Stock Price Prediction using Dynamic Neural Networks
This addresses stock market prediction for investors, but appears incremental as it applies existing neural network methods to a known problem without new data or major breakthroughs.
This paper tackles stock price prediction by implementing a dynamic neural network to analyze daily closing prices, claiming it provides more precise predictions than current techniques like fundamental or technical analysis. It also refutes the Efficient Market Hypothesis and supports Chaos theory using neural networks.
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.