Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157702
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dc.contributor.authorRaoul Ramesh Nanwanien_US
dc.date.accessioned2022-05-19T05:36:41Z-
dc.date.available2022-05-19T05:36:41Z-
dc.date.issued2022-
dc.identifier.citationRaoul Ramesh Nanwani (2022). Stock market prediction with artificial intelligence. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157702en_US
dc.identifier.urihttps://hdl.handle.net/10356/157702-
dc.description.abstractUncertainty is a word any investor despises. Uncertainty creates doubts in even the most established investor’s mind and blurs the line between investing and gambling. Hence, many investors use financial analysis tools to try and predict stock prices. Investors rely heavily on economic reports from companies to decide whether the company is worth investing in. However, with technological advancements and an increase in computing power, machine learning models can be used to predict stock prices. These algorithms eliminate the need for humans to spot patterns that would take much longer than the algorithm’s mere seconds of data analysis. As seen in the AMC and GameStop debacle, social media can influence stock prices as others influence retail investors on social media platforms to buy certain stocks. Hence, the amount of data generated on these platforms can be used with financial indicators to create a superior prediction model. This project aims to use machine learning algorithms to predict stock price trends using technical indicators for the first part. Following this, sentiment analysis on tweets will be used with technical indicators to generate more accurate predictions. For this project, all model’s predicted trends will be compared to the actual trend and then analysed. The model with the closest predicted trend will be concluded as the best model to be used by investors.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleStock market prediction with artificial intelligenceen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorMohammed Yakoob Siyalen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
dc.contributor.supervisoremailEYAKOOB@ntu.edu.sgen_US
item.grantfulltextrestricted-
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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