Stock Market Prediction using Machine Learning Algorithms: A Classification Study

Abstract

Predicting the stock market has been an area of interest not only for traders but also for the computer engineers. Predictions can be performed by mainly two means, one by using previous data available against the stock and the other by analysing the social media information. Predictions based on previous data lack accuracy due to changing patterns in the stock market al.so, some fields might have been missed due to their insignificance in some stocks or unavailability of data. For example, some models may require `return rate' as a parameter for stock prediction, but the available data might not have it. On the other hand, a model predicting only on the basis of the return rate may find opening and closing price to be insignificant parameters. The data has to be cleansed before it can be used for predictions. This paper focuses on categorising various methods used for predictive analytics in different domains to date, their shortcomings. Further, the authors of this paper have suggested some improvements that could be incorporated to achieve better accuracy in these approaches.