Historical backtesting

In the previous article, we introduced TESG’s scoring mechanism in detail and its rating ratio among popular ETFs in China. This article will teach readers how to further use the ESG rating provided by TESG, and use Python to construct a concurrent An investment portfolio with sustainable operations and financial growth potential.

Principal Component Analysis (PCA) is a key technique in unsupervised learning widely used in machine learning and statistics to analyze data and reduce data dimensionality. Its core idea is to break down the original data into representative principal components, achieving dimensionality reduction and providing a new description of the data.

In recent years, various asset management companies have launched ESG-related ETFs, such as the pioneering Fubon Corporate Governance ETF (00692), which focuses on the top 100 companies in Taiwan in terms of corporate governance, and the Cathay Sustainable High Dividend ETF (00878), which combines high dividend yields with sustainability criteria. The common thread among these ESG ETFs is their tracking of ESG indices introduced by domestic and international index providers. While each ESG ETF specializes in different areas, they all rely on ESG screening criteria, emphasizing sustainable business practices among their constituent stocks, allowing investors to invest in assets that balance environmental sustainability and robust growth potential.