Stock prediction with data mining techniques is one of the most important issues in finance being investigated by researchers
across the globe. Data mining techniques can be used extensively in the financial markets to help investors make qualitative
decision. One of the techniques is artificial neural network (ANN). However, in the application of ANN for predicting the
financial market the use of technical analysis variables for stock prediction is predominant. In this paper, we present a hybridized
approach which combines the use of the variables of technical and fundamental analysis of stock market indicators for prediction
of future price of stock in order to improve on the existing approaches. The hybridized approach was tested with published stock
data and the results obtained showed remarkable improvement over the use of only technical analysis variables. Also, the
prediction from hybridized approach was found satisfactorily adequate as a guide for traders and investors in making qualitative
decisions
