Artificial neural network based stock value prediction

Financial market turmoil is a new normal today and modern computer science is partialy responsible for this. In this paper we try to show that modern fincancial markets are informationally efficent. In order to show this attribute of financial markets we use a neural network and test it against the S & P 500 stock index. We train our neural network using index information from past in order to predict the future value of the index. We compare the results of neural network based index prediction and a simple buy & hold strategy. Based on this comparison we make a decision about validity of the market efficiency hypothesis. Finally we present some possible improvements to our solution of this problem.