A neurofuzzy arbitrage simulator for stock investing

We study the success of a neural network computer model to predict the price, of a stock, given the fluctuations in the rest of the market that day. Based on the neural net´s prediction, the program then measures its success by simulating buying or selling that stock, based on whether the market´s price is determined over valued or under valued. The neural net itself is a modification of a fuzzy based neural network (Kung, 1993). The program consistently averages over 20% A.P.R. and has been time tested over 6 years with several stocks