*
Abstract:*

Peoples tend
to invest in stocks because of its high returns over time. Stock
markets are affected by many highly interrelated economic, social,
political and even psychological factors, and these factors interact
with each other in a very complicated manner. Therefore it is
generally challenging task to predict the movement of stock market.
It is observed that conventional statistical techniques for prediction
have reached their limitation in applications with nonlinearities in
the data set. Artificial Neural Network, a computing system containing
many simple nonlinear computing units as neurons interconnected by
links, is a well-tested method for financial analysis on the stock
market. This paper explains in detail various prediction methodologies
for stock market and found that Artificial Neural network could be
useful for stock market prediction.

** **

*Key
words**:*
Artificial Neural Network, Stock market, Time series analysis etc.

**
**

**1.
Introduction: **

**
**

There are
several motivations for trying to predict stock market prices. The
most basic of these is financial gain. Any system that can
consistently pick winners and losers in the dynamic market place would
make the owner of the system very wealthy. Thus, many individuals
including researchers, investment professionals, and average investors
are continually looking for this superior system which will yield them
high returns.

The
prediction of stock market is without doubt an interesting task. In
the literature there are a number of methods applied to accomplish
this task. These methods use various approaches, ranging from highly
informal ways (e.g. the study of a chart with the fluctuation of the
market) to more formal ways (e.g. linear or non-linear regressions).

Analytical
Methods (Technical & fundamental) ,Traditional Time Series Prediction
Methods,Choas theory ,computer techniques, comparative methods &
Machine learning method (i.e Neural Network) these techniques were
used for prediction of stock markets. What is common to these
techniques is that they are used to predict and thus benefit from the
market’s future behavior. None of them has proved to be the
consistently correct prediction tool that the investor would like to
have. Furthermore many analysts question the usefulness of many of
these prediction techniques.

Neural
networks are used to predict stock market prices because they are able
to learn nonlinear mappings between inputs and outputs. Contrary to
the EMH, several researchers claim the stock market and other complex
systems exhibit chaos. Chaos is a nonlinear deterministic process
which only appears random because it cannot be easily expressed. With
the neural networks’ ability to learn nonlinear, chaotic systems, it
may be possible to outperform traditional analysis and other
computer-based methods [5].

In addition
to stock market prediction, neural networks have been trained to
perform a variety of financial related tasks. There are experimental
and commercial systems used for tracking commodity markets and
futures, foreign exchange trading, financial planning, company
stability, and bankruptcy prediction. Banks use neural networks to
scan credit and loan applications to estimate bankruptcy
probabilities, while money managers can use neural networks to plan
and construct profitable portfolios in real-time. As the application
of neural networks in the financial area is so vast, this paper will
focus on stock market prediction.

Finally,
although neural networks are used primarily as an application tool in
the financial environment, several research improvements have been
made during their implementation. Notable improvements in network
design and training and the application of theoretical techniques are
demonstrated by the examination of several example systems [5].

**
2.Analytical Methods:**

** **

Before the
age of computers, people traded stocks and commodities primarily on
intuition. As the level of investing and trading grew, people searched
for tools and methods that would increase their gains while minimizing
their risk. Statistics, technical analysis, fundamental analysis, and
linear regression are all used to attempt to predict and benefit from
the market’s direction. None of these techniques has proven to be the
consistently correct prediction tool that is desired, and many
analysts argue about the usefulness of many of the approaches.
However, these methods are presented as they are commonly used in
practice and represent a base-level standard for which neural networks
should outperform. Also, many of these techniques are used to
preprocess raw data inputs, and their results are fed into neural
networks as input.

**2.1
Technical Analysis:**

** **

“Technical
analysis is the method of predicting the appropriate time to buy or
sell a stock used by those believing in the castles-in-the-air view of
stock pricing” [10]. The idea behind technical analysis is that share
prices move in trends dictated by the constantly changing attributes
of investors in response to different forces. Using technical data
such as price, volume, highest and lowest prices per trading period
the technical analyst uses charts to predict future stock movements.
Price charts are used to detect trends; these trends are assumed to be
based on supply and demand issues which often have cyclical or
noticeable patterns. From the study of these charts trading rules are
extracted and used in the market environment. The technical analysts
are known as a‘*chartists*’. Most chartists believe that the
market is only *10 percent logical and 90* *percent
psychological *[10]. The chartist’s belief is that a careful study
of what the other investors are doing will shed light on what the
crowed is likely to do in the future.

