Forecasting Financial Markets Using Neural Networks

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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.


Jason E. Kutsurelis - Forecasting Financial Markets Using Neural Networks_ An An.pdf (415.12 KB, 需要: 30 个论坛币)

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1998年的paper，那时预测的准确度就能达到93.3%和88.07%，不知现在进展到什么程度了。哪位能推荐一两本这个领域的最新著作？

1998年的paper，那时预测的准确度就能达到93.3%和88.07%，不知现在进展到什么程度了。哪位能推荐一两本这个领域的最新著作？

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