Forecasting stock exchange movements using neural networks: Empirical evidence from Kuwait

Financial time series are very complex and dynamic as they are characterized by extreme volatility. The major aim of this research is to forecast the Kuwait stock exchange (KSE) closing price movements using data for the period 2001–2003. Two neural network architectures: multi-layer perceptron (MLP) neural networks and generalized regression neural networks are used to predict the KSE closing price movements. The results of this study show that neuro-computational models are useful tools in forecasting stock exchange movements in emerging markets. These results also indicate that the quasi-Newton training algorithm produces less forecasting errors compared to other training algorithms. Due to their robustness and flexibility of modeling algorithms, neuro-computational models are expected to outperform traditional statistical techniques such as regression and ARIMA in forecasting stock exchanges’ price movements.