González-Núñez, Enrique and Kampouridis, Michail and Trejo, Luis A (2024) A Comparative Study for Stock Market Forecast Based on a New Machine Learning Model. Big Data and Cognitive Computing, 8 (4). p. 34. DOI https://doi.org/10.3390/bdcc8040034
González-Núñez, Enrique and Kampouridis, Michail and Trejo, Luis A (2024) A Comparative Study for Stock Market Forecast Based on a New Machine Learning Model. Big Data and Cognitive Computing, 8 (4). p. 34. DOI https://doi.org/10.3390/bdcc8040034
González-Núñez, Enrique and Kampouridis, Michail and Trejo, Luis A (2024) A Comparative Study for Stock Market Forecast Based on a New Machine Learning Model. Big Data and Cognitive Computing, 8 (4). p. 34. DOI https://doi.org/10.3390/bdcc8040034
Abstract
This research aims at applying the Artificial Organic Network (AON), a nature-inspired, supervised, metaheuristic machine learning framework, to develop a new algorithm based on this machine learning class. The focus of the new algorithm is to model and predict stock markets based on the Index Tracking Problem (ITP). In this work, we present a new algorithm, based on the AON framework, that we call Artificial Halocarbon Compounds, or the AHC algorithm for short. In this study, we compare the AHC algorithm against genetic algorithms (GAs), by forecasting eight stock market indices. Additionally, we performed a cross-reference comparison against results regarding the forecast of other stock market indices based on state-of-the-art machine learning methods. The efficacy of the AHC model is evaluated by modeling each index, producing highly promising results. For instance, in the case of the IPC Mexico index, the R-square is 0.9806, with a mean relative error of 7×10‾⁴. Several new features characterize our new model, mainly adaptability, dynamism and topology reconfiguration. This model can be applied to systems requiring simulation analysis using time series data, providing a versatile solution to complex problems like financial forecasting.
Item Type: | Article |
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Uncontrolled Keywords: | artificial intelligence; machine learning; bio-inspired; genetic algorithm; stock market index; financial forecasting |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 14 Jun 2024 13:32 |
Last Modified: | 07 Aug 2024 15:25 |
URI: | http://repository.essex.ac.uk/id/eprint/38108 |
Available files
Filename: BDCC-08-00034-with-cover.pdf
Licence: Creative Commons: Attribution 4.0