Research Area: Machine Learning
In the financial world, the forecasting of stock price gains significant attraction. For the growth of shareholders in a company-s stock, stock price prediction has a great consideration to increase the interest of speculators for investing money to the company. The successful prediction of a stock-s future cost could return noteworthy benefit. Different types of approaches are taken in forecasting stock trend in the previous years. In this research, a new stock price prediction framework is proposed utilizing two popular models; Recurrent Neural Network (RNN) model i.e. Long Short Term Memory (LSTM) model, and Bi-Directional Long Short Term Memory (BI-LSTM) model. From the simulation results, it can be noted that using these RNN models i.e. LSTM, and BI-LSTM with proper hyper-parameter tuning, our proposed scheme can forecast future stock trend with high accuracy. The RMSE for both LSTM and BI-LSTM model was measured by varying the number of epochs, hidden layers, dense layers, and different units used in hidden layers to find a better model that can be used to forecast future stock prices precisely. The assessments are conducted by utilizing a freely accessible dataset for stock markets having open, high, low, and closing prices.
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Author(s) Name: Md. Arif Istiake Sunny; Mirza Mohd Shahriar Maswood; Abdullah G. Alharbi
Journal name:
Conferrence name: 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)
Publisher name: IEEE
DOI: 10.1109/NILES50944.2020.9257950
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