Data-Driven Large-Cap US Stock Price Forecasting Using a Hybrid MCDM-Machine Learning Approach

Mehmet Ozcalici; Nazli Ersoy; Dragan Pamucar

doi:10.31181/sor202771

Authors

Mehmet Ozcalici Department of International Trade and Logistics, Faculty of Economics and Administrative Sciences, Kilis 7 Aralık University, Kilis, Türkiye Author https://orcid.org/0000-0003-0384-6872
Nazli Ersoy Department of Business Administration, Faculty of Economics and Administrative Sciences, Osmaniye Korkut Ata University, Osmaniye, Türkiye Author https://orcid.org/0000-0003-0011-2216
Dragan Pamucar 1) Faculty of Engineering, Dogus University, Istanbul, Türkiye// 2) UNEC Applied Artificial Intelligence Research Center, Azerbaijan State University of Economics (UNEC), Baku, Azerbaijan// 3) Department of Applied Mathematical Science, College of Science and Technology, Korea University, Sejong, Republic of Korea Author https://orcid.org/0009-0001-9876-0328

DOI:

https://doi.org/10.31181/sor202771

Keywords:

Stock price prediction, Neural network, Transformer, WENSLO, CRITIC, MEREC, SECA, MCDM, Decision making

Abstract

This study investigates the enhancement of stock price forecasting for the large-cap sector of the U.S. market through the integration of objective weights derived from multi-criteria decision-making (MCDM) methods. Due to the inherent complexities and volatility of stock markets, traditional forecasting approaches often struggle to provide accurate predictions. In this research, MCDM techniques such as EW, SD, MEREC, SECA, CRITIC, CILOS, and WENSLO are applied to assign appropriate weights to various input features, thereby improving the predictive capabilities of stock price models. The proposed methodology utilizes historical stock price data alongside objectively determined weights to train advanced machine learning models, including transformer neural networks. The findings of this study demonstrate that the application of MCDM weighting techniques to input features significantly enhances the forecasting performance of the transformer neural network model. This research not only contributes to the field of stock price prediction but also offers a framework for applying objective feature-weighting methods in various financial forecasting contexts.

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