[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121015-en":3,"doc-seo-121015-105":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},121015,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Expanding a Machine Learning Class Towards its Application to the Stock Market Forecast","A new efficient algorithm is proposed for short-term stock market trend forecasting using the Artificial Organic Networks (AON) metaheuristic machine learning framework. The work introduces the Artificial Halocarbon Compounds (AHC) approach as a bioinspired supervised method within the AON topology. Forecast performance is contrasted with earlier results from Artificial Hydrocarbon Networks (AHN) and additionally compared against ARIMA, as well as against other state-of-the-art machine learning index prediction methods. Evaluation on the IPC Mexico index reports strong fit with an R-square of 0.9919 and a mean relative error of 8×10−4.","Research Repository  \nExpanding a Machine Learning Class Towards its Application  \nto the Stock Market Forecast  \nAccepted for publication in Applied Intelligence.  \nResearch Repository link: [https://repository.essex.ac.uk/39299/](https://repository.essex.ac.uk/39299/)  \n[Please note:](Please note:)  \nChanges made as a result of publishing processes such as copy-editing, formatting and page numbers may not be reflected in this version. For the definitive version of this publication, please refer to the published source. You are advised to consult the publisher’s version if you wish to cite this paper.  \n[www.essex.ac.uk](www.essex.ac.uk)  \nUniversity of Essex  \nExpanding a Machine Learning Class Towards its Application to the Stock Market Forecast  \nEnrique Gonz´alez-N´u˜nez. [0000-0002-5410-0483] 1*, Luis A. Trejo [0000-0001-9741-4581] 1† and Michael Kampouridis [0000-0003-0047-7565]2†  \n1 School of Engineering and Science, Tecnologico de Monterrey, Carretera al Lago de Guadalupe Km. 3.5, Atizap´an, 52926, Edo. de M´exico, M´exico.  \n2 School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, Essex, United Kingdom.  \n*Corresponding author(s). E-mail(s): [enrique.gonzalez@tec.mx](enrique.gonzalez@tec.mx) ; Contributing authors: [ltrejo@tec.mx](ltrejo@tec.mx) ; [mkampo@essex.ac.uk](mkampo@essex.ac.uk) ;  \n†These authors contributed equally to this work.  \nAbstract  \nIn this work, we present a new and efficient algorithm to perform a shortterm market trend forecast, based on the Artificial Organic Networks (AON) metaheuristic machine learning framework. Regarding this goal, we present the concept of Artificial Halocarbon Compounds (AHC) or AHC-algorithm as a bioinspired supervised machine learning algorithm based on the AON framework. Through our research, we contrast the forecast acquired with the proposed AHC model, to previously reported outcomes using the Artificial Hydrocarbon Networks (AHN) in similar tasks. The AHN algorithm is the first formally defined topology based on the AON, making the AHN algorithm a vital benchmark to contemplate. After comparing the AHC-algorithm to the original AHN-algorithm, we found out that due to the high computational complexity of the latter, the new topology is more convenient when modeling more complex systems; being this characteristic the main contribution of the AHC-algorithm, allowing it to bea more adaptable, dynamic, and reconfigurable topology. Likewise, we compared the results of the AHC-algorithm against the outcomes derived from an ARIMA model; we also made a cross-reference contrast against results concerning the prediction of other stock market indices using former state-of-the-art machine learning methods. The proficiency of the AHC-algorithm is assessed by doing a forecast of the IPC Mexico index obtaining good results, achieving a computed R-square of 0 .9919, and an 8 × 10 −4 mean relative error for the forecast.  \n1  \nKeywords: Artificial Intelligence, Machine learning, Bio-inspired, Metaheuristic,  \nStock market index, Financial Forecasting  \n1 Introduction  \nThe Index Tracking Problem (ITP) or stock market prediction as more commonly known, is a complex process affected by many factors [1, 2] . As remarked in [3], stock market forecasts, despite being a recurrent subject of many investigation groups, remain an essential financial research topic within other aspects due to their economic impact. As an update of the example provided in the previously referred article, the New York Stock Exchange had a $40.5 trillion market capitalization (market value of all shares traded from public companies listed in its market) as of December 2022 and had a $52 .2 trillion market capitalization as of December 2021 . The ITP is a trading strategy based on the buy-and-hold of assets [4, 5], that uses an index tracker to replicate the performance of a stock market index or any other security found in the capital markets, that consid","cbCaibBZrgPr2az5","https://ap.wps.com/l/cbCaibBZrgPr2az5","pdf",1687131,1,33,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Index Tracking Problem (ITP)\n## Proposed AHC-algorithm within AON","[{\"question\":\"What problem does the paper address?\",\"answer\":\"It addresses short-term stock market trend forecasting framed as the Index Tracking Problem, where an index tracker replicates market index performance while considering risk.\"},{\"question\":\"What algorithm does the paper propose?\",\"answer\":\"The paper proposes the Artificial Halocarbon Compounds (AHC) algorithm, a bioinspired supervised machine learning approach based on the Artificial Organic Networks (AON) framework.\"},{\"question\":\"How is the proposed method evaluated and what results are reported?\",\"answer\":\"The AHC-algorithm is evaluated by forecasting the IPC Mexico index and compared with AHN and ARIMA. The reported results include an R-square of 0.9919 and a mean relative error of 8×10−4.\"}]","Expanding a Machine Learning Class Towards its Application to the Stock Market Forecast | PDF",1785733321,83,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"expanding-a-machine-learning-class-towards-its-application-to-the-stock-market-forecast","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/expanding-a-machine-learning-class-towards-its-application-to-the-stock-market-forecast/121015/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address?","Question",{"text":75,"@type":76},"It addresses short-term stock market trend forecasting framed as the Index Tracking Problem, where an index tracker replicates market index performance while considering risk.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What algorithm does the paper propose?",{"text":80,"@type":76},"The paper proposes the Artificial Halocarbon Compounds (AHC) algorithm, a bioinspired supervised machine learning approach based on the Artificial Organic Networks (AON) framework.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the proposed method evaluated and what results are reported?",{"text":84,"@type":76},"The AHC-algorithm is evaluated by forecasting the IPC Mexico index and compared with AHN and ARIMA. 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