Analysis of stock forecasting based on LSTM

Abstract

The use of the long short-term memory (LSTM) machine learning algorithm for stock prediction is widely used contemporarily. This study the feasibility of price prediction based on LSTM. This study first collected extensive data on stock prices, including the closing prices, volumes, and other relevant indicators, for several companies. Then, it implemented an LSTM-based model using this data, which was trained on a dataset containing historical stock price information over a fixed period. The models performance was evaluated using a separate test set. Based on the analysis, LSTM is successful in forecasting stock a remarkable degree of precision, rendering it an invaluable asset for investors aiming to make well-informed investment choices. Its ability to capture long-term dependencies and patterns within time series data makes it an ideal choice for forecasting future trends in financial markets. By leveraging this model, investors can gain valuable insights into potential market movements and adjust their investment strategies accordingly. Overall, the promising results obtained from applying LSTM-based models highlight their potential value in supporting investors' decision-making processes and ultimately improving investment outcomes.