An electronic financial system adviser for investors: the case of Saudi Arabia

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonFinancial markets, particularly capital and stock markets, play an important role in mobilizing and canalising the idle savings of individuals and institutions to the investment options where they are really required for productive purposes. The prediction of stock prices and returns is carried out in order to enhance the quality of investment decisions in stock markets, but it is considered to be tricky and complicates tasks as these prices behave in a random fashion and vary with time. Owing to the potential of returns and inherent risk factors in stock market returns. Various stock market prediction models and decision support systems such as Capital asset pricing model, the arbitrage pricing theory of Ross, the inter-temporal capital asset pricing model of Merton ,Fama and French five-factor model, and zero beta model to provide investors with an optimal forecast of stock prices and returns. In this research thesis, a stock market prediction model consisting of two parts is presented and discussed. The first is the three factors of the Fama and French model (FF) at the micro level to forecast the return of the portfolios on the Saudi Arabian Stock Exchange (SASE) and the second is a Value Based Management (VBM) model of decision-making. The latter is based on the expectations of shareholders and portfolio investors about taking investment decisions, and on the behaviour of stock prices using an accurate modern nonlinear technique in forecasting, known as Artificial Neural Networks (ANN). This study examined monthly data relating to common stocks from the listed companies of the Saudi Arabian Stock Exchange from January 2007 to December 2011. The stock returns were predicted using the linear form of asset pricing models (capital asset pricing model as well as Fama and French three factor model). In addition, non-linear models were also estimated by using various artificial neural network techniques, and adaptive neural fuzzy inference systems. Six portfolios of stock predictors are combined using: average, weighted average, and genetic algorithm optimized weighted average. Moreover, value-based management models were applied to the investment decision-making process in combination with stock prediction model results for both the shareholders’ perspective and the share prices’ perspective. The results from this study indicate that the ANN technique can be used to predict stock portfolio returns; the investment decisions and the behaviour of stock prices, optimized by the genetic algorithm weighted average, provided better results in terms of error and prediction accuracy compared to the simple linear form of stock price prediction models. The Fama and French model of stock prediction is better suited to Saudi Arabian Stock Exchange investment activities in comparison to the conventional capital assets pricing model. Moreover, the multi-stage type1 model, which is a combination of Fama and French predicted stock returns and a value-based management model, gives more accurate results for the stock market decision-making process for investment or divestment decisions, as well as for observing variation in and the behaviour of stock prices on the Saudi stock market. Furthermore, the study also designed a graphic user interface in order to simplify the decision-making process based upon Fama and French and value-based management, which might help Saudi investors to make investment decisions quickly and with greater precision. Finally, the study also gives some practical implications for investors and regulators, along with proposing future research in this area

A Data Model for Processing Financial Market and News Data in Electronic Financial System for Investors with Non- Financial Expertises: The Case of Saudi Arabia

In this paper, prediction model consists of two parts is presented. The first is three factors of the Fama and French model (FF) at the micro level to forecasting the return of the portfolios in Saudi Arabia Stock Exchange (SASE) and the second is Value Based Management (VBM) model of decision-making on the basis of expectations of shareholders and portfolio investors to take the investment decision and the behaviour of stoke price using an accurate modern technique in forecasting Artificial Neural Networks (ANN). This study examined monthly data relating to common stocks from the listed companies of Saudi Arabia Stock Exchange from January 2007 to December 2011. The results from this study indicate that ANN technique can be used in predicting the stock portfolios returns, the investment decision and the behaviour of stoke price

An Analysis of the Factors Affecting the Performance of Stock Markets in the GCC Region

This article analyses the factors affecting the performance of stock markets in the Gulf Council Countries (GCC) region. In order to tackle the aim, this study collected data from 3 major economies in the GCC countries (Saudi Arabia Qatar, and UAE) in the period between 2007 and 2020. Multiple regression analysis was used to test the effect of interest rate, inflation, exchange rate and foreign direct investment (FDI) on stock market index. The findings indicated that both exchange rate and FDI have substantially impacted the stock market performance in the three countries within the GCC. However, both interest rate and Inflation has negative effect on the performance of stock markets. Keywords: stock markets, GCC, FDI, interest rate, inflation, exchange rate. DOI: 10.7176/EJBM/15-6-01 Publication date:March 31st 202

The Role of Corporate Governance, Risk Behaviour, and Capital Structure on Financial Performance of Companies: Evidence from GCC Countries

This study examined the role of corporate governance, risk behaviour, and capital structure on financial performance of list companies within Gulf Council Countries. This is done through investigating the influence of three independent variables of (corporate governance, risk behaviour, and capital structure ) over the dependent variable of financial performance. Data collected from 230 companies between 2016 and 2021 from Saudi Arabia, Kuwait, Qatar, United Arab Emirates, and Bahrain, and Oman. The findings suggested that CEO duality, size of the board, Debt to Equity (DTE) rationale and Debt to Asset (DTA) ration negatively impacted firm performance. However, the findings indicated that the audit committee positively influenced financial performance of companies.Keywords: GCC, Corporate Governance , Risk Behaviour, And Capital Structure , financial performance DOI: 10.7176/RJFA/14-4-01 Publication date: February 28th 202