الفهرس 
Problem DefinitionOnly 14 pages are availabe for public view
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Abstract
The financial sector is a part of the economy composed of firms and institutions that
introduce financial services to commercial and retail customers around the world. This sector
includes a wide range of businesses including banks, investment businesses, insurance firms,
and real estate organizations. The financial market is a dynamic and composite system where
people can transport, buy, and sell currencies, stocks, and derivatives over virtual platforms
introduced by agents. Financial stock is a significant factor in the economy. The stock market
enables stockholders from owning shares of public companies through the trading process
either by exchange or over-the-counter markets. This market has given investors the chance
of earning money and having a prospering life through investing small initial amounts of
money.
The ability to predict stock prices is an important issue regarding the academic area as well as
business. Prediction of stock price behavior is an area of strong effect for both academic
researchers and industry practitioners, as it is both a difficult task and could lead to increased
profits. In this thesis, we developed four approaches for stock analysis and recommendation.
The main idea of the first one is to develop a stock price prediction framework using different
machine learning models such as and deep learning models. This approach is trained and
tested against three stock datasets as Indian, yahoo, and Google stock. In this approach, timeseries stock data problems such as missing values, data duplication, outliers, wide data range,
and feature selection are detected and solved by preprocessing techniques, and the overfitting
problem is solved using the time series cross-validation technique.
The purpose of the second proposed approach is to explore the impact of social news and
historical data together on the stock movement and stock trend using an intelligent technique.
The main purpose is to develop hybrid modeling that is composed of several stages of
random forest classifier and one stage of stacked- LSTM to improve the analysis accuracy.
This approach is developed with two versions, the first version using default parameters of
intelligent models and the second version adding hyper parameters technique for intelligent
models then the proposed approach is improved using time-series cross-validation with 5splitto provide more accurate predictions. By using time series cross-validation, the overfitting
problem is eliminated.
The third proposed approach was developed to recommend the investor with the best decision
about the stock exchange process (good investment/ bad investment) using stock twits’
analysis and historical data analysis during Crises (covid -19 (to predict the future stock price
based on intelligent techniques such as random forest and stacked – LSTM and preprocessing
techniques in addition to parameters tunning. The main aim of this approach is to explore the
impact of StockTwits (customer opinions) and historical data together on the stock close
price for future days. Then our system is improved using time series cross-validation to
enhance system confidence.
from the previous work, some conclusions are gathered. Some work concentrated on using
only historical data analysis for the stock price prediction process rejecting the effect of news
headlines and stock twits on the future stock movement. Or using the news headlines or
stock twits neglecting the effect of historical data analysis. So, in this thesis, proposed hybrid
systems are introduced. Also, most of the previous work stopped at the prediction process and
does not support the economy system with an efficient recommendation system for the
investment decision-making process, this problem is detected and solved using the previous
three proposed approaches. Another problem in the previous work there is a lack of stock
recommendation systems that are not based on historical stock data analysis, so in the fourth
proposed system, we proposed a stock recommendation system for e-commerce that supports
the best recommendations without analysis of stock historical data based on stock and
investor retail history. The fourth proposed system, a hybrid mixed collaborative and contentbased recommendation system is proposed based on machine learning models and similarity
calculation techniques.