本文为美国海军研究生学院（作者：Jason E. Kutsurelis）的硕士论文，共82页。

本文研究并分析了神经网路作为预测工具的使用方法。具体来说，测试了神经网络预测股票市场指数未来趋势的能力，并与传统的预测方法——多元线性回归分析法进行了比较。最后，利用条件概率计算模型预测正确的概率。

本研究在简要探讨神经网络理论的同时，确定了将神经网络作为个人投资者预测工具的可行性和实用性。这项研究建立在爱德华·盖特利在他的《金融预测的神经网络》一书中所做的工作之上。本研究证实了盖特利的工作，并描述了神经网络的发展，该网络正确预测市场上涨的概率为93.3%，预测标准普尔500指数下跌的正确概率为88.07%。最后得出的结论是，神经网络确实具有预测金融市场的能力，如果经过适当的训练，个人投资者可以从使用该预测工具中获益。

This research examines and analyzes the use of neural networks as a forecasting tool.  Specifically a neural network's ability to predict future trends of Stock Market Indices is tested. Accuracy is compared against a traditional forecasting method, multiple linear regression analysis. Finally, the probability of the model's forecast being correct is calculated using conditional probabilities. While only briefly discussing neural network theory, this research determines the feasibility and practicality of using neural networks as a forecasting tool for the individual investor. This study builds upon the work done by Edward Gately in his book Neural Networks for Financial Forecasting. This research validates the work of Gately and describes the development of a neural network that achieved a 93.3 percent probability of predicting a market rise, and an 88.07 percent probability of predicting a market drop in the S&P500. It was concluded that neural networks do have the capability to forecast financial markets and, if properly trained, the individual investor could benefit from the use of this forecasting tool.

1 引言

A 研究目标

2 文献回顾与理论框架

A 文献回顾

B 理论框架

3 算法设计

4 数据搜集

A 闭合网络

B 百分比网络

C 多元线性回归模型

D 条件概率

5 数据分析与解释

6 结论与未来研究建议

A 结论

B 未来研究建议 

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