Abstract
In recent years, many attempts have been made to predict the behavior of bonds, currencies, stocks, or stock markets. The Standard and Poors 500 Index is modeled using different neural network classification architectures. Most previous experiments used multilayer perceptrons for stock market forecasting. A multilayer perceptron architecture and a probabilistic neural network are used to predict the incline, decline, or steadiness of the index. The results of trading with the advice given by the network is then compared with the maximum possible performance and the performance of the index. Results show that both networks can be trained to perform better than the index, with the probabilistic neural network performing slightly better than the multi layer perceptron.
Recommended Citation
K. Schierholt and C. H. Dagli, "Stock Market Prediction Using Different Neural Network Classification Architectures," Proceedings of the IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering, 1996, Institute of Electrical and Electronics Engineers (IEEE), Jan 1996.
The definitive version is available at https://doi.org/10.1109/CIFER.1996.501826
Meeting Name
IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering, 1996
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Poors 500 Index; Financial Data Processing; Forecasting Theory; Maximum Possible Performance; Multilayer Perceptrons; Neural Net Architecture; Neural Network Classification Architectures; Pattern Classification; Probabilistic Neural Network; Probability; Stock Market Forecasting; Stock Market Prediction; Stock Markets
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 1996 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 1996
