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Stock market prediction using LSTM
1 College of Business, Westcliff University, Irvine, CA 92614, USA.
2 Bangladesh University of Professionals, Mirpur Cantonment, Dhaka-1216, Bangladesh.
Research Article
International Journal of Science and Research Archive, 2024, 12(02), 3146-3153.
Article DOI: 10.30574/ijsra.2024.12.2.1542
Publication history:
Received on 10 July 2024; revised on 22 August 2024; accepted on 28 August 2024
Abstract:
The stock market prediction patterns are seen as an important activity and it is more effective. Hence, stock prices will lead to lucrative profits from sound taking decisions. Because of the stagnant and noisy data, stock market-related forecasts are a major challenge for investors. Therefore, forecasting the stock market is a major challenge for investors to use their money to make more profit. This presents a stock market prediction based on Long Short-Term Memory (LSTM), designed to handle time series data and long-term dependencies. Historical stock price data for major technology companies, including Apple, Amazon, Google, and Microsoft, is collected from Yahoo Finance. The dataset contains key attributes such as open price, close price, high price, low price, and trading volume. These features are used to analyze stock behavior and predict future price movements. LSTM-based models can capture temporal relationships in stock market data more effectively than traditional statistical methods.
Keywords:
Stock market prediction; Long Short-Term Memory (LSTM); Time series analysis; Financial forecasting; Yahoo Finance data
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Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
