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A Novel Method to Improve Model fitting for Stock Market Prediction

Sukriti Jain, Samarth Gupta, Amarjot Singh

About The Authors

Sukriti Jain
Dept of Electronics and Communication Engineering, Ambedkar Institute of Advanced Communication technologies and research, GGSIPU, India

Samarth Gupta
Dept of Mechanical and Production Engineering, IIT Rookee, India

Amarjot Singh
Dept of Engineering, Simon Fraser University, Burnaby, Canada


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%.



Stock market; Gaussian Fitting; Linear Models; Technical indicators; Istanbul Stock Exchange Dataset

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IJRBT introduces peer-review from its first Edition onwards. The researchers submitting their papers for publication should review atleast one technical paper from their domain. The manuscript also undergoes mandatory procedural review with IJRBT review and scholar panel.