| dc.description.abstract | Forecasting stock prices is of critical importance for investors who wish to reduce investment risks. Forecasting is based on the idea that stock prices move in patterns. So far, it is understood that developed, emerging, and frontier markets have different general characteristics. Subsequently, this research uses design of experiments (DOE) to study the significance and behavior of artificial neural networks' (ANN) design parameters and their effect on the performance of predicting movement of developed, emerging, and frontier markets. In this study, each classification is represented by two market indices. The data is based on Morgan Stanley Country Index (MSCI), and includes the indices of UAE, Jordan, Egypt, Turkey, Japan, and UK. Two designed experiments are conducted where 5 neural network design parameters are varied between two levels. The first model is a 4 factor full factorial, which includes the parameters of type of network, number of hidden layer neurons, type of output transfer function, and the learning rate of Levenberg-Marquardt (LM) algorithm. The second model, a 5 factor fractional factorial, includes all previous four parameters plus the shape of hidden layer sigmoid function. The results show that, for a specific financial market, DOE is a useful tool in identifying the most significant ANN design parameters. Furthermore, the results show that there exist some commonly significant and commonly insignificant factors among all tested markets, and sometimes among markets of the same classification only. However, there does not seem to be any differences in ANN design parameters' effect based on market classification; all main effects and interactions that appear to be significant behave similarly through all tested markets. Search Terms: Artificial neural networks (ANN), Design of experiments (DOE), Frontier, Emerging, Developed, Financial time series | en_US |
