Understanding of Singapore REITs (office, retail and diversified) using traditional analysis and machine learning

This study structures a methodology to sift out Singapore Real Estate Investment Trusts (S-REITs) with bad fundamentals and identify the good ones using Fundamental Analysis (FA). The methodology will eventually give scores to each REIT in Office, Retail and Diversified sectors and the REITs will be...

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Main Author: Luen, Zhe Yong
Other Authors: Wong Jia Yiing, Patricia
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/167784
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1677842023-07-07T15:43:03Z Understanding of Singapore REITs (office, retail and diversified) using traditional analysis and machine learning Luen, Zhe Yong Wong Jia Yiing, Patricia School of Electrical and Electronic Engineering EJYWong@ntu.edu.sg Business::Finance::Stock exchanges Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence This study structures a methodology to sift out Singapore Real Estate Investment Trusts (S-REITs) with bad fundamentals and identify the good ones using Fundamental Analysis (FA). The methodology will eventually give scores to each REIT in Office, Retail and Diversified sectors and the REITs will be ranked based on FA. After which, the top REIT from each sector will be pick out for Price Prediction and Price Movement Prediction using LSTM model. Bachelor of Engineering (Electrical and Electronic Engineering) 2023-06-05T00:22:55Z 2023-06-05T00:22:55Z 2023 Final Year Project (FYP) Luen, Z. Y. (2023). Understanding of Singapore REITs (office, retail and diversified) using traditional analysis and machine learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/167784 https://hdl.handle.net/10356/167784 en A1136-221 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Business::Finance::Stock exchanges
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
spellingShingle Business::Finance::Stock exchanges
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Luen, Zhe Yong
Understanding of Singapore REITs (office, retail and diversified) using traditional analysis and machine learning
description This study structures a methodology to sift out Singapore Real Estate Investment Trusts (S-REITs) with bad fundamentals and identify the good ones using Fundamental Analysis (FA). The methodology will eventually give scores to each REIT in Office, Retail and Diversified sectors and the REITs will be ranked based on FA. After which, the top REIT from each sector will be pick out for Price Prediction and Price Movement Prediction using LSTM model.
author2 Wong Jia Yiing, Patricia
author_facet Wong Jia Yiing, Patricia
Luen, Zhe Yong
format Final Year Project
author Luen, Zhe Yong
author_sort Luen, Zhe Yong
title Understanding of Singapore REITs (office, retail and diversified) using traditional analysis and machine learning
title_short Understanding of Singapore REITs (office, retail and diversified) using traditional analysis and machine learning
title_full Understanding of Singapore REITs (office, retail and diversified) using traditional analysis and machine learning
title_fullStr Understanding of Singapore REITs (office, retail and diversified) using traditional analysis and machine learning
title_full_unstemmed Understanding of Singapore REITs (office, retail and diversified) using traditional analysis and machine learning
title_sort understanding of singapore reits (office, retail and diversified) using traditional analysis and machine learning
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/167784
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