was the process of
Knowledge Discovery in Databases (KDD), which is a AI field that looks for the knowledge
extracting from larger amounts of data. To Fayyad [14] the KDD is a multistep process, non-
trivial, interactive and iterative, for identification of comprehensive, valid, new and potentially
useful standards from large data sets.
By identifying patterns, trends, and finally extracting the desired knowledge, the person
responsible for the process is able to make a more informed and strategic decision about the
transaction, then solving the problem caused by the “Information Age”. [14].
And possessing domain over its application and problem, one can attain knowledge through 5
stages (eg, Figure 1): Data selection, pre-processing, transformation, mining and, finally,
Evaluation and Results Interpretation. These steps are described and exemplified by the proposed
study.
Figure 1. An Overview of the Steps That Compose the KDD Process [15]

24
At the first stage, there is a possibility to find a certain complexity if the data is not in a single
repository or requires an expert to collect the data. It is worth mentioning the importance of this
step, as we consider the data as key components in the full scope of the problem [16].
For data collection, Petr4 assets has been set as scope, in the timeframe ranging from 01/02/14 to
06/19/15. Located on the Yahoo Finance platform [17], through a web requisition and a few
parameters, as depicted in the table (eg, Table 1).
Table 1. Data type to Yahoo Finance.
Parameter Description
s Action Stock Exchange
a Initial Month
b Initial Day
c Initial Year
d Final Month
e Final Day
f Final Year
g Type of Grouping
Consequently, the quotation data was acquired: date, opening price, maximum, minimum, closing
price, volume and the average closing.
On the second step, the Pre-processing and data cleansing process can take a larger amount of
time [18], aiming integration of heterogeneous data, eliminating incompleteness, verified
consistency of information, filling or eliminating null values, incomplete, duplicated or corrupted.
The data was then analysed and was stated that there are no inconsistent, redundant, null or
corrupted values. “Duplicated” values were found, however, was verified, and certified, that it
was not the case of duplicity, but rather of days in which the closing price on the stock was the
same as the day before.
A study was applied to the data and it was defined as variables only the opening, closing,
minimum, maximum and volume values. Later, an exploratory analysis of variables was made
and ranked them by measure levels, such as quantitative variables, and by manipulative levels,
such as independent variables (i.e. their values affects others) and dependent (i.e. the measured or
registered variables, which are influenced).
The third step is dependent on data mining, targeting the transformation of data, to present the
ideal model for development and interpretation of the algorithm in the next step.
With the view to transform the data to meet the requirements of the mining step, was noticed the
lack of variables making mining more based on the real stock exchange scenario. Then, the main
indicators utilized by financial experts were collected by determining the behavior of the shares
and, which to invest. Among those checked, it was found:
1. Relative Strength Index (RSI): it measures the weakening degree of a trend.
2. Williams: informs the position of an asset.
3. Stochastic: indicates overbought and oversold situations.
4. Money Flow Index (MFI): evaluates the intensity of cash flow.
5. Force Index (FI): indicates force of an asset involving the shift change in price.
6. Moving Average (MA): average quotation, softening its movement.

25
7. Moving Average Convergence Divergence (MACD): shows the relation between moving
averages.
8. Direction Movement: it shows the strength of a positive and negative tendency.
9. Average Directional Index (ADX): it measures the strength of a positive and negative
tendency.
10. On Balance Volume (OBV): it measures the volume flux.
11. Accumulation Distribution Line (ADL): determines the relation between demand and
supply of an asset.
12. Closing Point and Closing Point Volume: position of the closing price within a range, and
its volume.
13. Average True Range (ATR): measures how much the price of a stock is subject to
variation.
After gathering this data, a new analysis of the variables was realized and, subsequently,
performed a two-dimensional analysis of all quantitative variables in relation to an independent
variable, in other words, the closing price of an asset. This procedure had the requirement of
listing which variables would be best applied in the mining algorithm. Thus, the Pearson
correlation coefficient and the Coefficient of Determination were calculated, which determines if
there is a correlation between variables, its force and direction. In the picture below, the variables
are shown: opening price, maximum, minimum, moving average, MACD, OBV, ADL, pivot
close and ATR obtained better results exceeding it by 0.5, given the time series chosen.
Therefore, these were the ones chosen to be used in the next step.
Table 2. Coefficient of Indicators.
Indicators Pearson Correlation
Coefficient
Coefficient of
Determination
Open 0,994487473 0,989005334
High 0,993211486 0,986469055
Low 0,9948341 0,989694887
Pivot Close 0,995855488 0,991728153
Moving Average 0,978291082 0,957053441
MACD 0,941127663 0,885721278
OBV 0,762275575 0,581064052
ATR 0,584487817 0,341626008
ADL 0,508326204 0,25839553
Directional Movement 0,452690778 0,204928941
FI 0,327514359 0,107265656
Volume Pivot Close 0,247212267 0,061113905
IFR 0,193201546 0,037326837
Stochastic 0,182864204 0,033439317
Williams 0,169468496 0,028719571
MFI 0,151817821 0,023048651
ADX -0,029023545 0,000842366
Volume -0,097613838 0,009528461
The mining stage is considered the center of the KDD, which is a technique to determine
behavioral patterns, in larger databases, assisting decision-making [19].
Mining is composed by tasks and techniques (methods). The task consists of the specification of
what do you want to search in the data, types of regularities and patterns. The technique consists
of the specification of methods that show how to discover patterns.

