Stock Price Forecasting Using LSTM Model

This research work incorporates historical data and various predictive models to forecast the future prices of stocks traded in financial markets based on factors such as market trends, company performance, economic indicators, and external events. This work introduces a new approach for the prediction of stock market using Long Short-Term Memory (LSTM) type of networks. It enhances the ability of model to recognize patterns and temporal connections in historical stock price data by incorporating multiple layers of LSTM units. The model can effectively learn both short-term and long-term dependencies, helping to adapt to the volatile nature of these financial markets. Rigorous experiments validate the efficacy of this stacked LSTM-based approach in predicting and understanding stock market trends. Once the models are trained and validated, it is used to make predictions on unseen or future data, generating forecasts of stock prices alongside measures of uncertainty or confidence intervals. The accuracy of the model has been increased to 98.2% using the proposed technique. Sentiment Analysis has been incorporated to include the effects of news articles etc. as positive, negative or neutral sentiment. In order to continue to explore the possibilities of machine learning in financial forecasting, our findings pave the way for further advancements in predictive modelling and risk management strategies.