[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120918-en":3,"doc-seo-120918-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120918,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","A Comparative Study for Stock Market Forecast Based on a New Machine Learning Model","This research applies the Arti􀀂cial Organic Network (AON), a nature-inspired supervised metaheuristic framework, to build a new algorithm for stock market modeling and prediction via the Index Tracking Problem (ITP). The proposed method, Arti􀀂cial Halocarbon Compounds (AHC), is evaluated by forecasting eight stock market indices. Results are compared against genetic algorithms (GAs) and cross-referenced with contemporary machine learning approaches, showing highly promising fit such as an R-square of 0.9806 for the IPC Mexico index.","3.7  \n4.9  \nArticle  \nA Comparative Study for Stock Market Forecast Based on a New Machine Learning Model  \nEnrique González-Núñez, Luis A. Trejo and Michael Kampouridis  \n[https://doi.org/10.3390/bdcc8040034](https://doi.org/10.3390/bdcc8040034)  \nArticle  \nA Comparative Study for Stock Market Forecast Based on a New Machine Learning Model  \nEnrique González-Núñez 1, *, Luis A. Trejo 1 and Michael Kampouridis 2  \nCitation: González-Núñez, E.; Trejo, L.A.; Kampouridis, M. A Comparative Study for Stock Market Forecast Based on a New Machine Learning Model. Big Data Cogn. Comput. 2024, 8, 34. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/bdcc8040034](10.3390/bdcc8040034)  \nAcademic Editor: Min Chen  \nReceived: 25 January 2024  \nRevised: 3 March 2024  \nAccepted: 7 March 2024  \nPublished: 26 March 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 School of Engineering and Science, Tecnologico de Monterrey, Atizapán de Zaragoza 52926, Mexico; [ltrejo@tec.mx](ltrejo@tec.mx)  \n2 School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK; [mkampo@essex.ac.uk](mkampo@essex.ac.uk)  \n* Correspondence: [enrique.gonzalez@tec.mx](enrique.gonzalez@tec.mx)  \nAbstract: This research aims at applying the Arti􀀂cial 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 Arti􀀂cial 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 ef􀀂cacy 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 recon􀀂guration. This model can be applied to systems requiring simulation analysis using time series data, providing a versatile solution to complex problems like 􀀂nancial forecasting.  \nKeywords: arti􀀂cial intelligence; machine learning; bio-inspired; genetic algorithm; stock market index; 􀀂nancial forecasting  \n1. Introduction  \nThe handling of risk and uncertainty across various 􀀂nancial domains has prompted the development of diverse models and methodologies. As exposed by Elliot and Timmermann [1], asset allocation requires real-time stock return forecasts, and improved predictions contribute to enhanced investment performance. Consequently, the ability to forecast returns holds crucial implications for testing market ef􀀂ciency and developing more realistic asset pricing models that better re􀀃ect the available data. Furthermore, Elliot and Timmermann [1] state that stock returns inherently contain a sizable unpredictable component, so the best forecasting models can only explain a relatively small part of stock returns.  \nIn this respect, we propose a new algorithm, called Arti􀀂cial Halocarbon Compounds (AHC), or the AHC algorithm for short, to tackle the Index Tracking Problem (ITP) . The ef-􀀂cacy is evaluated by forecasting eight stock market indices. The outcomes obtained using the AHC model, as an alternative topology rooted in th","cbCaijdSef7vQobq","https://ap.wps.com/l/cbCaijdSef7vQobq","pdf",706781,1,21,"English","en",105,"# Introduction\n## Risk, uncertainty, and stock return forecasting\n## Proposed AHC algorithm and evaluation setup","[{\"question\":\"What is the proposed AHC approach in this study?\",\"answer\":\"The study introduces Arti􀀂cial Halocarbon Compounds (AHC), a new algorithm derived from the Arti􀀂cial Organic Network (AON) framework to model and predict stock markets under the Index Tracking Problem.\"},{\"question\":\"How does the paper evaluate the new model?\",\"answer\":\"It forecasts eight stock market indices, then assesses effectiveness by modeling each index and measuring predictive performance metrics such as R-square and mean relative error.\"},{\"question\":\"What comparisons are made to validate AHC?\",\"answer\":\"The AHC algorithm is compared against genetic algorithms (GAs) and cross-referenced with results from state-of-the-art machine learning methods for forecasting other stock market indices.\"}]","A Comparative Study for Stock Market Forecast Based on a New Machine Learning Model | 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is the proposed AHC approach in this study?","Question",{"text":75,"@type":76},"The study introduces Arti􀀂cial Halocarbon Compounds (AHC), a new algorithm derived from the Arti􀀂cial Organic Network (AON) framework to model and predict stock markets under the Index Tracking Problem.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper evaluate the new model?",{"text":80,"@type":76},"It forecasts eight stock market indices, then assesses effectiveness by modeling each index and measuring predictive performance metrics such as R-square and mean relative error.",{"name":82,"@type":73,"acceptedAnswer":83},"What comparisons are made to validate AHC?",{"text":84,"@type":76},"The AHC algorithm is compared against genetic algorithms (GAs) and cross-referenced with results from state-of-the-art machine learning methods for forecasting other stock market 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