This is a
very popular approach used to predict the market, which has been
heavily criticized. The major point of criticism is that the
extraction of trading rules from the study of charts is highly
subjective therefore different analysts might extract different
trading rules by studying the same charts.Some technical indicator
categories include filter indicators, momentum indicators, trend line
analysis, cycle theory, volume indicators, wave analysis, and pattern
analysis. Indicators may provide short or long term information, help
identify trends or cycles in the market, or indicate the strength of
the stock price using support and resistance levels.An example of a
technical indicator is the moving average. The moving average averages
stock prices over a given length of time allowing trends to be more
visible. Several trading rules have been developed which pertain to
the moving average. For example, "when a closing price moves above a
moving average a buy signal is generated."[3]. unfortunately, these
indicators often give false signals and lag the market. That is, since
a moving average is a past estimate, a technical trader often misses a
lot of the potential in the stock movement before the appropriate
trading signal is generated. Thus, although technical analysis may
yield insights into the market, its highly subjective nature and
inherent time delay does not make it ideal for the fast, dynamic
trading markets of today.

2.2
**Fundamental Analysis:**

Fundamental
analysis is the technique of applying the tenets of the firm
foundation theory to the selection of individual stocks”[10]. The
analysts that use this method of prediction use fundamental data in
order to have a clear picture of the firm (industry or market) they
will choose to invest on. They are aiming to compute the ‘real’ value
of the asset that they will invest in and they determine this value by
studying variables such as the growth, the dividend payout, the
interest rates, the risk of investment, the sales level, the tax rates
an so on. Their objective is to calculate the intrinsic value of an
asset (e.g. of a stock). Since they do so they apply a simple trading
rule. *If the intrinsic* *value of the asset is higher than the
value it holds in the market, invest in it. If not,* *consider it
a bad investment and avoid it. *The fundamental analysts believe
that the market is defined 90 percent by logical and 10 percent by
physiological factors.

This type of
analysis is not possible to fit in the objectives of our study. The
reason for this is that the data it uses in order to determine the
intrinsic value of an asset does not change on daily basis. Therefore
fundamental analysis is helpful for predicting the market only in a
long-term basis.

** **

The
advantages of fundamental analysis are its systematic approach and its
ability to predict changes before they show up on the charts.
Companies are compared with one another, and their growth prospects
are related to the current economic environment. This allows the
investor to become more familiar with the company. Unfortunately, it
becomes harder to formalize all this knowledge for purposes of
automation (with a neural network for example), and interpretation of
this knowledge may be subjective. Also, it is hard to time the market
using fundamental analysis. Although the outstanding information may
warrant stock movement, the actual movement may be delayed due to
unknown factors or until the rest of the market interprets the
information in the same way. However, fundamental analysis is a
superior method for long-term stability and growth. Basically,
fundamental analysis assumes investors are 90% logical, examining
their investments in detail, whereas technical analysis assumes
investors are 90% psychological, reacting to changes in the market
environment in predictable ways.

**3.
Traditional Time Series Forecasting**

The
Traditional Time Series Prediction analyzes historic data and attempts
to approximate future values of a time series as a linear combination
of these historic data.

In
econometrics there are two basic types of time series forecasting: *
univariate *(simple regression) and *multivariate *
(multivariate regression) [9].These types of regression models are the
most common tools used in econometrics to predict time series. The way
they are applied in practice is that firstly a set of factors that
influence (or more specific is assumed that influence) the series
under prediction is formed.

These factors
are *the explanatory variables xi *of the prediction model. Then
a mapping between their values xit and the values of the time series
yt *(y is the to-be* *explained variable) *is done, so that
pairs {xit , yt} are formed. These pairs are used to define the
importance of each *explanatory variable *in the formulation of
the *to-be explained variable*. In other words the linear
combination of xi that approximates in an* *optimum way y is
defined. *Univariate *models are based on one *explanatory
variable *(I=1) while *multivariate *models use more than one
variable (I>1).* *Regression models have been used to predict
stock market time series. A good example* *of the use of
multivariate regression is the work of Pesaran and Timmermann (1994)*
*[15].

