Title

Conservative Thirty Calendar Day Stock Prediction Using a Probabilistic Neural Network

Abstract

We describe a system that predicts significant short-term price movement in a single stock utilizing conservative strategies. We use preprocessing techniques, then train a probabilistic neural network to predict only price gains large enough to create a significant profit opportunity. Our primary objective is to limit false predictions (known in the pattern recognition literature as false alarms). False alarms are more significant than missed opportunities, because false alarms acted upon lead to losses. We can achieve false alarm rates as low as 5.7% with the correct system design and parameterization.

Meeting Name

IEEE/IAFE 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr) (1995: Apr. 9-11, New York, NY)

Department(s)

Electrical and Computer Engineering

International Standard Book Number (ISBN)

0000780321456

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 1995 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.


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