A Novel Method to Improve Model fitting for Stock Market Prediction

Abstract

Forecasting the trends of stock market is of extreme importance and profitable stock market traders and also to the researchers who are always trying to find an analogy to describe the behaviour of stock market. Various data mining techniques have been implemented in the recent past to predict the behaviour of stock market.  Every method tries to fit a model to the training data to predict the future. As it is obvious the accuracy of prediction depends on the model fitting. We propose a linear model for stock market prediction and further elaborate on improving the fit of the model. The proposed model and the correction method are tested on Istanbul stock exchange. The proposed model fits the dataset with an average error of 12% which is corrected by proposed method to average of 6%.