Big Data Analytics in Highly Liquid Exchange-Traded Funds (ETFs) Tracking Performance in China

Abstract

As big data technology advances, financial markets have seen a significant rise in data volume and variety, profoundly impacting ETF (Exchange-traded funds) tracking errors. It is crucial to leverage big data technology to analyze new trends and characteristics in ETF tracking errors. The present research studies the tracking performance of 28 high-liquidity stock ETFs in China in the last year. The paper uses tracking difference, daily tracking error(DTE), annual tracking error(ATE), and Panel Linear Regression to evaluate the tracking performance of ETF and the role of the determinants. The study found the underlying benchmark tracking performance of Chinese highly liquid equity ETFs is less efficient than that of more developed region ETFs. The number of stocks included in the underlying index tracked by ETFs has a significant positive impact on the annualized tracking error, while AUM, listing years and daily turnover have a negative impact on the tracking error of ETFs.