  Stock exchanges are considered major players in financial sectors of many
countries. Most Stockbrokers, who execute stock trade, use technical,
fundamental or time series analysis in trying to predict stock prices, so as to
advise clients. However, these strategies do not usually guarantee good returns
because they guide on trends and not the most likely price. It is therefore
necessary to explore improved methods of prediction.
  The research proposes the use of Artificial Neural Network that is
feedforward multi-layer perceptron with error backpropagation and develops a
model of configuration 5:21:21:1 with 80% training data in 130,000 cycles. The
research develops a prototype and tests it on 2008-2012 data from stock markets
e.g. Nairobi Securities Exchange and New York Stock Exchange, where prediction
results show MAPE of between 0.71% and 2.77%. Validation done with Encog and
Neuroph realized comparable results. The model is thus capable of prediction on
typical stock markets.