26
The mining tasks are sorted by the diversified ability to extract knowledge, and its success relies
exclusively on the application domain. The most common are: Classification, Association,
Estimation or Regression, Clustering and Summarization [14]. The Regression is similar to the
Classification however, is used to define a numerical value for an unknown continuous variable.
It can be used to estimate future data, such as family income, lifetime-based in diagnosis, etc.
Estimation is learning a function that maps a given item for a variable of estimated real prediction
[14]. Given the main objective of this study the prediction of the closing price value of an asset,
meaning, predict a continuous and quantitative variable, it can be asserted that this technique is
best suited for this case study.
The mining techniques (or methods) can be applied to tasks, implemented by an algorithm
elaborated to solve a task. For Harrison [20] there are different techniques are better suited to a
determined task, having advantages and disadvantages.
The methods are divided into Supervised Learning and Unsupervised Learning. In this case study,
the chosen was the former, also known as predictive, where there is a class for where each sample
is assigned in training.
The algorithms belonging to these methods perform inferences in data, providing predictions, the
classification and regression tasks nevertheless, use different methods, such as: neural networks,
genetic algorithms, inductive logic, decision trees, Bayes classifier, rule based classification,
SVM (Support Vector Machines), Fuzzy Set, linear regression, nonlinear regression (logistic,
polynomial), etc.
It was verified that there are a wide range of works using neural networks, genetic algorithms and
even fuzzy logic, so it was proposed using regression techniques, since it allows to find a function
that estimates the manner based on a data set, through mathematical and statistics resources.
The next step was the analysis of all the possible variables of the problem scope with objective to
predict the closing price for the next day. Therefore, proving the existence of a univariate, linear
and multiple model. As a result of only one dependent variable or answer (closing price) was
found and, for all other variables that went through two-dimensional analysis only the ones with
strong correlation with the dependent variable (as seen in the picture above) were selected,
confirming linearity of the problem (for the selected variables), and multiple for presenting more
than one predictor or independent variable. The model specification is about:
• Multiple regression (has more than one predictor variable);
• Univariate regression (has only one variable answer);
• Positive linear regression;
As a consequence, the selected method for applying data mining was the multiple linear
regression, using the Least Square Method.
For the fifth and last stage of the KDD is the Evaluation and Interpretation of Results which seeks
the knowledge, transforming it in a real model for decision-making. This step is intend to
facilitate the interpretation and evaluation of the utility of the discovered knowledge, by the end
user.
4. RESULTS
Only the preferential asset from the company Petrobras, or simply PETR4, was utilized in the
scope of this study due to its history in Bovespa since it is the focus of other works related with
the stock exchange of São Paulo, thereby, assisting in comparing results.

27
Data collection was conducted between 01/02/14 and 12/30/15, in a total of 342 days analyzed,
willing to predict the next day. It was split 70% for training and discovery of the pattern and 30%
for validation. Then it was set as predictor variables: opening price, maximum, minimum, 5-day
moving average (MMS_5), 10-day moving average (MMS_10), 15-day moving average
(MMS_15), exponential 20-day moving average (MME_20), MACD, OBV, ADL, Closing Point
volume (Volume_PF), Closing Point (PF), ADX and ATR. Of the first data, 70% were applied to
training, subsequently generating a pattern, therefore, a mathematical function, as shown in the
figure (eg, Figure 2).
Figure 2. Prediction Function
Through the exploratory and two-dimensional analysis of the variables already mentioned it is
possible to fit into a linear model. Nonetheless, some variables did not present enough
representation to be considered into account in linear regression. Therefore, obtaining the
mathematical function coefficient equal to 0 (zero). This is seen through the variables of
minimum value, OBV, ADL and Volume_PF, so, despite having 13 indicators, only 10 of those
were used to predict values.
We also obtained a Correlation Coefficient of 0.9917, a Mean Absolute Error of 0.3699, a Root
Mean Squared Error (identifies deviation from the best result) of 0.4716 and a Relative Absolute
Error (indicates the proximity of the prediction compared to the real value) of 6.1555%.
Therefore, claiming the current study proposes a positive impact and is an efficient model
assisting decision-making by financial experts.
The equation found was applied to the remaining 30% of the data, so that is possible verify the
percentage of correct answers, represented in a total of 66 instances. Discovering the prediction of
the closing price and comparing it with the real closing price value, we obtained the following
results.
Table 3. Case Study Results.
Indicators Pearson Correlation
Coefficient
Coefficient of
Determination
Hit
14,96 15,13929 0,179285 true
14,61 14,87246 0,262457 true
14,58 14,79304 0,213037 true
14,43 14,75516 0,325162 true
14,11 14,51267 0,402667 true

