Visualization and forecasting of stock’s closing price using machine learning

Stock market investments have become an essential part of our lives as they offer a means of growing wealth and securing financial stability for individuals and businesses alike. However, predicting and investing in a stock is a complex method and demands significant levels of knowledge, proficiency, and skill. Stock market prediction is the act of analyzing historical information and trends in the market in order to make informed forecasts about the potential future worth of a given stock or instrument that is traded on a financial instrument exchange. When making stock predictions, most stockbrokers use both fundamental and technical analysis and time series analysis. The study focuses on the implementation of Multi-Linear Regression, LSTM, CNN, and LSTM + GRU based Machine learning techniques using technical analysis to predict stock’s closing values from the NYSE, and NASDAQ markets for multiple days. The dataset has been taken from Yahoo, of 10-year span. The factors taken into consideration for predicting stock prices are open, close, low, and high. The model’s effectiveness is measured using common strategic metrics like RMSE, MSE, MAE, and R2. A lower value for these variables suggests that the models are good at forecasting stock closing prices. After conducting a comprehensive evaluation, we found that LSTM + GRU model performs the best among the tested models for predicting multiple days, followed by CNN and LSTM. The tested models demonstrate a remarkable level of accuracy in predicting stock market prices. This research work provides a valuable contribution to the fields of financial and technical analysis in the stock market research community.