Stock Price Prediction using Dynamic Neural Networks

TL;DR

This paper explores the use of dynamic neural networks for predicting stock prices, demonstrating their ability to outperform traditional methods and challenging the Efficient Market Hypothesis.

Contribution

It introduces a time series dynamic neural network model for stock prediction and compares its performance with existing fundamental, technical, and regression techniques.

Findings

Neural networks outperform traditional stock analysis methods.

The paper refutes the Efficient Market Hypothesis using neural network analysis.

Supports Chaos theory over EMH in stock price prediction.

Abstract

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.