Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157702
Title: Stock market prediction with artificial intelligence
Authors: Raoul Ramesh Nanwani
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Raoul Ramesh Nanwani (2022). Stock market prediction with artificial intelligence. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157702
Abstract: Uncertainty 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.
URI: https://hdl.handle.net/10356/157702
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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