Tools
Tools
Adoghe, Osaigbovo Daniel (2024) Understanding the Impact of Social Media Sentiment on Financial Decision-making within the Stock Market: A Deep Learning Computational Analysis. Masters thesis, Dublin, National College of Ireland.
Preview |
PDF (Master of Science)
Download (1MB) | Preview |
Preview |
PDF (Configuration Manual)
Download (1MB) | Preview |
Abstract
This study considers an innovative way to explore social media sentiment analysis for stock market prediction through the use of Generative Adversarial Networks (GANs) to improve the accuracy of the forecasting models. Traditional financial theories like the Efficient Market Hypothesis (EMH), and the Random Walk (RW) theory have often overlooked the psychological and behavioral aspects of market dynamics that drive stock prices. Thus, our study incorporates the psychological component through sentiment data expressed in X (formerly known as Twitter) by designing three predictive models; Long Short-Term Memory, Random Forest, and GAN. These models were subsequently evaluated against Tesla (TSLA) and Amazon (AMZN) stock data, focusing on some major performance metrics such as accuracy, precision, and recall. In this respect, the GAN model demonstrated superior performance with an accuracy of 82.67%, precision of 70.21%, and recall of 81.11% for TSLA, and accuracy of 84.21%, precision of 85.71%, and recall of 75.00% for AMZN. In comparison, the LSTM model achieved an accuracy of 66.67% for TSLA and 53.84% for AMZN, while the RF model achieved 56.86% for TSLA and 54.00% for AMZN. This research not only contributes to the evolution of computational finance but also accentuates how decisive behavioral economics can be in understanding and predicting market trends. The results subsequently indicate that incorporating social media sentiment increases substantially the predictive power of financial models, therefore offering a more nuanced approach toward market analysis.
Actions (login required)
 |
View Item |