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 Computational Intelligence for Financial Engineering, 1995


Electrical and Computer Engineering

Keywords and Phrases

30 Day; 30-Day Stock Prediction; Conservative Strategies; False Alarms; False Predictions; Financial Data Processing; Forecasting Theory; Losses; Missed Opportunities; Neural Nets; Parameterization; Pattern Recognition; Preprocessing Techniques; Price Gains; Probabilistic Neural Network; Profit Opportunity; Short-Term Price Movement Prediction; Stock Markets; System Design; Uncertainty Handling

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





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

Publication Date

01 Jan 1995