A share market is a place of high interest to the investors as
it presents them with an opportunity to benefit financially
by investing their resources on shares and derivatives of
various companies. It is a chaos system; meaning the
behavioral traits of share prices are unpredictable and
uncertain. To make some sort of sense of this chaotic
behavior, researchers were forced to find a technique which
can estimate the effect of this uncertainty to the flow of
share prices. From the analyses of various statistical
models, Artificial Neural Networks are analogous to
nonparametric, nonlinear, regression models. So, Artificial
Neural Networks (ANN) certainly has the potential to
distinguish unknown and hidden patterns in data which can
be very effective for share market prediction. If successful,
this can be beneficial for investors and financers and that
can positively contribute to the economy.

There are different methods that have been applied in order
to predict Share Market returns. Tang and Fishwick[1];
Wang and Leu [2] provided a general introduction of how a
neural network should be developed to model financial and
economic time series. During the last decade, Artificial
Neural Networks have been used in share market
prediction. One of the first such projects was by Kimoto et
al. [3] who had used ANN for the prediction of Tokyo
stock exchange index. Minzuno et al. [4] applied ANN
again to Tokyo stock exchange to predict buying and
selling signals with an overall prediction rate of 63%.
Sexton et al. [5] theorized that the use of momentum and
start of learning at random points may solve the problems
that may occur in training process in 1998. Phua et al. [6]
applied neural network with genetic algorithm to the stock
exchange market of Singapore and predicted the market
direction with an accuracy of 81%.

This paper demonstrates Back propagation method for
training the Neural Network and Multilayer Feed forward
network in order to forecast the share values. The aim of
this paper is to use ANNs to forecast Bangladesh Stock
Exchange market index values with reasonable a degree of
accuracy.

2. PREDICTION METHOD ANALYSIS:
Trading shares and commodities were primarily based on
intuitions. As the trading grew, people tried to find methods
and tools which can accurately predict the share prices
increasing their gains and minimizing their risk. Many
methods like fundamental analysis, technical analysis, and
machine learning method have all been used to attempt
predictions of share prices but none of these methods have
been proven as a consistently applicable prediction tool.

2.1 Fundamental Analysis
Fundamental analysis is the physical study of a company in
terms of its product sales, manpower, quality, infrastructure
etc. to understand it standing in the market and thereby its
profitability as an investment [7]. The fundamental analysts
believe that the market is defined 90 percent by logical and
10 percent by physiological factors. But, this analysis is not
suitable for our study because the data it uses to determine
the intrinsic value of an asset does not change on daily
basis and therefore is not suitable for short-term basis.
However, this analysis is suitable for predicting the share
market only in long-term basis.

To undergo this training refer the link below: