Using sentiment analysis to forecast stock short-term trend
This research investigates the effectiveness of sentiment analysis in stock market prediction, integrating advanced computational techniques with financial analytics. Specifically, the study examines the efficacy of the Autoregressive Distributed Lag (ARDL) model combined with the GPT-4 Turbo mod...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2024
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| Online Access: | http://eprints.utar.edu.my/6997/ http://eprints.utar.edu.my/6997/1/fyp_CS_2024_TL.pdf |
| Summary: | This research investigates the effectiveness of sentiment analysis in stock market prediction,
integrating advanced computational techniques with financial analytics. Specifically, the study
examines the efficacy of the Autoregressive Distributed Lag (ARDL) model combined with
the GPT-4 Turbo model from OpenAI for sentiment analysis to predict the stock price
movements influenced by various news sources in Malaysia. The project employs a systematic
methodology to preprocess data, integrate sentiment scores, and apply the ARDL model to
analyze the impact of news sentiment on stock prices. The sentiment analysis, powered by
GPT-4 Turbo, provides a robust framework for interpreting the emotional tone within financial
news content. Results indicate that the ARDL model, while capturing general market trends
and oscillations, exhibits moderate success in forecasting, as evidenced by varying RMSE
values across different news sources. This variability highlights the influential capacity of news
sources and underscores the necessity for nuanced analysis techniques. The findings contribute
to the broader understanding of how different news sources impact stock market movements
and demonstrate the potential for enhanced predictive accuracy through the integration of
advanced AI-driven tools in financial forecasting. The study’s insights encourage further
exploration into hybrid models that might combine traditional financial indicators with
innovative sentiment analysis methodologies to improve the reliability and effectiveness of
stock market predictions. |
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