|Project Description: ||The goal of this project was to predict stock trends based on data from a financial market. This project originated from two ideas: that the human brain has a well-defined structure; and that a financial market has a state and some rule of evolution. The system in this project has two neural networks, which use a genetic algoritm to learn concepts. Neural networks and genetic algorithms are two different optimization methods, which may be used, either seperately or together, in many applications where other methods have less success. The assumption was that at a moment of time, two things are known: the value of the data series at that time; and the state of the market given by its history. These values acted as variables of input for two neural networks: one predicted the next value of the data series; and the other predicted the next state of the market. The neural network system was tested upon various stocks where predicted trend line slpes were compared to actual trend line slopes. The overall results were good and showed that the accuracy of the predictions depended on parameter settings of the genetic algorithm.