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

Modelling and trading the Greek stock market with gene expression and genetic programing algorithms

This paper presents an application of the gene expression programming (GEP) and integrated genetic programming (GP) algorithms to the modelling of ASE 20 Greek index. GEP and GP are robust evolutionary algorithms that evolve computer programs in the form of mathematical expressions, decision trees or logical expressions. The results indicate that GEP and GP produce significant trading performance when applied to ASE 20 and outperform the well-known existing methods. The trading performance of the derived models is further enhanced by applying a leverage filter

A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption e ciency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence di ers from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.Ministerio de Economía y Competitividad DPI2015-70842-