Work

Enhancing financial product forecasting accuracy using EMD and feature selection with ensemble models

Year: 2025

Type: article

Abstract: This study examines the impact of Empirical Mode Decomposition (EMD) and Recursive Feature Elimination (RFE) on the prediction of financial product performance employing several ensemble machine learning models, including Random Forest, XGBoost, LightGBM, AdaBoost, CatBoost, Bagging, and ExtraTrees. The research sample comprises ten diverse financial products such as stocks, indices, cryptocurrencies, and commodities. The findings reveal that the combination of EMD and RFE significantly enhances prediction accuracy, with XGBoost delivering the best results. Although all ensemble models benefited from these preprocessing techniques, XGBoost, Random Forest, and LightGBM consistently outperformed the others. This research underscores the critical role of EMD and RFE in improving the predictive capabilities of machine learning models in the dynamic and complex landscape of financial markets, offering valuable insights for practitioners aiming to enhance forecasting accuracy.

Source: Journal of Open Innovation Technology Market and Complexity

DOI: 10.1016/j.joitmc.2025.100531

Authors: Eddy Suprihadi, Nevi Danila, Zaiton Ali

Institutions: Tun Hussein Onn University of Malaysia, Prince Sultan University

Language: English

Topic: Stock Market Forecasting Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $1,250

Funder: Prince Sultan University