[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126378-en":3,"doc-seo-126378-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126378,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","High-Frequency Trading Liquidity Analysis - Application of Machine Learning Classification","High-Frequency Trading Liquidity Analysis focuses on how liquidity conditions shape transaction costs and market stability, and how liquidity shortfalls during trading hours can amplify costs and risk. Building on prior work clustering liquidity metrics and detecting outliers, the study proposes a robust framework using HFT data to identify and manage liquidity risk, produce statistical liquidity-based models, and generate inputs for broader financial network evaluations. Data processing relies on TAQ and limit order book datasets from Refi nitiv Tick History to compute predictive liquidity measures.","arXiv :2408 . 10016v1 [ q-fin .TR] 19 Aug 2024  \nStevens Institute of Technology  \nHigh-Frequency Trading Liquidity Analysis  \nApplication of Machine Learning Classi􀀌cation  \nSid Bhatia, Sidharth Peri,  \nSam Friedman, Michelle Malen  \nAugust 5, 2024  \n1 Introduction  \nLiquidity is fundamentally intertwined with the dynamics of 􀀌nancial markets and plays a critical role in in􀀍uencing transaction costs and market stability. A de􀀌ciency in liquidity during trading hours can dramatically increase transaction costs, adversely impacting market stability. Utilizing established models, we have derived several liquidity measures from various market characteristics, including tightness, depth, resiliency, and trading dynamics.  \nIn previous research, we focused on liquidity measures derived from trades and the limit order book, e􀀋ectively clustering relevant liquidity metrics and identifying signi􀀌cant outlier events. This current research builds on these foundations by proposing to develop a robust framework for analyzing liquidity using High-Frequency Trading (HFT) data. The primary goal of this study is to uncover critical insights across multiple domains, including the identi􀀌cation and management of liquidity risk, the creation of statistical models based on liquidity analysis, and the generation of novel inputs for comprehensive 􀀌nancial network evaluations.  \n2 Literature Review  \n2.1 Macroeconomic Events and Liquidity  \nThe impact of macroeconomic events on liquidity has been extensively studied, with signi􀀌cant contributions from researchers such as Kong. Their analysis of liquidity measures during Brexit highlightshow geopolitical events can lead to substantial 􀀍uctuations in market liquidity, a􀀋ecting both market stability and e􀀎ciency. This study provides a foundation for understanding the broader implications of macroeconomic changes on 􀀌nancial systems.  \n2.2 Detection of Rare Events  \nThe methodology for detecting rare events in 􀀌nancial markets has been advanced by Golbayani and Bozdog, who employed Zonoid depth functions to identify outlier events in 􀀌nancial datasets. Their work is crucial for understanding how extreme market conditions can disrupt market equilibrium and a􀀋ect liquidity, thereby helping market analysts and traders anticipate and mitigate potential risks.  \n2.3 Political Turmoil and Market Liquidity  \nExploring the direct impact of political events on market dynamics, Mago and others investigate liquidity risks and asset movements during the Brexit referendum. Their 􀀌ndings underscore the challenges markets face during periods of political uncertainty and the heightened liquidity risks that can arise, stressing the need for e􀀋ective risk management strategies to combat these e􀀋ects.  \n2.4 Cluster Analysis in Liquidity Measurement  \nSalighehdar and others contribute to liquidity modeling by employing cluster analysis on high-frequency data to study liquidity measures in stock markets. Their research demonstrates that liquidity can be effectively predicted through advanced statistical techniques, providing crucial insights for the development of more resilient 􀀌nancial strategies.  \n2.5 Multidimensional Analysis of Liquidity  \nZaika extends the analysis of liquidity measures by focusing on rare events and their multidimensional characteristics. This approach enriches our understanding of liquidity beyond traditional models, o􀀋ering comprehensive strategies to handle liquidity under diverse market conditions, especially in high-frequency trading scenarios.  \n2.6 Geopolitical Impact on Liquidity Distribution  \nFurther emphasizing the sensitivity of markets to geopolitical changes, Kong and others examine how Brexit in􀀍uenced liquidity distribution characteristics. This study aligns with other research by illustrating the profound impact external shocks can have on liquidity and the ongoing need for 􀀌nancial markets to adapt to these changes dynamically.  \nThese sections collectively underscore the complexit","cbCaibqi1dQOpw3Z","https://ap.wps.com/l/cbCaibqi1dQOpw3Z","pdf",73776,4,1,13,"English","en",105,"# Introduction\n## Liquidity and market stability\n## Research objectives\n# Literature Review\n## Macroeconomic events and liquidity\n## Detection of rare events\n## Political turmoil and market liquidity\n## Cluster analysis in liquidity measurement\n## Multidimensional analysis of liquidity\n## Geopolitical impact on liquidity distribution\n# Research Agenda\n## Study objectives\n## Planned activities\n# Methodology & Data\n## Data collection\n## Data sampling and reduction\n# Predictive Model Development\n## Model overview\n## Model optimization","[{\"question\":\"Why is liquidity important in high-frequency trading?\",\"answer\":\"Liquidity is tightly linked to financial market dynamics and influences transaction costs and market stability. Low liquidity during trading hours can sharply increase transaction costs and worsen stability.\"},{\"question\":\"What data sources does the study use to compute liquidity measures?\",\"answer\":\"The methodology centers on TAQ data and limit order book (LOB) information, using high-frequency data from the Refinitiv Tick History Dataset.\"},{\"question\":\"How is the predictive task formulated for machine learning models?\",\"answer\":\"The model treats price movement direction as a classification target, labeled Up or Down, and evaluates classifiers such as Logistic Regression, Support Vector Machine, and Random Forest.\"}]","High-Frequency Trading Liquidity Analysis - Application of Machine Learning Classification | PDF",1785904748,33,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"high-frequency-trading-liquidity-analysis-application-of-machine-learning-classification","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/high-frequency-trading-liquidity-analysis-application-of-machine-learning-classification/126378/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is liquidity important in high-frequency trading?","Question",{"text":76,"@type":77},"Liquidity is tightly linked to financial market dynamics and influences transaction costs and market stability. Low liquidity during trading hours can sharply increase transaction costs and worsen stability.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What data sources does the study use to compute liquidity measures?",{"text":81,"@type":77},"The methodology centers on TAQ data and limit order book (LOB) information, using high-frequency data from the Refinitiv Tick History Dataset.",{"name":83,"@type":74,"acceptedAnswer":84},"How is the predictive task formulated for machine learning models?",{"text":85,"@type":77},"The model treats price movement direction as a classification target, labeled Up or Down, and evaluates classifiers such as Logistic Regression, Support Vector Machine, and Random Forest.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]