Intraday stock market prediction using LSTM and sentiment analysis

The dynamic and unpredictable nature of the stock exchange keeps day trading among the most complicated and profit-generating markets. The authors judge a hybrid methodology that combines technical analysis of stock price movement with sentiment analysis from financial news and social media outlets. Intraday trading is the buying and selling of stocks within one single day. It consists of a highly risky sector that sometimes offers better returns but always calls for quicker and more accurate predictions. This analysis setup entitled “Intraday Stock Market Analysis through LSTM and Sentiment Analysis” seeks to aid a trader's decision-making with a twofold approach: LSTM-based deep learning of real-time stock price data and sentiment analysis on real-time textual data. LSTM, a class of RNN, would be best suited in predicting time series due to its capability to learn and remember long-term dependencies in data. With the application of LSTM to historical intraday stock price data, the system learns subtle market trends and patterns, thereby producing better predictions of the near-future price movements.