Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156512
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dc.contributor.authorChin, Yi Xingen_US
dc.date.accessioned2022-04-19T05:58:19Z-
dc.date.available2022-04-19T05:58:19Z-
dc.date.issued2022-
dc.identifier.citationChin, Y. X. (2022). AI-based stock market trending analysis. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156512en_US
dc.identifier.urihttps://hdl.handle.net/10356/156512-
dc.description.abstractStock market prediction is gaining popularity and is widely used due to the lucrative rewards it reap. With accurate prediction of stock prices, we are able to yield significant monetary profits. Stock prices are essentially determined by its demand and supply at that point of time in the stock market. The factors affecting the stock’s demand and supply can be primarily grouped into Technical and Sentimental Indicators. With the advancement in the field of Artificial Intelligence and the vast availability of data, we are now able to predict the stock market more efficiently. This project focuses on finding the best machine learning model to predict stock prices. Currently, the LSTM model and SVM display one of the highest accuracy in predicting stock prices with the technical indicators using time series models. While the VADER and TextBlob models have shown high accuracy in predicting stock prices with sentimental indicators using sentiment analysis. The proposed methodology then inputs results from the best sentiment analysis model as variable together with the stock’s price information into the LSTM model to further enhance prediction capabilities.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.titleAI-based stock market trending analysisen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorLi Fangen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Engineering)en_US
dc.contributor.supervisoremailASFLi@ntu.edu.sgen_US
item.grantfulltextrestricted-
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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