Ensemble Fusion: Optimizing Market Prediction with Neural Networks, Residual Networks and Xgboost

Mengran Zhu; Ye Zhang; Xinyu Zhang

doi:10.5281/zenodo.11060009

Authors

Mengran Zhu Miami University
Ye Zhang University of Pittsburgh
Xinyu Zhang North Carolina State University

DOI:

https://doi.org/10.5281/zenodo.11060009

References:

24

Keywords:

Quantitative Trading, Market Prediction, Ensemble Learning, Neural Networks, Resnet, Xgboost

Abstract

In the realm of financial markets, accurate prediction of market trends plays a pivotal role in guiding investment decisions and maximizing returns. This paper presents an innovative ensemble model that combines neural networks (NN), residual networks (Resnet), and Xgboost, offering a comprehensive approach to market prediction. Through extensive experimentation and evaluation, our ensemble model demonstrates remarkable performance enhancements over individual models and other ensemble configurations. By integrating the predictive strengths of NN, Resnet, and Xgboost, our ensemble achieves significant improvements in predictive accuracy, underscoring the potential of ensemble learning in refining market prediction strategies and empowering traders and investors with enhanced decision-making capabilities. This research contributes to advancing the field of quantitative trading by providing a robust and effective framework for market prediction, offering insights and opportunities for practitioners to navigate the complexities of financial markets with greater confidence and success.

Author Biographies

Mengran Zhu, Miami University

Miami University, USA.

Ye Zhang, University of Pittsburgh

University of Pittsburgh, USA.

Xinyu Zhang, North Carolina State University

North Carolina State University, USA.

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