ItemConference Paper

Prediction of Next-Day Stock Price Using Stacked Ensemble Learning TechniquesAn Exploration of Model Compatibility

Title
Prediction of Next-Day Stock Price Using Stacked Ensemble Learning TechniquesAn Exploration of Model Compatibility
Creator
Joseph, Amal; Pambhar, Bansi; George, Allen; Kokatnoor, Sujatha Arun; Kumar, Sandeep
Description
Trading professionals can make well-informed decisions about what to purchase or sell in order to maximize short-term gains by forecasting stock prices for the next day. This research study focuses on exploring the compatibility of ensemble learning techniques through stacking to predict next-day stock prices. The models involvedRandom Forest, Extra Trees, AdaBoost, and Gradient Boosting, were paired two at a time, and their predictions were used as inputs to a Multi-Layer Perceptron (MLP) Regressor, which served as the meta-learner. The results revealed that the combination of Extra Trees Regressor and Gradient Boosting outperformed the individual base models, due to their complementary strengths and ability to capture non-linear relationships effectively. However, other model combinations showed only average performance. This outcome was attributed to overlapping model strengths, leading to increase in error and overfitting. The findings highlight the importance of thoughtful model selection in ensemble methods and suggest that not all combinations are equally beneficial. Understanding the compatibility of different models is crucial to improving performance in ensemble learning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Source
Lecture Notes in Networks and Systems;Volume;1239 LNNS;pp.253-265
Date
01-01-2025
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
AdaBoost; Ensemble machine learning models; Extra trees; Gradient boosting; Multi-Layer Perceptron (MLP); Random forest; Stacking
Coverage
Joseph A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Pambhar B., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; George A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Kumar S., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 23673370; ISBN: 978-981961187-4;
Format
online
Language
English
Type
Conference paper

Citation

Joseph, Amal; Pambhar, Bansi; George, Allen; Kokatnoor, Sujatha Arun; Kumar, Sandeep, “Prediction of Next-Day Stock Price Using Stacked Ensemble Learning TechniquesAn Exploration of Model Compatibility,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25465.

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