Decision Support Systems - Special issue: Data mining for financial decision making
Knowledge Discovery with SOM Networks in Financial Investment Strategy
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
DKAS: a distributed knowledge acquisition system in a DSS
Journal of Management Information Systems - Special section: Research in integrating learning capabilities into information systems
An Empirical Analysis of Data Requirements for Financial Forecasting with Neural Networks
Journal of Management Information Systems
Intelligent technical analysis based equivolume charting for stock trading using neural networks
Expert Systems with Applications: An International Journal
The use of data mining and neural networks for forecasting stock market returns
Expert Systems with Applications: An International Journal
IEEE Transactions on Neural Networks
Agent-based computational investing recommender system
Proceedings of the 7th ACM conference on Recommender systems
Hi-index | 12.05 |
This paper presents a new model to do stock market timing on the basis of a supervised feed-forward neural network and the technical analysis of Japanese Candlestick. In this approach the network is not going to learn the candlestick lines alone or in combination, but is to present a kind of regression model whose independent variables are important clues and factors of the technical analysis patterns; and its dependent variable is the market trend in near future. In defining the independent variables two approaches are taken; one is Raw data-based and the other is Signal-based with fifteen and twenty-four variables, respectively. Experimental results, in which estimated signals are compared with actual events according to real published daily data in Yahoo.finance, showed that the proposed model performs brilliantly well in emission of buy and sell signals while the first approach seems to some extent better then the second.