**4 .The
Efficient Market Hypothesis:**

** **

The Efficient
Market Hypothesis (EMH) states that at any time, the price of a share
fully captures all known information about the share. Since all known
information is used optimally by market participants, price variations
are random, as new information occurs randomly. Thus, share prices
perform a "random walk", and it is not possible for an investor to
beat the market

Despite its
rather strong statement that appears to be untrue in practice, there
has been inconclusive evidence in rejecting the EMH. Different studies
have concluded to accept or reject the EMH. Many of these studies used
neural networks to justify their claims. However, since a neural
network is only as good as it has been trained to be, it is hard to
argue for acceptance or rejection of the hypothesis based solely on
neural network performance. In practice, stock market crashes, such as
the market crash in October 1987, contradict the EMH because they are
not based on randomly occurring information, but arise in times of
overwhelming investor fear.

The EMH is
important because it contradicts all other forms of analysis. If it is
impossible to beat the market, then technical, fundamental, or time
series analysis should lead to no better performance than random
guessing. The fact that many market participants can consistently beat
the market is an indication that the EMH may not be true in practice.
The EMH may be true in the ideal world with equal information
distribution, but today’s markets contain several privileged players
who can outperform the market by using inside information or other
means.

**5 .Chaos
Theory:**

** **

A relatively
new approach to modeling nonlinear dynamic systems like the stock
market is chaos theory. Chaos theory analyzes a process under the
assumption that part of the process is deterministic and part of the
process is random. Chaos is a nonlinear process which appears to be
random. Various theoretical tests have been developed to test if a
system is chaotic (has chaos in its time series). Chaos theory is an
attempt to show that order does exist in apparent randomness. By
implying that the stock market is chaotic and not simply random, chaos
theory contradicts the EMH .In essence, a chaotic system is a
combination of a deterministic and a random process. The deterministic
process can be characterized using regression fitting, while the
random process can be characterized by statistical parameters of a
distribution function. Thus, using only deterministic or statistical
techniques will not fully capture the nature of a chaotic system. A
neural networks ability to capture both deterministic and random
features makes it ideal for modeling chaotic systems.

**6. Other
Computer Techniques:**

Many other
computer based techniques have been employed to forecast the stock
market. They range from charting programs to sophisticated expert
systems. Fuzzy logic has also been used.

Expert
systems process knowledge sequentially and formulate it into rules.
They can be used to formulate trading rules based on technical
indicators. In this capacity, expert systems can be used in
conjunction with neural networks to predict the market. In such a
combined system, the neural network can perform its prediction, while
the expert system could validate the prediction based on its
well-known trading rules. The advantage of expert systems is that they
can explain how they derive their results. With neural networks, it is
difficult to analyze the importance of input data and how the network
derived its results. However, neural networks are faster because they
execute in parallel and are more faults tolerant.

The major
problem with applying expert systems to the stock market is the
difficultly in formulating Knowledge of the markets because we
ourselves do not completely understand them. Neural networks have an
advantage over expert systems because they can extract rules without
having them explicitly formalized. In a highly chaotic and only
partially understood environment, such as the stock market, this is an
important factor. It is hard to extract information from experts and
formalize it in a way usable by expert systems. Expert systems are
only good within their domain of knowledge and do not work well when
there is missing or incomplete information. Neural networks handle
dynamic data better and can generalize and make "educated guesses."
Thus, neural networks are more suited to the stock market environment
than expert systems.

**7.
Comparing the various models:**

** **

In the wide
variety of different modeling techniques presented so far, every
technique has its own set of supporters and detractors and vastly
differing benefits and shortcomings. The common goal in all the
methods is predicting future market movements from past information.
The assumptions made by each method dictate its performance and its
application to the markets.

The EMH
assumes that fully disseminated information results in an
unpredictable random market. Thus, no analysis technique can
consistently beat the market as others will use it, and its gains will
be nullified. I believe that the EMH has some merit theoretically, but
in real-world applications, it is painfully obvious that there is an
uneven playing field. Some market participants have more information
or tools which allow them to beat the market or even manipulate it.
Thus, stock market prices are not simply a random walk, but are
derived from a dynamic system with complexities to vast to be fully
accounted for. If an investor does not believe in the EMH, the other
models offers variety of possibilities. Technical analysis assumes
history repeats itself and noticeable patterns can be discerned in
investor behavior by examining charts. Fundamental analysis helps the
long-term investor measure intrinsic value of shares and their future
direction by assuming investors make rational investment decisions.
Statistical and regression techniques attempt to formulate past
behavior in recurrent equations to predict future values. Finally,
chaos theory states that the apparent randomness of the market is just
nonlinear dynamics too complex to be fully understood.

