ETF Forecast: Application of Innovative Machine Learning Models in the Field of ETF Forecasting

Tong Zhang

2025

Abstract

With global exchange-traded fund (ETF) assets exceeding $15 trillion, ETFs are becoming increasingly significant in the global financial system. Specifically, because of the unpredictability of the outside world in recent years, investors have been more interested in low-cost, low-risk, and highly transparent investments, which is reflected in the increasing scale of passive investment ETFs and the increase in research in the field of ETF prediction. This study examines how machine learning models are now being used in the field of ETF prediction and chooses innovative machine learning models from the previous two years to review. Including the superposition of common models, the combination of traditional financial models and deep learning models, etc., the results show that these innovative combinations can significantly improve the effectiveness of ETF predictions. Researchers who wish to develop innovative machine learning models for ETF prediction will benefit from this study's understanding of current findings and possible research directions.

Paper Citation

in Harvard Style

Zhang T. (2025). ETF Forecast: Application of Innovative Machine Learning Models in the Field of ETF Forecasting. In Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-765-8, SciTePress, pages 497-501. DOI: 10.5220/0013700100004670

in Bibtex Style

@conference{icdse25,
author={Tong Zhang},
title={ETF Forecast: Application of Innovative Machine Learning Models in the Field of ETF Forecasting},
booktitle={Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2025},
pages={497-501},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013700100004670},
isbn={978-989-758-765-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - ETF Forecast: Application of Innovative Machine Learning Models in the Field of ETF Forecasting
SN - 978-989-758-765-8
AU - Zhang T.
PY - 2025
SP - 497
EP - 501
DO - 10.5220/0013700100004670
PB - SciTePress