Profitable Ranking of Stock Market Organizations Using Different Machine Learning Models

The prediction of a stock exchange price is immensely challenging and needful as it can provide a guideline to the investors and organizations. However, stock exchange data prediction has never been an easy task for noisiness, time fluctuating, and dynamic nature of the data. The aim of this work is to predict future data of stock exchange by using four different machine learning models and make a ranking of the sequence for various organizations' stock. Using four various Machine learning models: ARIMA, Prophet, LSTM, and GRU, data are analyzed and used for future prediction. For this, eight years of historical data of Dhaka Stock Market were collected and the Closing price, Market Value, Fluctuate Range and Opening price were observed. Finally, observing the accuracy and error rate of the models this work also presented a profit base ranking of different organizations.