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.
Recommended Citation
H. Tan et al., "Conservative Thirty Calendar Day Stock Prediction Using a Probabilistic Neural Network," Proceedings of the IEEE/IAFE 1995 Computational Intelligence for Financial Engineering, 1995, Institute of Electrical and Electronics Engineers (IEEE), Jan 1995.
The definitive version is available at https://doi.org/10.1109/CIFER.1995.495262
Meeting Name
IEEE/IAFE 1995 Computational Intelligence for Financial Engineering, 1995
Department(s)
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
text
Language(s)
English
Rights
© 1995 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 1995
