للكاتبين :
Hassan Shaheen and Amir Atiya
Department of Computer Engineering Faculty of Engineering, Cairo University , Giza , EGYPT
ABSTRACT
This work deals with the problem of forecasting a complex environment behavior. The word
“complex behavior” is here used in the sense that the environment may be modeled as an
almost random walk environment. Stock market prediction is known to be such an
environment and is the case study of this work. The ultimate goal for human investors is to
build an intelligent tool for stock market prediction. The heart of any such tool is the
intelligent technique used for earning forecast. This paper presents a swarm based tool
tailored for stock market prediction. Stock market prediction is first formulated as a traveling
salesman problem that is solved using swarm optimization. The solution is used as the basic
indicators for prediction. Although empirical results are comparable to conventional statistical
and evolutionary based techniques, the technique is superior in terms of execution efficiency
and speed. In addition, the technique may be used in case of missing data and may be based
on simple raw data rather than more sophisticated financial indicators that may not be
available to the average novel investor.