Analysis of the Application of XGBoost in Exchange-Traded Funds

Abstract

In the quickly expanding landscape of contemporary financial markets, the paramount significance lies in comprehending and effectively employing novel technologies. Trading, a practice that bears resemblances to the use of sticks, has become a prominent option for structuring investment portfolios owing to its inherent diversification properties. This concept has the potential to enhance individuals' comprehension and decision-making abilities inside the intricate and densely populated realm of contemporary finance. This research aims to explore the correlation between Exchange-Traded Funds (ETFs) and the utilization of sophisticated machine learning methodologies, with a particular focus on XGBoost (eXtreme Gradient Boosting). The study additionally offers an extensive overview of the significance of artificial intelligence (AI) in the field of finance. It employs the concept of Xgboost to address the challenge of handling the substantial volume of datasets in the stock market. This approach is based on the utilization of decision trees, which are a robust machine learning algorithm. The objective is to examine and investigate the profound impact of AI in the realm of finance.