Property stock analysis in SGX

This study focuses on the application of machine learning techniques in the analysis of Singapore real estate property stocks. The research aims to utilize historical stock data, financial indicators, and real estate market trends to predict the performance and value of selected property stocks list...

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Main Author: Jiang, Rui
Other Authors: Wong Jia Yiing, Patricia
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/176850
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1768502024-05-24T15:45:28Z Property stock analysis in SGX Jiang, Rui Wong Jia Yiing, Patricia School of Electrical and Electronic Engineering EJYWong@ntu.edu.sg Engineering This study focuses on the application of machine learning techniques in the analysis of Singapore real estate property stocks. The research aims to utilize historical stock data, financial indicators, and real estate market trends to predict the performance and value of selected property stocks listed in Singapore. The study starts with a basic stock analysis, which involves assessing key financial metrics, market trends, and company performance to identify potential investment opportunities in the real estate sector. Subsequently, machine learning models are employed to analyse and predict stock prices based on historical data, market sentiment, and external factors affecting the real estate industry. The research evaluates the effectiveness of various machine learning algorithms such as deep learning, Long Short-Term Memory which is a type of recurrent neural network in forecasting stock prices and making investment decisions. By combining traditional stock analysis techniques with advanced machine learning methods, this study aims to provide insights into the potential of using data-driven approaches for analyzing Singapore real estate property stocks and improving investment strategies in the real estate sector. Bachelor's degree 2024-05-23T03:36:18Z 2024-05-23T03:36:18Z 2024 Final Year Project (FYP) Jiang, R. (2024). Property stock analysis in SGX. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176850 https://hdl.handle.net/10356/176850 en 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 Engineering
spellingShingle Engineering
Jiang, Rui
Property stock analysis in SGX
description This study focuses on the application of machine learning techniques in the analysis of Singapore real estate property stocks. The research aims to utilize historical stock data, financial indicators, and real estate market trends to predict the performance and value of selected property stocks listed in Singapore. The study starts with a basic stock analysis, which involves assessing key financial metrics, market trends, and company performance to identify potential investment opportunities in the real estate sector. Subsequently, machine learning models are employed to analyse and predict stock prices based on historical data, market sentiment, and external factors affecting the real estate industry. The research evaluates the effectiveness of various machine learning algorithms such as deep learning, Long Short-Term Memory which is a type of recurrent neural network in forecasting stock prices and making investment decisions. By combining traditional stock analysis techniques with advanced machine learning methods, this study aims to provide insights into the potential of using data-driven approaches for analyzing Singapore real estate property stocks and improving investment strategies in the real estate sector.
author2 Wong Jia Yiing, Patricia
author_facet Wong Jia Yiing, Patricia
Jiang, Rui
format Final Year Project
author Jiang, Rui
author_sort Jiang, Rui
title Property stock analysis in SGX
title_short Property stock analysis in SGX
title_full Property stock analysis in SGX
title_fullStr Property stock analysis in SGX
title_full_unstemmed Property stock analysis in SGX
title_sort property stock analysis in sgx
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/176850
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