Real stock trading using soft computing models

Abstract

The main focus of this study is to compare different performances of soft computing paradigms for predicting the direction of individuals stocks. Three different artificial intelligence techniques were used to predict the direction of both Microsoft and Intel stock prices over a period of thirteen years. We explore the performance of artificial neural networks trained using backpropagation and conjugate gradient algorithm and a Mamdani and Takagi Sugeno fuzzy inference system learned using neural learning and genetic algorithm. Once all the different models were built the last part of the experiment was to determine how much profit can be made using these methods versus a simple buy and hold technique.