Hazim El-Baz

Competency Domain Model and the Perception of Engineering Managers in the United Arab Emirates

Abstract: Because of the multidimensional nature of their job, defining the Engineering Manager (EM) competencies is of particular interest. Todays global business environment and increasingly changing workplace dictate that the EM must cultivate a wider range of competencies. In this paper, a comprehensive competency model is presented. The model combines the views of previous researches and views of EM academicians and experienced practitioners. The paper also presents the results of surveying EMs in the UAE and reports their perception of the relative importance of EM competencies using the AHP methodology. The results indicate that practicing EMs in the UAE perceive the Leadership and Interpersonal competencies to be the most important among all other competencies in the developed model.

Employee competency maturity model and its application in global software outsourcing

Global Software Outsourcing (GSO) is a complex process. Success often depends on a host of diverse competencies. This paper presents a model for specifying personnel competencies that adequately capture the competency dimensions of the Job-Focus, Role-Focus, and Person-Focus. In addition, the maturity levels of these competencies in each dimension can be specified at one of five levels. Employee Competency Maturity Model (ECMM) is illustrated by applying it to competency descriptions embedded in SCRUM, DSDM and OpenUP process descriptions. ECMM provides a foundation for management of employee competencies, based on which firms may make strategic outsourcing decisions in the future.

An Epsilon Constraint Method for selecting Indicators for use in Neural Networks for Stock Market Forecasting

Abstract Forecasting future moves of stock markets has been and always will be of great interest to researchers and practitioners. This paper proposes a multi-objective programming methodology to select the optimum technical indicators to be used as input in a Neural Network (NN) in order to predict stock market prices. A new mathematical model will be proposed which involves objective functions and constraints to filter out the noisy signals and maximize the prediction power. The 0–1 multi-objective model aims to select the indicators maximizing the covariance of the indicators with the output of the NN while minimizing the covariance among the indicators themselves. The Multi-objective model is transformed via the Epsilon Constraint technique. Many efficient configurations of indicators for different values of epsilon are evaluated and their resulting errors are presented. Our approach provides a systematic methodology in order to choose the variables that significantly affect price movements. The methodology is applied on the NIKKEI225 stock market index.

Multi-input single-output (MISO) random system modeling using methods of system identification

The paper utilizes techniques commonly used in the system identification dynamic systems behavior using output-input data to an unknown dynamic system. The identification techniques are based on nine inputs and one output. The system is applied to a financial time series that represent the historical prices of gold. The nine inputs are the technical indicators calculates form the historical data of open, high, low, close, and volume of trading the gold while the output is the forecasted value of the closing price of gold. Nonlinear Identification techniques used in this paper include wavelet Network, Sigmoid Network and Tree Partition. The purpose of the identification techniques is come up with a dynamic system model “either a transfer function or State-Space model” that is capable of predicting the values of the output “close”. The data is split into estimation set and verification set. The estimation group is used in determining the best possible model that can predict the verification set of data. The highest match obtained was 92%. Details on the modeling techniques as well as the effect of each input on the output are also presented in this paper. Simulation results are utilized to examine the accuracy and integrity of the model proposed.

Neural Network design parameters for forecasting financial time series

Neural Networks (NN) have been used extensively by researchers and practitioners to forecast financial time series. The forecasting accuracy of NN depends on several design parameters, and fine-tuning them to suit a particular financial time series is essential for attaining lower error levels and minimizing running time. This paper presents the results of a two-level full-factorial Design of Experiment developed to investigate the significant factors that influence the performance of NN in forecasting financial time series. The factors considered in this paper are NN type, number of neurons in the hidden layer, the learning rate of LM algorithm, and the type of output layer transfer function. The methodology is applied to the Morgan Stanley Capital International Index for United Arab Emirates.

SMA and MACD combinations for stock investment decisions in frontier markets: evidence from Dubai financial market

One of the most challenging financial decisions is when to buy and sell stocks. Frontier markets offer high profit opportunities but also have high risk. Consequently, technical analysis is used to assist in properly timing entry and exit points from stock trades. Previous research presented applications of technical analysis in developed and emerging markets since they, unlike frontier markets, exist in an environment of political stability, regulations, and liquidity. This paper shows how trade signals generated from Simple Moving Average (SMA) confirmed by Moving Average Convergence Divergence (MACD) can be used to minimise trading risk in frontier markets such as Dubai Financial Market (DFM). The results show that the standard time-periods for SMA and MACD do not apply well to frontier markets and that trade signals generated from SMA and confirmed by signals generated from medium to long-term MACD or vice versa result in excellent hit ratios.