So what model
is the right one? There is no right model. Each model has its own
benefits and Short comings. I feel that the market is a chaotic
system. It may be predictable at times, while at other times it
appears totally random. The reason for this is that human beings are
neither totally predictable nor totally random. Although it is nearly
impossible to determine a person’s reaction to information or
situations, there are always some basic trends in behavior as well as
some random elements. The market is a collection of millions of people
acting in a chaotic manner. It is as impossible to predict the
behavior of a million people as it is to predict the behavior of one
person. Investors are neither mostly psychological as predicted by
technical analysis, nor logical as predicted by fundamental analysis.
Our approach and view on the world varies daily in a manner that we do
not even fully understand, so it follows that the stock market behaves
in similar ways.

In
conclusion, these methods work best when employed together. The major
benefit of using a neural network then is for the network to learn how
to use these methods in combination effectively, and hopefully learn
how the market behaves as a factor of our collective consciousness.

**8. Machine
Learning Methods:**

** **

Several
methods for inductive learning have been developed under the common
label*“Machine Learning”*. All these methods use a set of samples
to generate an approximation of the underling function that generated
the data. The aim is to draw conclusions from these samples in such
way that when unseen data are presented to a model it is possible to
infer the *to-be explained variable *from these data. The methods
we discuss here are: The Nearest Neighbor and the Neural Networks
Techniques. Both of these methods have been applied to market
prediction; particularly for Neural

Networks
there is a rich literature related to the forecast of the market on
daily basis.

**8.1
Nearest Neighbor Techniques:**

** **

The nearest
neighbor technique is suitable for *classification tasks*. It
classifies unseen data to bins by using their ‘distance’ from the k
bin centroids. The ‘distance’ is usually the Euclidean distance. In
the frame of the stock market prediction this method can be applied by
creating three (or more) bins. One to classify the samples that
indicate that the market will rise. The second to classify the samples
that indicate fall and the third for the samples related with no
change of the market [16].

Although this
approach can be used to predict the market on daily basis we will not
attempt to apply it on this study. The main reason is that we will not
attempt a classification but a regression task. The classification
task has the disadvantage that it flattens the magnitude of the change
(rise of fall). On the other hand it has the advantage that as a task
it is less noisy comparing to regression. Our intention is to see how
well a regression task can perform on the prediction of the market.

**9.2
Applications of Neural Networks to Market Prediction:**

** **

**9. 2.1
.Introduction:**

** **

The ability
of neural networks to discover nonlinear relationships in input data
makes them ideal for modeling nonlinear dynamic systems such as the
stock market. Various neural network configurations have been
developed to model the stock market. Commonly, these systems are
created in order to determine the validity of the EMH or to compare
them with statistical methods such as regression.

‘A neural
network may be considered as a data processing technique that maps, or
relates, some type of input stream of information to an output stream
of data‘ [2].

Neural
Networks (NNs) can be used to perform *classification *and *
regression *tasks. More specifically it has been proved by Cybenko
(cited in Mitchel, 1997) that any function can be approximated to
arbitrary accuracy by a neural network [11].

NNs are
consisted of *neurons *(or *nodes*) distributed across *
layers*. The way these neurons are distributed and the way they are
linked with each other define the *structure* *of the network*.
Each of the links between the neurons is characterized by a *weight*
value. A *neuron *is a processing unit that takes a number of
inputs and gives a distinct output. Apart from the number of its
inputs it is characterized by a function *f *known as *transfer
function*. The most commonly used transfer functions are: the *
hardlimit*, the *pure linear*, the *sigmoid *and the *
tansigmoid *function.

Ample of work
is done on Neural Network for predicting stock market by many
scientist, as a neural network being most ideal technique for
predicting stock market putforth by some researcher is enlighted as
below.

