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Recurrent Neural Network for Stock Market Forecasting using Long Short-Term Memory and an Analysis of How Social Media Affects Share Prices
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 10 |
| Year of Publication: 2024 |
| Authors: SR Samarasuriya, DVDS Abeysinghe, KGK Abeywardhane |
10.5120/ijca2024923450
|
SR Samarasuriya, DVDS Abeysinghe, KGK Abeywardhane . Recurrent Neural Network for Stock Market Forecasting using Long Short-Term Memory and an Analysis of How Social Media Affects Share Prices. International Journal of Computer Applications. 186, 10 ( Feb 2024), 9-14. DOI=10.5120/ijca2024923450
Abstract
With the increase in computing power and the popularity of machine learning (ML), it has become the norm to tackle more complex problems using ML. The stock market is known to be a highly volatile environment in which stock prices can fluctuate in an erratic manner. The main goal behind this study is to use a deep learning artificial intelligence model to understand and forecast future stock prices. An analysis was also done to assess the role of social media in the stock market price variation and to what extent, it impacts stock prices. The favored approach was to use a Recurrent neural network (RNN) composed of a Long Short-Term Memory (LSTM) model to predict the prices as it is the most suitable to work with time- series data. A successful model was deployed which showed a high level of accuracy and produced low values with regards to the loss function.
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