Stock Market Prediction using Recurrent Neural Network’s LSTM Architecture

Abstract

Stock market price prediction is a difficult undertaking that generally requires a lot of human-computer interaction. The stock market process is fraught with risk and is influenced by a variety of factors. Of all the market sectors, it is one of the most volatile and active. When buying and selling stocks from various corporations and businesses, more caution is required. As a result, stock market forecasting is an important endeavor in business and finance. This study analyzes one of the explicit forecasting tactics based on Machine Learning architectures and predictive algorithms and gives an independent model-based strategy for predicting stock prices. The predictor model is based on the Recurrent Neural Networks' LSTM (Long Short-Term Memory) architecture, which specializes in time series data classification and prediction. This model does rigorous mathematical analysis and estimates RMSE to improve forecast accuracy (Root Mean Square Error).All calculations and performance checks are done in Python 3. A number of machine learning libraries are used for prediction and visualization. This study demonstrates that stock performance, sentiment, and social data are all closely related to recent historical data, and it establishes a framework and predicts trading pattern linkages that are suited for High Frequency Stock Trading based on preset parameters using Machine Learning.