Property stock analysis in SGX - Jiang, Rui (Nanyang Technological University, 2024)
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.