Understanding Singapore office, retail, and diversified REITs with python and traditional analysis

Abstract

Modern investors use various analyses to aid them in investing or trading equities. More information is accessible to retail investors or traders in current times. However, with so much data and parameters being available to the public may cause information overload and often confuse retail investors and traders if they are new to the equity market. This project proposes a set methodology to sift out good Real Estate Investment Trusts (REITs) with good fundamentals using their financial metrics obtained through the official financial statement available on the REIT's website. Secondly, this project will use three technical trading strategies that include some of the most used technical indicators by traders and investors to see the effectiveness of technical analysis. Thirdly, this project will explore the ex-dividend date effect for REITs and conclude if the market reflects this information in the share price. Next, this project will use the Jupyter notebook to analyze if the share prices of REITs of the same sector correlate with each other and find data outliers to perform news analysis. Finally, this project will use Google Colaboratory to predict REIT share prices using different models.