[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-152910-105":59,"doc-detail-152910-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","predicting-intraday-jumps-in-stock-prices-using-liquidity-measures-and-technical-indicators","Predicting Intraday Jumps in Stock Prices Using Liquidity Measures and Technical Indicators","","Intraday stock jumps represent large, discontinuous return movements and remain difficult to forecast because they occur instantly with limited or imperceptible precursors. This paper presents a data-driven prediction method that uses information embedded in liquidity measures and technical indicators. A trading day is split into 5-minute intervals, candidate features are generated at the end of each interval, and machine learning models predict both the arrival and direction of a jump in the following interval. Validation on Shenzhen level-2 data for 1271 stocks shows initial predictive evidence and demonstrates the superiority of random forest.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/predicting-intraday-jumps-in-stock-prices-using-liquidity-measures-and-technical-indicators/152910/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/predicting-intraday-jumps-in-stock-prices-using-liquidity-measures-and-technical-indicators/152910.png","ImageObject",300,407,{"name":92,"@type":93},"Levi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-08-27",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is predicting intraday stock jumps challenging?","Question",{"text":113,"@type":114},"Intraday jumps occur instantly and typically have imperceptible precursors, making it difficult to capture predictive signals before the jump arrives.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How is the prediction task formulated in the paper?",{"text":118,"@type":114},"Each trading day is divided into 5-minute intervals, and the model uses features computed at the end of an interval to predict whether a jump (and its direction) will occur in the next 5 minutes.",{"name":120,"@type":111,"acceptedAnswer":121},"What data and models are used to validate the approach?",{"text":122,"@type":114},"The method is tested on level-2 high-frequency data from 1271 Shenzhen-listed stocks, using machine learning algorithms; random forest is reported as superior to other approaches for building prediction models.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},152910,1787868244,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Predicting intraday jumps in stock prices using liquidity measures and technical indicators  \nAo Kong*  \nSchool of Finance, Nanjing University of Finance and Economics, Nanjing 210023, China  \nHongliang Zhu  \nSchool of Management and Engineering, Nanjing University, Nanjing 210023, China  \nRobert Azencott  \nDepartment of Mathematics, University of Houston, TX 77204, USA  \nAbstract  \nPredicting the intraday stock jumps is a signi􀀌cant but challenging problem in  \n􀀌nance. Due to the instantaneity and imperceptibility characteristics of intra  \nday stock jumps, relevant studies on their predictability remain limited. This  \npaper proposes a data-driven approach to predict intraday stock jumps using the  \ninformation embedded in liquidity measures and technical indicators. Specif  \nically, a trading day is divided into a series of 5-minute intervals, and at the  \nend of each interval, the candidate attributes de􀀌ned by liquidity measures and  \ntechnical indicators are input into machine learning algorithms to predict the  \narrival of a stock jump as well as its direction in the following 5-minute interval.  \nEmpirical study is conducted on the level-2 high-frequency data of 1271 stocks  \nin the Shenzhen Stock Exchange of China to validate our approach. The result  \nprovides initial evidence of the predictability of jump arrivals and jump direc  \ntions using level-2 stock data as well as the e􀀋ectiveness of using a combination  \nof liquidity measures and technical indicators in this prediction. We also re  \nveal the superiority of using random forest compared to other machine learning  \nalgorithms in building prediction models. Importantly, our study provides a  \nportable data-driven approach that exploits liquidity and technical information  \nfrom level-2 stock data to predict intraday price jumps of individual stocks.  \nPreprint submitted to Journal of Forecasting July 23, 2020  \nThis article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as doi: 􀀔􀀓􀀑􀀔􀀓􀀓􀀕􀀒IRU􀀑􀀕􀀚􀀕􀀔  \nKeywords: Intraday stock jumps, High-frequency data, Liquidity measures,  \nTechnical indicators, Machine learning  \n1. Introduction  \nStock price jumps refer to large discontinuous changes in the path of stock  \nreturns and are mainly associated with company-speci􀀌c events, market news  \nannouncements or large arbitrage hedging activities (Merton, 1976; Lee and  \nMykland, 2008; Boudt and Pertitjean, 2014) . Compared with continuous price  \nchanges, stock jumps bring higher risk, leading to di􀀋erent applications in risk  \nmanagement, option pricing and asset allocation. While various studies of stock  \njumps, such as jump detection, causes of jumps, jump modelling and the e􀀋ect  \nof jumps on the market, have attracted much attention(Lee and Mykland, 2008;  \nEvans, 2011; Lee and Mykland, 2012; Boudt and Pertitjean, 2014; Huang, 2018;  \nMa et al., 2019), research on the predictability of stock jumps, especially under  \nthe intraday scenario, is very limited. This limitation might be because stock  \njumps normally occur instantly with imperceptible precursors, which makes it  \nvery challenging to capture the predictive features before their arrival. This  \nstudy thus attempts to complement the literature in this area by designing an  \napproach to predict intraday stock jumps and testing the method on thousands  \nof stocks in a Chinese stock market.  \nTo design a mechanism for jump prediction, statistically signi􀀌cant jumps in  \nstock prices must be detected accurately. Most jump identi􀀌cation methods are  \ndesigned for low-frequency data, which can only identify the existence of jumps  \nwithin a day. Recent development of volatility modelling with high-frequency  \nprice series promotes advanced methods to detect the intraday timing of jump  \narrivals(Lee and Myklan","cbCaisCf03B245cE","https://ap.wps.com/l/cbCaisCf03B245cE","pdf",2181800,47,"English","# Introduction\n## Problem background and motivation\n## Jump detection and intraday timing\n## Liquidity measures vs. technical indicators\n## Proposed combined prediction approach","[{\"question\":\"Why is predicting intraday stock jumps challenging?\",\"answer\":\"Intraday jumps occur instantly and typically have imperceptible precursors, making it difficult to capture predictive signals before the jump arrives.\"},{\"question\":\"How is the prediction task formulated in the paper?\",\"answer\":\"Each trading day is divided into 5-minute intervals, and the model uses features computed at the end of an interval to predict whether a jump (and its direction) will occur in the next 5 minutes.\"},{\"question\":\"What data and models are used to validate the approach?\",\"answer\":\"The method is tested on level-2 high-frequency data from 1271 Shenzhen-listed stocks, using machine learning algorithms; random forest is reported as superior to other approaches for building prediction models.\"}]","Predicting Intraday Jumps in Stock Prices Using Liquidity Measures and Technical Indicators | PDF",118]