Browsing by Author "1421274042"

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    Stock Market Price Forecasting Service using LSTM
    (North-south University, 2019-04-30) Md. Mahabubul Hasan; Pritom Roy; Sabbir Sarker; Dr. Mohammad Monirujjaman Khan; 1421274042; 1430378042; 1420134042
    In recent years we’ve seen in Bangladesh that the Stock market is not stable at all. In 2011 Stocks continued to tumble amid jitters over banks' liquidity crisis. After starting the day at 5,710, DSEX, the benchmark index of the Dhaka Stock Exchange, plunged to below 5,700 points in less than half an hour. Eventually, it lost 81.92 points to close the day at 5,623.64. People invest a lot in stock market. Many people just lost their hard-earned money in a blink of an eye because of investing at the wrong place. But we’ve seen the vice versa situation from the stock market as well. Stockify comes in play here. Stockify is dedicated to show accurate predicated prices of market shares. 70% accuracy have been achieved via training the algorithm Using LSTM which is an artificial neural network. Deep learning algorithm library TensorFlow have been implemented to show us predicted price using the closing prices of a day. Currently we are providing free service via our website and application. It would be on subscription based when the site will be more enriched in a business level. Thus, we hope to help people to make a profit in stock market and upraise the stock market of Bangladesh.
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    Open Access
    The Prediction of Stock Market Using Recurrent Neural Network
    (North South University, 2021) Sadman Bin Islam; Mohammad Mahabubul Hasan; Mohammad Monirujjaman Khan; 1611957042; 1421274042
    Stock price forecasting is becoming increasingly popular recently in the financial realm. Shares price prediction is important for increasing the interest of speculators in putting money in a company's stock in order to grow the number of shareholders in the stock. Successfully predicting the future price of a stock could result in a sizable return. When it involves forecasting, various methodologies are used. This report uses a replacement stock price prediction framework is proposed utilizing a well-liked model which is Recurrent Neural Network (RNN) model i.e., Long Short-Term Memory (LSTM) model. It is often shown from the simulation results that utilizing these RNN models, i.e., LSTM, and with proper hyper-parameter tuning, the proposed scheme can forecast future stock trend with high accuracy. The RMSE for LSTM model was measured by varying the number of epochs, difference between predicted stock price and actual stock price. The model is trained and evaluated for accuracy with various sizes of knowledge. The assessments are conducted by utilizing a freely accessible dataset for stock markets having date, volume, open, high, low, and closing prices. The major goal of this article is to determine to what degree a Machine Learning algorithm can anticipate the stock market price with greater accuracy.