[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123919-en":3,"doc-seo-123919-105":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},123919,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Machine learning algorithms applied to the estimation of liquidity: the 10-year United States treasury bond - Research paper","The study aims to evaluate how well representative liquidity variables predict liquidity-related economic fluctuations. Liquidity is treated as a key financial indicator used to support financial stability analysis and systemic risk anticipation, with focus on capital management among private-sector investors. Multiple machine learning models are benchmarked using proxy variables representing liquidity concepts and comparing predictive capacity across sources. Private-sector liquidity data contributes more to predictions than public-sector data, while international liquidity is identified as a less standardized, more diffuse concept requiring future research.","The current issue and full text archive of this journal is available on Emerald Insight at:  \n[https://www.emerald.com/insight/2444-8494.htm](https://www.emerald.com/insight/2444-8494.htm)  \nMachine learning algorithms applied to the estimation of liquidity: the 10-year United States treasury bond  \nIgnacio Manuel Luque Raya  \nUniversidad de Granada, Granada, Spain, and  \nPablo Luque Raya  \nCredit Suisse Group AG, Zurich, Switzerland  \nAbstract  \nPurpose–Having defined liquidity, the aim is to assess the predictive capacity of its representative variables, so that economic fluctuations may be better understood.  \nDesign/methodology/approach – Conceptual variables that are representative of liquidity will be used to formulate the predictions. The results of various machine learning models will be compared, leading to some reflections on the predictive value of the liquidity variables, with a view to defining their selection. Findings – The predictive capacity of the model was also found to vary depending on the source of the liquidity, in so far as the data on liquidity within the private sector contributed more than the data on public sector liquidity to the prediction of economic fluctuations. International liquidity was seen as a more diffuse concept, and the standardization of its definition could be the focus of future studies. A benchmarking process was also performed when applying the state-of-the-art machine learning models.  \nOriginality/value–Better understanding of these variables might help us toward a deeper understanding of the operation of financial markets. Liquidity, one of the key financial market variables, is neither well-defined nor standardized in the existing literature, which calls for further study. Hence, the novelty of an applied study employing modern data science techniques can provide a fresh perspective on financial markets.  \nKeywords Data science, Finance, International markets, Machine learning liquidity, Treasury bond Paper type Research paper  \n1. Introduction  \nThe foundation of the present study is the concept of liquidity as a key financial indicator with which to predict the behavior of financial markets.  \nLiquidity is the flow of capital and credit within the global financial system. A concept that both the Bank for International Settlements and the Federal Reserve System apply, as well as many other financial institutions, as is reflected in the Fed Financial Stability Report. The concept of liquidity is approached in this study, so as to analyze financial stability, to anticipate systemic risk and particularly to analyze capital management among certain private sector investors.  \nThe area of greatest economic significance in relation to liquidity is the central banking community, in which the term “Financial conditions” is also used. This concept is equivalent to the underlying idea behind liquidity. The capability to anticipate financial instability helps policymakers to make decisions on monetary policy, and it is likewise decisive for capital management among certain investors.  \n© Ignacio Manuel Luque Raya and Pablo Luque Raya. Published in European Journal of Management and Business Economics. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4 .0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and noncommercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at [http://](http://)[ ](http://)[creativecommons.org/licences/by/4.0/legalcode](creativecommons.org/licences/by/4.0/legalcode)  \nEstimation of liquidity  \nReceived 1 August 2022 Revised 22 January 2023  \n8 March 2023 Accepted 12 March 2023  \nEuropean Journal of Management  \nand Business Economics Emerald Publishing Limited e-ISSN: 2444-8494  \np-ISSN: 2444-8451 DOI 10. 1108/EJMBE-06-2022-0176  \nEJMBE Alessi and Detken ","cbCaicAEYwKiVLEW","https://ap.wps.com/l/cbCaicAEYwKiVLEW","pdf",6976155,1,25,"English","en",105,"# Abstract\n## Purpose\n## Design/methodology/approach\n## Findings\n## Originality/value\n# Introduction\n## Liquidity as a financial indicator\n## Liquidity and financial instability\n# Estimation of liquidity","[{\"question\":\"What is the main purpose of the study?\",\"answer\":\"To assess the predictive capacity of variables representative of liquidity so that economic fluctuations can be better understood.\"},{\"question\":\"How do the authors approach liquidity in their modeling?\",\"answer\":\"They formulate predictions using conceptual variables that represent liquidity and compare the results of multiple machine learning models.\"},{\"question\":\"What factor most affects predictive performance in the findings?\",\"answer\":\"Predictive capacity varies by the source of liquidity; data on private-sector liquidity contributes more than public-sector liquidity.\"}]","Machine learning algorithms applied to the estimation of liquidity: the 10-year United States treasury bond - Research paper | PDF",1785819239,63,{"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},"machine-learning-algorithms-applied-to-the-estimation-of-liquidity-the-10-year-united-states-treasury-bond-research-paper","",{"@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/machine-learning-algorithms-applied-to-the-estimation-of-liquidity-the-10-year-united-states-treasury-bond-research-paper/123919/",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","2026-08-04",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 is the main purpose of the study?","Question",{"text":75,"@type":76},"To assess the predictive capacity of variables representative of liquidity so that economic fluctuations can be better understood.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do the authors approach liquidity in their modeling?",{"text":80,"@type":76},"They formulate predictions using conceptual variables that represent liquidity and compare the results of multiple machine learning models.",{"name":82,"@type":73,"acceptedAnswer":83},"What factor most affects predictive performance in the findings?",{"text":84,"@type":76},"Predictive capacity varies by the source of liquidity; 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