In this study we combine support vector machine (SVM) and windowing operator in order to predict share market trend as well as the share price. The instability of the time series data is one of the main reasons to lead to decrease of prediction accuracy in this analysis. On the other hand, some special SVM parameters such as c,
, g should be carefully determined to gain high accuracy. In order to solve this problem mentioned above we use windowing operator as preprocess in order to feed the highly reliable input to SVM model. And train the model in iterative process such that we can find out the best combination of SVM parameters. This study is done on some listed company of Dhaka stock exchange (DSE), Bangladesh. And the training and testing data sets are real time values are collected from DSE. Four years historical data (2009-2012) are used in this analysis. And finally, we compare the output with the real time trend from DSE.