Title :
Self-organizing neural network system for trading common stocks
fDate :
27 Jun- 2 Jul 1994
Abstract :
A fully automatic common stock trading system has been developed. The system takes in daily price and volume data on a list of 200 stocks and 10 market indexes. A chaos based modeling procedure is then used to construct alternate price prediction models based on technical, adaptive, and statistical models. A self-organizing neural network is used to select the best model for each stock or index on a daily basis. A second self-organizing network is then used to to make a short-term gain-lose prediction from each model. These predictions are combined in a trade selection module to generate buy-sell-hold recommendations for the entire list of stocks on a daily basis. Finally, the trading recommendations are combined by a portfolio management utility to produce a set of risk-reward ranked alternate portfolios
Keywords :
finance; self-organising feature maps; stock markets; buy-sell-hold recommendations; chaos based modeling procedure; common stocks; portfolio management; price prediction models; risk-reward ranked alternate portfolios; self-organizing neural network system; short-term gain-lose prediction; statistical models; trade selection module; trading; trading recommendations; Chaos; Economic forecasting; History; Neural networks; Orbits; Organizing; Portfolios; Power system modeling; Predictive models; Risk management;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374924
