Exploring Different Dynamics of Recurrent Neural Network Methods for Stock Market Prediction - A Comparative Study
The intricate and unpredictable nature of stock markets underscores the importance of precise forecasting for timely detection of downturns and subsequent rebounds. Various factors, including news, rumors surrounding events or companies, market sentiments, and governmental policies, can significantl...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2371706 |
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| Summary: | The intricate and unpredictable nature of stock markets underscores the importance of precise forecasting for timely detection of downturns and subsequent rebounds. Various factors, including news, rumors surrounding events or companies, market sentiments, and governmental policies, can significantly impact stock prices. Nevertheless, the precision of current methods remained insufficient until the adoption of artificial neural network architectures like long short-term memory (LSTM). The aim of this study is to create a precise AI-driven platform tailored for both the Indian and international stock markets. This platform is designed to assist retail investors in navigating digital environments by employing various LSTM algorithms. Its primary goals include predicting stock price fluctuations, pinpointing potential investment prospects, and refining trading strategies. The application aims to leverage advanced LSTM algorithms to analyze historical market data, recognize patterns, and provide real-time insights. It will take past price and process it through LSTM algorithms to take a logical decision. In the quest to broaden retail participation in the capital markets, the effort is to develop an application for novice investors who either have no time in research or are the victims of financial mis-selling and enable them to leverage the technology to their advantage. |
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| ISSN: | 0883-9514 1087-6545 |