Published Paper Details:

Predicting the stock market is one of the most difficult tasks in the field of computation. Physical vs. physiological elements, rational vs. irrational conduct, investor sentiment, market rumours, and other factors all play a role in the prediction. All of these factors combine to make stock values extremely volatile and difficult to anticipate accurately. In this arena, we look into data analysis as a game-changer. When all information about a company and stock market events is promptly available to all stakeholders/market participants, the impacts of those events are already embedded in the stock price, according to efficient market theory.. So, it is said that only the historical spot price carries the impact of all other market events and can be employed to predict its future movement. Hence, considering the past stock price as the final manifestation of all impacting factors we employ Machine Learning (ML) techniques on historical stock price data to infer future trends. ML approaches have the potential to uncover previously unseen patterns and insights, which can then be used to generate imprecise predictions. We propose a framework using the LSTM (Long Short Term Memory) model and companies' net growth calculation algorithm to analyze as well as predictions of future growth of a company.