الفهرس 
Chapter One: Research FrameworkOnly 14 pages are availabe for public view
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Abstract
Stock Market is very volatile. Prices of stocks change almost instantly. Financial analysts who purchase stocks are unaware of all the factors that affect stock prices and help in prediction. In addition to that, they need an idea of which stocks to invest in and sell. Stockbrokers can easily manipulate them. Stock prices depend on news appearing in news articles. An average buyer can only analyze a small amount of information.
Because of the volatile nature of stock prices, there is always an urgent need to predict the future movement of stock prices. Many factors interact in the stock market, including political factors, Macroeconomic factors, financial variables, and traders’ expectations. Analyzing the relationship between political, macroeconomic, and financial variables and stock market prices, returns, and volatility has gained a lot of interest from both academics and businesses. At the same time, the evolution of big data analytics has created a set of new factors that may affect Stock exchange markets.
Macroeconomic variables and social media variables, in addition to Google Trend variables have a significant effect on Stock Market return and volatility, which means that it can be used to forecast the Stock market return and volatility. The prediction of stock market volatility using macroeconomic variables has been widely studied. However, the concept of predicting stock market returns and volatility depending on social media variables and Google trends as parts of big data are new and introduced only in the last few years especially in the emerging Stock exchange markets in Middle East and North Africa (MENA).
This research aims to determine the effect of macroeconomic variables, social media variables, and Google trends variables on Stock market return, stock market price, and return volatility.
To realize this objective, stock market indices data for MENA countries’ stock market exchanges in addition to social media and Google trends data, was collected. Panel Data was chosen because it allows the analysis of multiple data points across time.
As panel data is collected, panel data analysis will quantify the effect of macroeconomics, Google trends, and social media variables on stock market price, return, and volatility.
As the Unit Root test confirms the stationary of research variables, Ordinary least squares (OLS) will be applied. To estimate ordinary most miniature regression model, three different techniques can be used; first the pooled OLS, second the fixed effects model which allows controlling for unobserved heterogeneity across countries and third Random Effect model.
The current study shows the existence of a significant effect for inflation rate and GDP on stock market return and stock market price volatility. In addition, the results of the current study reveal the existence of significant effect for Google search volume on stock market volatility. Meanwhile, the effect of GSV on stock market return and its volatility appears weak and insignificant.
The current study also shows that social media variables do not significantly affect stock market return and volatility. On the other hand, the effect of negative mention on stock market prices appears to be negative and significant, which means that investors may be more sensitive to negative than positive ones.
The study recommends that investors analyze macroeconomic variables trends, especially inflation and exchange rate, before investing in stock exchange markets. It is also recommended that policymakers introduce new strategies to control inflation rates and stabilize exchange rates to maintain the health of the stock exchange market.
The study also recommends that investors pay attention to Google trends and social media variables when analyzing and predicting stock market trends and volatility.