In this research, three variance ratio tests: the standard variance ratio test, the wild bootstrap multiple variance ratio test, and the non-parametric rank scores test are adopted to test the random walk hypothesis (RWH) of stock markets in Middle East and North Africa (MENA) region using most recent data from January 2010 to September 2012. The empirical results obtained by all three econometric tests show that the RWH is strongly rejected for Kuwait, Tunisia, and Morocco. However, the standard variance ratio test and the wild bootstrap multiple variance ratio test reject the null hypothesis of random walk in Jordan and KSA, while non-parametric rank scores test do not. We may conclude that Jordan and KSA stock market are weak efficient. In sum, the empirical results suggest that return series in Kuwait, Tunisia, and Morocco are predictable. In other words, predictable patterns that can be exploited in these markets still exit. Therefore, investors may make profits in such less efficient markets.

Autoregressive moving average (ARMA) process and dynamic neural networks namely the nonlinear autoregressive moving average with exogenous inputs (NARX) are compared by evaluating their ability to predict financial time series; for instance the S & P500 returns. Two classes of ARMA are considered. The first one is the standard ARMA model which is a linear static system. The second one uses Kalman filter (KF) to estimate and predict ARMA coefficients. This model is a linear dynamic system. The forecasting ability of each system is evaluated by means of mean absolute error (MAE) and mean absolute deviation (MAD) statistics. Simulation results indicate that the ARMA-KF system performs better than the standard ARMA alone. Thus, introducing dynamics into the ARMA process improves the forecasting accuracy. In addition, the ARMA-KF outperformed the NARX. This result may suggest that the linear component found in the S & P500 return series is more dominant than the nonlinear part. In sum, we conclude that introducing dynamics into the ARMA process provides an effective system for S & P500 time series prediction.

This paper investigates the dynamics of the NASDAQ topology before, during, and after 2008 financial crisis. First, multiresolution analysis by virtue of wavelet transform is employed to denoise each NASDAQ sector return series. Second, the correlation matrix of sectors is built and analyzed in each time period to view comovements of sectors. Third, hierarchical clustering trees are constructed in each time period to find out how the structure of the NASDAQ market evolves through time. Our results suggest that interrelationships between sectors become stronger in times of crisis and especially in post-crisis period. In addition, some markets tend to form the same cluster in all time periods; for instance the Industrial and Bank sectors and the Telecommunication and Computer sectors. However, the general topology of the NASDAQ market has been considerably changed over periods. In sum, the complex structure of the NASDAQ market is dynamic and is more integrated after 2008 financial crisis. This result indicates that there are less diversification opportunities in the post-crisis period in comparison with pre-crisis period. These empirical findings are important for the development of subsequent portfolio strategies.

In this study, the backpropagation neural network (BPNN) is tested for the ability to forecast the daily volatility of two stock market indices from the Middle East and North Africa (MENA) region using volume; namely Morocco and Saudi Arabia. Volatility series were estimated using the Exponential Auto-Regressive Conditional Heteroskedasticity (EGARCH) model. The simulation results show that trading volume helps improving the forecasting accuracy of BPNN in Morocco but not in Saudi Arabia. As a result, volume represents valuable information flow to be used in the modeling and prediction of volatility in Morocco. In addition, it is found that BPNN overpredicts volatility during high volatile periods. This finding is important in financial applications such as asset allocation and derivatives pricing.

This study employs the generalized autoregressive conditionally heteroskedastic in the mean (GARCH-M) methodology to investigate the return generating process of Jordan, Kingdom of Saudi Arabia (KSA), Kuwait, and Morocco stock market indices. The tradeoff between returns and the conditional variance is found to be positive in all markets. In other words, the empirical findings show that investors are rewarded for their exposure to more risk in these financial markets. This result is consistent with both financial theory and empirical finance.

The purpose of this paper is to predict the S & P500 down moves with technical analysis indicators using learning vector quantization (LVQ) neural networks and probabilistic neural networks (PNN). In addition, entropy-based input selection technique is employed to improve the prediction accuracies. The out-of-sample simulations show that LVQ outperforms PNN. In addition, the Entropy-LVQ system achieved higher accuracy in comparison with the literature.

Crude oil price volatility dynamics are governed by nonlinear and chaotic behaviour. This paper presents and compares the performance of four hybrid systems used to estimate and predict crude oil price volatility data. A GARCH family model is employed to estimate oil price volatility data and the Elman artificial neural network (ENN) system is used to model and predict the obtained data. Indeed, unlike previous studies found in the literature, recurrent artificial neural networks are chosen in this paper to model and predict future crude oil price volatility data estimated by GARCH family models since they are nonlinear systems capable of learning noisy and nonstationary data. In particular, four hybrid systems are tested and compared; including the GARCH-ENN, EGARCH-ENN, APARCH-ENN, and TARCH-ENN system. Using Brent crude oil price data, the obtained out-of-sample simulation results indicate that all hybrid systems provide very accurate forecasts of Brent future volatility. In addition, they show evidence of the superiority of the GARCH-ENN system over the EGARCH-ENN, TARCH-ENN, and APARCH-ENN systems. The presented four hybrid systems achieved very low forecasting errors. Thus, they could be effective in oil industry management and applications.

In financial industry, the accurate forecasting of the stock market is a major challenge to optimize and update portfolios and also to evaluate several financial derivatives. Artificial neural networks and technical analysis are becoming widely used by industry experts to predict stock market moves. In this paper, different technical analysis measures and resilient back-propagation neural networks are used to predict the price level of five major developed international stock markets, namely the US S & P500, Japanese Nikkei, UK FTSE100, German DAX, and the French CAC40. Four categories of technical analysis measures are compared. They are indicators, oscillators, stochastics, and indexes. The out-of-sample simulation results show a strong evidence of the effectiveness of the indicators category over the oscillators, stochastics, and indexes. In addition, it is found that combining all these measures lead to an increase of the prediction error. In sum, technical analysis indicators provide valuable information to predict the S & P500, Nikkei, FTSE100, DAX, and the CAC40 price level.