A Novel Machine Learning Algorithm to Assist Traders and Investors in Forecasting Stock Market Launches

The evaluation of potential machine learning techniques for stock value forecasting in this paper focuses specifically on the long short-term memory (LSTM). Recurrent Neural Network. The volatility of prices for stocks as an outcome of fluctuating demand as well as supply makes it harder to forecast them with accuracy. Through the use of trends in historical data, machine learning algorithms are able to identify the exact characteristics of a stock price. The LSTM method is very useful for time-series forecasting because of its ability to recall data over extended periods of time. One more advantage of using LSTMs for time-series data analysis is their ability to handle data in a sequential way while maintaining an internal state. The research illustrates how machine learning algorithms, such as LSTM, for stock price prediction can aid traders and investors in making informed choices.