28
14,2 14,11755 0,082447 true
14,16 14,14718 0,012817 true
14,15 14,26507 0,115068 true
14,5 14,1859 0,314104 true
14,18 14,49962 0,319625 true
13,68 14,29052 0,610521 false
14,04 13,9624 0,077603 true
13,59 14,25299 0,662988 false
13,29 13,88133 0,591326 false
13,43 13,6242 0,194204 true
It can be verified how close the equations results are similar to the actual real closing, and
considered as success a maximum deviation of 0.50 cents, which according to Bovespa is
satisfactory.
Important to highlight that a $ 0.50 cents deviation represents 4.7169% of the value, which
generates a success rate of 60.87% the cases. Proving that the main goal of the study was
achieved by finding a regressive model to predict the value of an asset with a high success rate.
5. DISCUSSIONS AND COMPARISONS
The information systems possess applications in several areas, in which the study of the financial
market was the focus in this paper.
Given the difficulty of time series predictions with countless variables and the complexity of
specifying a consistent and efficient model, the use of computational techniques of Artificial
Intelligence was selected to solve this problem. Consequently, the current study aimed to take
benefit from KDD to specify a multiple linear regressive model for predicting the closing price of
Petr4.
The application results prove the authenticity of the model, given the high accuracy rate, since the
acceptable movement in Bovespa is $ 0.50, making it possible to accomplish an estimative of
success of a movement even lower. This proposal can be applied to any asset, as long as it passes
through all the KDD stages, mentioned before, and serving as a strong base for the financial
expert in regards to decision-making. Confirming the proposed model as a contribution to the
financial market and a new, complete and, efficient form of analysis. The comparative between
the closing price and the prediction can be better observed in the picture (eg, Figure 3).

29
Figure 3. Comparison forecast
The necessity to insert several indicators as input variables is ratified, rendering a more robust
model in a real situation of stock exchange manner, due to its complex environment and countless
variables that can affect an asset.
What differentiates this study is the closing price prediction value, as most of others studies are
concerned with discovering trends, while few focus on value predictions.
By analyzing and interpreting the results, it has been proven that trend indicators present better
results when the database is composed of more than 100 entries. When the database is lower than
that, it is recommended not to use these variables.
In literature, renowned models capable of prediction time series behavior can be found, such as
the Box-Jenkins univariate method called ARIMA. However, applying this method using closing
prices for predictions, it did not result in success, even though 12 differentiations were applied a
stationary series that fulfilled the methodology was not found, leading towards believing in the
method applied by this study.
Almeida [12] says, the fuzzy logic was used to predict the trend, comparing it to three
consecutive days in the stock exchange. However, a percentage of success/mistakes was not
enough to validate the model. It was also considered low the quantity of indicators used as
variables input, given the stock exchange complexity. Nonetheless, in the work of Marques [11] a
hybrid model was proposed for trend prediction, but its success rate was only considered average.
The results produced by the current study the success rate was 60.87% and it can be considered
an improvement over the results of Marques [11], which concludes that linear techniques can be
considered in value predictions and its results are similar to other artificial intelligence
techniques.
Regarding the work of Fagner [13], which obtained great results, there was a difference while
choosing the data, as Fagner choose the end of the month as closing price values, while the
current study opted to analyze the whole time series of the asset, aiming to stay truthful to the
stock exchange complexity.