Research of
Kimoto, T., Asakawa, K., Yoda, M. and Takeoka, M [7] (1990) seems to
be the first research where a system based on neural networks has been
tried in a real environment (Tokyo Stock Exchange Prices Indexes) and
has succeeded in beating the market. They use five inputs; *vector
curve*, *turnover*, *interest rate*, *foreign exchange
rate *and *Dow Jones* *average index*. The approach
followed is the modular network approach, in which different networks
learn for different data items. Each expert module has its own input
domain and preprocessing unit. A final post-processing unit has
combined the results to an overall output. The research has been
further funded by an investment firm. Each modular network has one
hidden layer, uses standard sigmoid as an output function and is
trained using back propagation algorithm.

Jing Tao YAO &
Chew Lim TAN [6] in 1998 showed neural networks are suitable for
financial forecasting and marketing analysis .they can be very much
useful for financial time series, such as stock exchange indices etc.
and they shows that NN models can outperform conventional models in
most cases.

Benjamin W.Wah &
Minglun Qian [3] in 2002 developed a new constrained artificial neural
network (ANN) formulation and learning algorithms to predict future
stock prices, a difficult time series prediction problem. Their
experimental results demonstrate good prediction accuracy in a 10-day
horizon. They used recurrent neural network.

Erdinc Altay and
M. Hakan Satman ESQ [4] in 2005,studied the Istanbul Stock exchange
can be forecasted through the learning procedure of Artificial Neural
Network and compared the forecasting performance of artificial neural
network with linear regression and buy and hold strategies’

Qing Cao, Karyl
B Leggio, Marc J.Schniederjans [12] in 2005 used Artificial Neural
Networks to predict stock price movement (i.e. price return ) for
firms traded on the Shanghai Stock Exchange and compared the
predictive power of univariate and multivariate neural network models
and results shows that Neural Network outperform the linear models
compared and these results are statistically significant across our
sample firms and indicated neural networks are useful tool for stock
price Prediction in emerging markets like china.

AkinwaJe, A.T.,
IbharaJu, F.T. and Arogundade,[1] in 2006. Artificial neural network
was used to predict movements in stock prices in Nigerian Stock
Exchange market. Studies were carried out for the prediction of stock
index values as well as daily direction of changes in the index. A
network was designed using Back Propagation Algorithm (BPA) to predict
stock index values and prices in the exchange for a period of 90 days.
The data collected during this period was processed using the BPA
algorithm to get an output such that the error between the actual
indices and prices, and the computed output was brought to minimum.
About 90% of the data was used for the actual training while the
remaining 10% was used as test data. The same data was also processed
using the Least Squares (LS) method. The results show that BPA
algorithm has superior performances in terms of the accuracy of
prediction over the LS method. This result of the study is useful to
stock market operators.

Qing Cao ,Mark E.Parry and Karyl
B. Leggio [13] in 2009 examined the predictive ability of several
well-established forecasting models, including dynamic versions of a
single factor CAPM –based model and Fema and French’s three-factor
model , compared these models with artificial neural network(ANN)
models that contains the same predictor variables but relaxes the
assumptions of model linearity. And find no statistical difference in
the forecasting accuracy of CAPM and three factor model and also found
that neural networks may be a useful tool for stock price prediction
in emerging markets.

Roza Gharoie
Ahangar , Mahmood yahyazadehfar & Hassan Pournaghshband [14] in 2010
estimated the stock prices of activated companies in Tehran (Iran)
stock Exchange. It is used Linear Regression and Artificial Neural
Methods and compared these two methods. And presented an equation for
two methods and compared their results which shown that artificial
neural method is more efficient than linear regression method.

**9. 2.2
Biological Neuron: -**

Much is still
unknown about how the brain trains itself to process information, so
theories abound. Biological brains are composed of cells called
neurons. Each neuron has cell body or Soma. From which extended a
single axon (The neuron sends out spikes of electrical activity
through a long, thin stand known as an *axon),* along which
impulses are transmitted to other neurons when the soma is
sufficiently excited by incoming impulses. These incoming impulses are
received by sensors called dendrites. The area where the impulses are
received is called synapse. Never impulses are transmitted from one
neuron to another across synapses.

These neural
structures operate electro chemically. This means that the brain work
on electrical impulses that are created by chemical action. When the
combined interplay of many incoming impulses reaches a level of
excitement or electrical charge called as threshold.[5]

*
Synapse*
converts the activity from the axon into electrical effects that
inhibit or excite activity from the axon into electrical effects that
inhibit or excite activity in the connected neurons.

