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.

翻译：本文分析并实现了一种时间序列动态神经网络，用于预测每日收盘股票价格。神经网络在识别混沌、非线性和看似随机数据中的潜在模式方面具有无与伦比的能力，从而提供了一种比当前许多技术更为精确地预测股票价格走势的机制。本文讨论了包括基本面分析、技术分析和回归分析方法在内的现代股票分析技术，并将其与神经网络的性能进行了对比。此外，本文介绍了有效市场假说（EMH），并运用神经网络将其与混沌理论进行了对照研究。本文将对有效市场假说（EMH）提出反驳，并支持混沌理论。最后，本文提出了在股票价格预测中使用神经网络的相关建议。