This report investigates how prediction of stock markets with Artificial Neural Networks (ANN) is affected by altering aspects of data quantities. A short-term and a long-term perspective considering time delays are examined. Inspired by neurosciences, ANNs have shown great potential in terms of recognising patterns in nonlinear systems. Existing research suggests that ANN is an eminent model to predicting stock markets due to its dynamical characteristics. Closing prices of large-caps within the sectors of IT and Telecommunication represented by the Swedish of OMX30 Stockholm (OMXS30), have been leveraged as data. The ANNs are implemented as multilayer feedforward networks, trained using supervised learning. To identify specific configurations, the models have undergone extensive testing by mean squared errors and statistical analysis. The results obtained suggest that the short-term perspective is optimally predicted for significantly small numbers of time delays, and that optimal configurations do not alter for increasing quantities of data. No significant conclusions could be drawn from the results for the long-term perspective.Key words: ANOVA, Backpropagation, Configurations, Stock Prediction, Artficial Neural Networks

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