Electrical
activity down its axon. Learning occurs by changing the effectiveness
of the synapses so that the influence of one neuron on another
changes.

**9. 2.3 The
Mathematical Model:-**

An appropriate
notational convention, regression model in which the expected
response, y, is related to the values of x = (x_{1…….…} x_{p})
of covariance according to,

Y = w_{o}
+_{j
}x_{j}

The
notational convention is that the circle represents a computational
unit, into which the x_{j’ }s are fed and multiplied by the
respective w_{j}’s .The resulting products are added and then
a further w_{o} is added to provide the eventual output [5] .
In this way we create a neural network representation of a very
familiar statistical construct, because figure 1 is a version of a
standard neural network called the simple or single –unit perceptron.

**10.
Conclusion:**

Prediction of
stock market not an essay task. Scientist try to develope such a
methodology that investors, brokers could get maximum profits. At
earliest investors would use different methodologies like Time series
Analysis, Fundamental Analysis ,Technical anaysis.After that
Efficient market hypothesis,choas theory, Some comparing models,
various computer techniques were used. In contemporary period ,Neural
Network methodology is very effective for predicting stock markets.

**11.
References:**

[1]
AkinwaJe, A.T., IbharaJu, F.T. and
Arogundade “Comparative analysis of artificial neural network’s Back
propagation Algorithm to Statistical Least Square method in security
prediction using Nigerian Stock Exchange Market.” Proceedings of
Second Conference of Science & National Development, pp10-16, oct 2006

[2]
Azoff E. M. Neural network time series
forecasting of financial markets.Chichester: John Wiley and
Sons.1994.

[3]
Benjamin W.Wah & Minglun Qian “
Constrained Formulation and algorithms for stock price prediction
using Recurrent FIR Neural Networks”, prof.National Conferences on
Artificial Intelligence , AAAI,2002.

[4]
Erdinc Altay and M. Hakan Satman “ Stock
market forecasting; Artificial Neural Network and linear regression
comparison in an emerging market” Journal of financial management and
analysis ,vol. 18(2),pp.18-33,2005.

[5]
Haykin S. “Neural Network: A
Comprehensive Foundation”, Macmillan College Publication, New York,
1994.

[6]
Jing Tao YAO & Chew Lim TAN “ Guidelines
for Financial Forecasting with Neural network”,jounal of
forecasting”,, Proceedings of International Conference on Neural
Information Processing, Shanghai, China, 14-18 November,
pp757-761,2001

[7]
Kimoto, T., Asakawa, K., Yoda, M. and
Takeoka, M.” Stock market Prediction systemwith modular neural
networks”, In Proceedings of IJCNN-90, San Diego, 1990.

[8]
Kunwar Singh Vaisla and Authosh kumar
Bhaatt,”An Analysis of performance of Artificial Neural Network
technique for stock market forecasting”, International Journal on
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[9]
Maddala G.S. Introduction to econometrics.
New York, Toronto: Macmillan Publishing Company,1992.

[10]
Malkei B. G..). A random walk down wall
street. New York, London: W. W. Norton & Company, 7th ed, 1999

[11]
Mitchell M.T. Machine learning. New York:
The McGraw-Hill Companies,1997.

[12]
Quig Cao,Kary B.Leggio and Marc
J.Schniederjans “ A Comparison between Fema and French’s model and
artificial neural networks in predicting the Chinese stock market”,
Computers and operation Research,vol .32 ,pp2499-2512,2005.

[13]
Qing Cao, Mark E.Parry and Karyl B.Leggio
“The three-factor model and artificial neural networks; predicting
stock price movement in china”, Springer science and business Media
vol.250, 2009.

[14]
Roza Gharoie Ahangar,Mahmood yahyazadehfar
& Hassan Pournaghshband,” The comparison of methods Artificial Neural
Network with linear regression using specific variables for prediction
stock prices in Tehran stock Exchange”, International journal of
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2010.

[15]
Pesaran H. M. & Timmermann A. Forecasting
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335-367,1994.

[16]
Yuehui Chen, Bo Yang and Ajith
Abraham,”Flexiable Neural Trees ensemble for Stock index modeling”
Neurocomputing, vol.70, pp 697-703, 2007