Cover Image

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...

Full description

Saved in:
Bibliographic Details
Main Author: Tan, Lin
Format: Final Year Project / Dissertation / Thesis
Published: 2024
Subjects:
T Technology (General)
Online Access:http://eprints.utar.edu.my/6997/1/fyp_CS_2024_TL.pdf
http://eprints.utar.edu.my/6997/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my-utar-eprints.6997
record_format eprints
spelling my-utar-eprints.69972025-02-21T03:28:27Z Using sentiment analysis to forecast stock short-term trend Tan, Lin T Technology (General) 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. 2024-05 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6997/1/fyp_CS_2024_TL.pdf Tan, Lin (2024) Using sentiment analysis to forecast stock short-term trend. Final Year Project, UTAR. http://eprints.utar.edu.my/6997/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic T Technology (General)
spellingShingle T Technology (General)
Tan, Lin
Using sentiment analysis to forecast stock short-term trend
description 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.
format Final Year Project / Dissertation / Thesis
author Tan, Lin
author_facet Tan, Lin
author_sort Tan, Lin
title Using sentiment analysis to forecast stock short-term trend
title_short Using sentiment analysis to forecast stock short-term trend
title_full Using sentiment analysis to forecast stock short-term trend
title_fullStr Using sentiment analysis to forecast stock short-term trend
title_full_unstemmed Using sentiment analysis to forecast stock short-term trend
title_sort using sentiment analysis to forecast stock short-term trend
publishDate 2024
url http://eprints.utar.edu.my/6997/1/fyp_CS_2024_TL.pdf
http://eprints.utar.edu.my/6997/
_version_ 1825167457674854400
score 13.239859

Similar Items