An Experimental Study for Price Prediction of Stock Market using TFANN

The stock market attracts investor fraternity. It is associated with financial interest of an individual. Stock market is highly volatile in nature. The ups and downs are in stock market are generally event based. These events are generated due to several parameters, which might be independent or dependent on from one to another. Predominantly, prediction has remained one of the prime objectives of the probing, but how accurate, effective and sustainable those technological disruptions can be, particularly under the prevailing challenges of the time; researchers, scientists and engineers work tirelessly towards achieving the perfection. Only pen and paper based human efforts are not able to predicate elusive trends in stock market. As there exist various methodologies in Machine Learning to perform prediction in the stock market, the researcher in the present work has made an attempt to explore in time series forecasting, using python and analytics libraries to maximize profit margin. Since the fundamental and technical parameters affect the stock market in a great deal, implementation of the former in terms of historical time series data are applied to get the result. Therefore, in this research the researchers have explored a computational model that could determine how to maximize the profit margin in a stock market by applying the prediction model under Machine Mastering program.