Paper Info
Title
Stock investment decision support using an ensemble of L-GEM based on RBFNN diverse trained from different years
Abstract
Many researches attempt to find out regularities of the stock market so as to gain higher profit within stock investment activities. In fact, the price changes over time, but the data from previous period reflects the future trend in some extent. Thus, a new method is proposed to investigate how to use the historical data to make correct investment decision in this paper. Since the technical indicators are efficient tools on stock prediction, we use technical indicators of every trading day in current year as the input variables to train a RBFNN based on L-GEM and make prediction of trading actions (buy, sell or hold) for days in the next year. Then, a Multiple Classifier System (MCS) is built to make the final prediction from all base RBFNNs which trained by data in different years. Experimental results show that the new information from past stock data is predictable. Data in different years causes different effects on the future stock market. Accumulating all information from base RBFNNs brings a considerable profit in our experiments.
Year
DOI
Venue
2012
10.1109/ICMLC.2012.6358946
ICMLC
Keywords
Field
DocType
localized generalization error model,l-gem,radial basis function networks,radial basis function neural network,price changes,multiple classifier system,pattern classification,diverse trained rbfnn,stock markets,stock investment decision support,future stock market,decision support systems,stock investment,investment,mcs,pricing,time measurement,accuracy
Computer science,Decision support system,Stock prediction,Artificial intelligence,Classifier (linguistics),Stock market,Machine learning
Conference
Volume
ISSN
ISBN
1
2160-133X
978-1-4673-1484-8
Citations 
PageRank 
References 
2
0.38
8
Authors
2
Name
Order
Citations
PageRank
Xue-Ling Liang1131.48
Wing W. Y. Ng252856.12