30
About Nascimento [1], the best predictions made involved the Petrobrás share Petr3. In his
model, the author conducted estimations based on 258 of quotations. Among these, the number of
estimative that were closer to below 0.50 from the real values of shares were only 144, thus,
55.8% from the total sample. In a comparative study between the approach of Nascimento [1] and
the current study being presented, is noted that the success rate of Nascimento [1] has been
surpassed.
REFERENCES
[1] Nascimento, T. P. (2015) “A System Based on Genetic Algorithms as a Decision Making Support for
the Purchase and Sale of Assets at Bovespa”, In: International Biometrics & Smart Government
Summit, Sousse, International Biometrics & Smart Government Summit.
[2] Costa, F. V. & Marcondes, R. (2012) “Recursos estratégicos em corretoras de valores mobiliários
visando a busca de vantagem competitiva pela abordagem rbv”, Seminários em Administração-
SEMEAD.
[3] Junior, M. M. O. L. (2013) “Proposta de um modelo de predição da bolsa de valores usando uma
abordagem híbrida”, Dissertação de Mestrado (Programa de Pós-Graduação em Engenharia de
Eletricidade, Área de Concentração: Ciência da Computação) - Centro de Ciências Exatas e
Tecnologia - Universidade Federal do Maranhão, São Luís.
[4] Rocha, H. R. & Macedo, M. (2011) “Previsão do preço de ações usando redes neurais”, Congresso
USP de Iniciação Científica em Contabilidade, 8 - São Paulo, São Paulo.
[5] Graham, B. G. (1973) The Intelligent Investor, New York: Harper Business.
[6] Pinheiro, J. L. (2007) Mercado de Capitais: Fundamentos e Técnicas. Atlas, São Paulo.
[7] Pereira, U. N. C. & Turrioni, J. B. & Pamplona, E. O. (2005) “Avaliação de Investimentos em
Tecnologia da Informação – TI”, XXV Encontro Nacional de Engenharia de Produção, Anais, Porto
Alegre - RS, 1CD.
[8] Weiss, S. (2002) Handheld Usability, England: John Wiley & Sons Ltd.
[9] Graeml, A. R. (1998) O valor da tecnologia da informação. Anais do I Simpósio de Administração da
Produção, Logística e Operações Industriais.
[10] Koulouriotis, D. E. & Diakoulakis, I. E. & Emiris, D. M. (2001) “A Fuzzy Cognitive Map based
Stock Market Model: Synthesis, Analysis and Experimental Results”, IEEE International Fuzzy
Systems Conference, pp465-468.
[11] Marques, F. C. R. & Gomes, R. M. (2009) “Análise de séries temporais aplicadas ao mercado
financeiro com o uso de algoritmos genéticos e lógica fuzzy”, Congresso da Sociedade Brasileira de
Computação, No 29, pp749–758.
[12] Almeida, A. J. S. (2015) “A Market Prediction Model Stock Based on Fuzzy Logic”, In: 12th
CONTECSI International Conference on Information Systems and Technology Management, 2015,
Sao Paulo, Proceedings of the 12th CONTECSI International Conference on Information Systems and
Technology Management, Vol. 12.
[13] Oliveira, F. A. (2013) “Modeling via artificial neural networks the Capital Market to the share value
forecast and improve steering accuracy rate – Case Study Petr4 – Petrobras, Brazil”.
[14] Fayyad, U. M. & Piatetsky-Shapiro, G. & Smyth, P. & and Uthurusamy, R. (1996) Advances in
Knowledge Discovery and Data Mining, Menlo Park, Calif.: AAAI Press.
[15] Fayyad, U & Piatetsky-Shapiro, G & Smyth, P. (1996) “From Data Mining to Knowledge Discovery
in Databases”. American Association for Artificial Intelligente.
[16] Almeida, F. C. & Dumontier, P. (1996) “O Uso de Redes Neurais em Avaliação de Riscos de
Inadimplência”, Revista de Administração FEA/USP, São Paulo.
[17] Yahoo Finance, http://finance.yahoo.com/, Accessed in 2015/06/15 at 8:00 am.
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AUTHORS
Nadson Silva Timbó
BS in Computer Science – Federal University of Maranhao (2013). Has experience in
Computer Science Area.
Sofiane Labidi
BS in Computer Science - Institut Supérieur Scientifique (1990), MSc. In Computer
Science - Université de Nice Sophia Antipolis Centre National de Recherches
Scientifiques (1991) and Ph,D. In Computer Science - Institut National de Recherche
en Informatique et Automatique (1995). He is currently full professor at Universidade
Federal do Maranhão. Has experience in Computer Science, acting on the following
areas: knowledge management, multi-agent systems, educational technologies, agents,
artificial intelligence and business process modelling.
Thiago Pinheiro do Nascimento
BS in Computer Science - Faculdade de Ciências Humanas, Saúde, Exatas e Jurídicas
de Teresina (2012) and MSc. In Electric Engineering - Universidade Federal do
Maranhão (2015). Has experience in Computer Science, acting on the following
subjects: frameworks, software engineering component-based, service-oriented
architecture, software reuse and web development.
Gilberto Nunes Neto
B.Ed in Computing – Piaui State University (2006). Has experience in Computer
Science.
Milson Louseiro Lima
Has Postgraduate Diploma in ANALYSIS AND SYSTEMS PROJECT (UFMA,
2007), B.S in Economic Sciences - Federal University of Maranhão (2006). Is ERP
developer and mobile devices, since 1998. He is currently a MSc candidate in
Electrical Engineering course for Computer Science (UFMA, 2014), working in the
Intelligent Systems Laboratory at the Federal University of Maranhão (LSI / UFMA).
Rodrigo Costa Matos
Has experience in Computer Science.

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