Application and Development of Quantitative Factor Mining in Financial Markets

Tielong Ma

2025

Abstract

Quantitative factor mining is a crucial element of modern quantitative investment, focusing on identifying indicators that can predict asset price movements through data analysis. This paper systematically examines five key factors influencing the effectiveness of quantitative factor mining: data quality, factor construction methods, market environment, model selection, and computational resources, and provides targeted optimization strategies. The research highlights that data quality serves as the foundation of factor mining, while factor construction methods and model selection directly influence the predictive accuracy of factors. Market environment shifts may cause factor degradation, and sufficient computational resources are essential for efficient factor mining The paper explores traditional factor mining approaches, including fundamental analysis, technical analysis, statistical analysis, and macroeconomic analysis, as well as prediction methods based on major influencing factors, such as linear regression models, multi-factor models, time series models, machine learning models, and ensemble learning techniques, summarizing the strengths and limitations of each. Finally, the paper suggests future research directions, such as integrating multiple factor construction methods and leveraging advanced machine learning techniques to enhance factor mining efficiency and accuracy.

Paper Citation

in Harvard Style

Ma T. (2025). Application and Development of Quantitative Factor Mining in Financial Markets. In Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-765-8, SciTePress, pages 299-305. DOI: 10.5220/0013688300004670

in Bibtex Style

@conference{icdse25,
author={Tielong Ma},
title={Application and Development of Quantitative Factor Mining in Financial Markets},
booktitle={Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2025},
pages={299-305},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013688300004670},
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 - Application and Development of Quantitative Factor Mining in Financial Markets
SN - 978-989-758-765-8
AU - Ma T.
PY - 2025
SP - 299
EP - 305
DO - 10.5220/0013688300004670
PB - SciTePress