Stock Price Prediction using Dynamic Neural Networks
David Noel
This paper will analyze and implement a time series dynamic neural network to
predict daily closing stock prices. Neural networks possess unsurpassed
abilities in identifying underlying patterns in chaotic, non-linear, and
seemingly random data, thus providing a mechanism to predict stock price
movements much more precisely than many current techniques. Contemporary
methods for stock analysis, including fundamental, technical, and regression
techniques, are conversed and paralleled with the performance of neural
networks. Also, the Efficient Market Hypothesis (EMH) is presented and
contrasted with Chaos theory using neural networks. This paper will refute the
EMH and support Chaos theory. Finally, recommendations for using neural
networks in stock price prediction will be presented.