Machine Learning and Artificial Neural Network Process – Viability and Implications in Stock Market Prediction

  T. Vanitha,  Dr. V. Thiagarasu

The modern world happenings are presented and stored in the form of different types of information which might not mean anything unless probed with a purpose. Data Mining is one of the methods wherein this hidden information can be extracted and disseminated to the objective. Due to the abundance of such information in different formats, manipulation of the same has also increased. In this paper, an attempt is done on predicting the movements in Indian stock market and an Indian stock market is a place where every type of investors – small and large – try to maximize their returns by understanding the price movements. But, their returns would be better if they know when the price would fall and increase. Prediction of these movements basically rests on ordinary linear regression. But after the advent of Artificial Intelligence, this has become easier by the application of Artificial Neural Network (ANN). Using different algorithms, the accuracy rate of prediction is enabled. This can be done with the help of the ANN tools and deep learning tools. In this research, the researcher made an attempt to predict the direction flow of the market using ANN tools and deep learning tools like Random Forest for the accuracy. Using BSE Sensex, the present study tries to predict the output and to find evidence to support the efficiency of the ANN. In this paper, both classification and regression are performed to find the prediction accuracy.

Keywords- Machine Learning, ANN, Deep Learning, Random Forest, Stock Market, Price Movements, Prediction.

Unique Identification Number - IJEDR1803112
Page Number(s) - 675-680
Pubished in - Volume 6 | Issue 3 | September 2018
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  T. Vanitha,  Dr. V. Thiagarasu,   "Machine Learning and Artificial Neural Network Process – Viability and Implications in Stock Market Prediction", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.6, Issue 3, pp.675-680, September 2018, Available at :http://www.ijedr.org/papers/IJEDR1803112.pdf