Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 3rd International Conference on Business and Policy Studies
Series Vol. 74 , 17 April 2024
Open
Access | Article
Analysis of the Application of XGBoost in Exchange-Traded Funds
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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.
Keywords
Technical Analysis, Machine Learning, Quantitative Trading, Portfolio Management
References
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2. Shinde, P.P., Shah, S. (2018). A Review of Machine Learning and Deep Learning Applications. In Proceedings of the 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA) (pp. 1-6).
3. Min-Yuh Day and Jian-Ting Lin. (2019). "Artificial Intelligence for ETF Market Prediction and Portfolio Optimization." In Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1026-1033.
4. Myles, A. J., Feudale, R. N., Liu, Y., Woody, N. A., & Brown, S. D. (2004). An introduction to decision tree modeling. Journal of Chemometrics, 18(6), 275-285. DOI: 10.1002/cem.873
5. Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794).
6. Yun, K. K., Yoon, S. W., & Won, D. (2021). Prediction of stock price direction using a hybrid GA-XGBoost algorithm with a three-stage feature engineering process. Expert Systems With Applications, 186, 115716. https://doi.org/10.1016/j.eswa.2021.115716
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 3rd International Conference on Business and Policy Studies
- ISBN (Print)
- 978-1-83558-371-5
- ISBN (Online)
- 978-1-83558-372-2
- Published Date
- 17 April 2024
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/74/20241522
- Copyright
- 17 April 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited