Estimating Stock Price Based on Information from Financial Statements Using Long Short-Term Memory Network

This study introduces a new tool for stock market investors and institutions constructed from Long Short-Term Memory (LSTM) for predicting stock prices. Traditional methods for stock price prediction can be subject to biases and limitations, while LSTM provides a fast, unbiased, low-cost solution for stock price prediction, intending to increase profits for investors and reduce losses. The study results indicate that LSTM can effectively maintain captures complex relationships in the data and predict stock prices. This research highlights the potential of LSTM as a valuable and innovative tool for investors and institutions in the stock market.