الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
It became obvious the role of machine learning algorithms in different
fields which used to analyze data, values prediction, and classification.
One of these fields is the finance field so there are different applications
of machine learning in finance and one of these applications is the stock
market value prediction and classification of its direction.
A stock market is a place where individuals are buying and selling shares
of publicly traded companies. A stock is a type of investment that
represents an ownership share in a company and investors purchase stocks
that they think will go up in value so machine learning algorithms are used
to predict and classify the stock market value and its direction by gathering
historical datasets.
The purpose of predicting the stock market is to anticipate the price value
and direction of stock as higher profits will investors can be made by
higher accuracy prediction are gotten and here is one of the most
challenging issues is predicting how the stock market will move.
Neural Network has intended to mirror the elements of the human mind
and this imitation of brain modeling permits the neural network to learn
from experience without requiring human mediation and adjust
appropriately to the circumstances.
This thesis is going to predict the value of the close price and the direction
of the stock market by applying two proposed models. The first proposed
model is artificial neural network architecture and this is used to classify
the stock market’s direction. The second proposed model is Long-short
term memory neural network architecture and this is used to predict the
close price value. Finally, the results of performance will be compared
with other new published papers that were used the same dataset.