Paper Info
Title
Rough Set Generating Prediction Rules for Stock Price Movement
Abstract
This paper presents rough sets generating prediction rules scheme for stock price movement. The scheme was able to extract knowledge in the form of rules from daily stock movements. These rules then could be used to guide investors whether to buy, sell or hold a stock. To increase the efficiency of the prediction process, rough sets with Boolean reasoning discretization algorithm is used to discretize the data. Rough set reduction technique is applied to find all the reducts of the data. Finally, rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. A comparison between the obtained results using rough sets with decision tree and neural networks algorithms have been made. Rough sets show a higher overall accuracy rates reaching over 97% and generate more compact rules.
Year
DOI
Venue
2008
10.1109/EMS.2008.89
EMS
Keywords
Field
DocType
compact rule,rough set,stock price movement,daily stock movement,prediction rules scheme,prediction process,boolean reasoning discretization algorithm,rough confusion matrix,rough set reduction technique,rough set generating prediction,rough sets dependency rule,rough set theory,data mining,predictive models,decision tree,pricing,neural network,classification algorithms,knowledge extraction,confusion matrix,set theory,artificial neural networks,neural networks,neural nets,decision trees,boolean functions,computational modeling
Boolean function,Decision tree,Data mining,Set theory,Confusion matrix,Computer science,Rough set,Knowledge extraction,Statistical classification,Dominance-based rough set approach
Conference
Citations 
PageRank 
References 
0
0.34
17
Authors
4
Name
Order
Citations
PageRank
Hameed Al-Qaheri1329.31
Shariffah Zamoon200.34
Aboul Ella Hassanien31610192.72
Ajith Abraham48954729.23