Estimating stock price based on information from financial statement using machine learning approach

Abstract

This study introduces a new tool for stock market investors and institutions constructed from Long Short-Term Memory (LSTM) for predicting stock prices. By effectively analyzing financial statements and stock market data, 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 maintain effectively captures complex relationships in the data and predicts stock prices. This research highlights the potential of LSTM as a valuable and innovative tool for investors and institutions in the stock market.