International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Predicting the stock market is an extremely difficult endeavor given the unpredictable and nonlinear characteristics of financial markets, with price fluctuations occurring very quickly to complicate forecasting even more. The use of traditional statistical techniques frequently proves inadequate to replicate the complexities of stock movements, which has been the motivation behind increasing attention on the utilization of machine learning methodologies. This research examines the application of Long Short-Term Memory (LSTM) networks to predict stock prices based on past stock prices, using historical stock price data from 2019 to 2023, including Open, High, Low, and Close prices. The performance of the model is assessed through significant metrics like Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), indicating that LSTM can be used to successfully forecast long-term trends in stock prices. The study also investigates the performance of other machine learning models, including Gated Recurrent Units (GRU) and Artificial Neural Networks, for forecasting stock prices, with the results indicating that LSTM is superior to these models in identifying long-term dependencies. Nevertheless, it remains difficult to predict sudden changes in the market due to externalities, such as economic developments or geopolitical changes. The paper explores possible avenues for future work, such as combining sentiment analysis, hybrid models, and investigating the application of other deep learning architectures to further improve predictive power. The work adds to the body of research on machine learning in financial forecasting and sheds light on how stock market prediction models can be made more robust and accurate.

Keywords: Stock market prediction, machine learning, Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Artificial Neural Networks.

Downloads: | DOI: 10.17148/IJARCCE.2025.141271