Neural Network Predictions of Stock Price Fluctuations

The goal of this paper is to create a hybrid system based on a Multi-Agent Architecture that will investigate the evolution of some neural network methods along with technical and fundamental analysis methods on stock market indexes and how this information influences the stock market behavior in order to improve the profitability of a short or medium time period investment. The proposed system compares the results of Standard Feed Forward Neural Network, Elman and Jordan Recurrent Neural Networks and a Neural Network evolved with NeuroEvolution of Augmenting Topologies (NEAT) in order to investigate which network gives the most accurate result and time performance by taking in consideration the close price of a stock. The system also finds correlations between the pattern recognition methods and technical and fundamental methods results in order to find the direction of the market trend, to predict the next day price of a stock and to trigger a useful buy/sell signal. We are also interested in finding a correlation between the evolution of price, volume, number of transactions in order to have a better view on which is the effect of stock liquidity on a stock price. In order to validate our model a prototype was developed and applied to the Bucharest Stock Exchange Market indexes.