[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122095-en":3,"doc-seo-122095-105":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},122095,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Max Turunen - Modeling Indirect Market Impact of Limit Order Book Messages with Machine Learning Methods","This study proposes and evaluates a methodology for modeling the indirect market impact of limit order book (LOB) messages using state-of-the-art machine learning models and early data fusion. The objective is to assess whether LOB message information improves price prediction accuracy and how different message types—submissions, cancellations, and executions—affect market behavior individually and collectively. Using Nasdaq TotalView-ITCH 5.0 data on Apple, Facebook, Google, Intel, and Microsoft, 2400 models are trained and compared via averaged performance metrics.","Max Turunen  \nMODELING INDIRECT MARKET IMPACT OF LIMIT ORDER BOOK MESSAGES WITH MACHINE LEARNING METHODS  \nMaster of Science Thesis  \nFaculty of Information Technology and Communication Sciences Examiners: Prof. Juho Kanniainen  \nProf. Moncef Gabbouj  \ni  \nABSTRACT  \nMax Turunen: Modeling Indirect Market Impact of Limit Order Book Messages with Machine Learning Methods  \nMaster of Science Thesis Tampere University  \nComputer Sciences, Data Science August 2024  \nThis study proposes and evaluates a new methodology to study indirect market impact of limit order book (LOB) messages using state-of-the-art machine learning models and early data fusion. The primary objective is to evaluate the efficacy of the proposed methodology in measuring the indirect market impact of LOB messages. The research utilises data from Nasdaq TotalView-ITCH 5.0, focusing on Apple, Facebook, Google, Intel, and Microsoft stocks.  \nThe methodology involves integrating LOB message data into machine learning models to assess their effect on the accuracy of price predictions. The study examines the different types of LOB messages, such as order submissions, cancellations, and executions, to determine their individual and collective impact on market behavior.  \nA total of 2400 models were trained for the study. These models were trained in sets of ten, each set utilising identical hyperparameters except for ten distinct run seed numbers. Each set often models comprises a model group trained with messages and another group trained with the same hyperparameters but without LOB message data. To determine whether LOB messages resulted in improved accuracy, the metrics of the models were averaged and compared.  \nAmong the averaged models, 47% exhibited improved R2 and MSE scores with the inclusion of LOB messages. However, the remaining 53% of the models demonstrated poorer performance. Notably, when the inclusion of LOB messages impacted accuracy positively, the magnitude of this improvement was greater than the magnitude of the negative impact when it occurred.  \nThe results demonstrate that incorporating LOB messages does not significantly improve the accuracy of price prediction. However, the models are capable of modelling the LOB messages ina manner that provides insights into the indirect market impact of various types of messages, albeit the modelled indirect market impact is not robust and consistent across all stocks and parameters. This may suggest that either the indirect market impact is specific to stocks and prediction horizons or that the proposed models are in fact modeling noise. Concavity of the majority of indirect market impact functions validates that indirect market impact is being modelled, as this is a wellestablished concept in the field.  \nThe findings illustrate how the selected machine learning models may capture the market impact of various message types as variations in message price and quantity are introduced, while other message variables remain constant. This research highlights the potential of machine learning in refining market impact analysis and proposes avenues for future studies in this domain.  \nKeywords: indirect market impact, machine learning, data fusion, mid-price prediction, limit order book messages, limit order book  \nThe originality of this thesis has been checked using the Turnitin OriginalityCheck service.  \nii  \nTIIVISTELMÄ  \nMax Turunen: Tilakirjaviestien epäsuoran markkinavaikutuksen mallintaminen koneoppimismenetelmin  \nPro gradu-tutkielma Tampereen yliopisto Tietojenkäsittelyoppi, datatiede Elokuu 2024  \nTässä tutkimuksessa ehdotetaan ja arvioidaan uutta metodologiaa tilakirjaviestien (LOB) epäsuoran markkinavaikutuksen tutkimiseksi käyttämällä edistyneitä koneoppimismalleja ja varhaista datafuusiota. Ensisijaisena tavoitteena on arvioida ehdotetun menetelmän tehokkuutta LOBviestien epäsuoran markkinavaikutuksen mittaamisessa. Tutkimuksessa hyödynnetään Nasdaq TotalView-ITCH 5.0:n dataa tutkien Applen, Fac","cbCaifhjHJ9Sl8u1","https://ap.wps.com/l/cbCaifhjHJ9Sl8u1","pdf",9436809,1,84,"English","en",105,"# Abstract\n## Methodology\n## Data and Experimental Setup\n## Results\n## Implications\n# Keywords","[{\"question\":\"What is the main goal of the proposed methodology?\",\"answer\":\"To evaluate how effectively machine learning can measure the indirect market impact of limit order book messages.\"},{\"question\":\"Which LOB message types are analyzed in the study?\",\"answer\":\"Order submissions, cancellations, and executions are examined for their individual and collective impact on market behavior.\"},{\"question\":\"Do LOB messages improve price prediction accuracy overall?\",\"answer\":\"On average, incorporating LOB messages does not significantly improve prediction accuracy, though some models show improved metrics while others perform worse.\"}]","Max Turunen - Modeling Indirect Market Impact of Limit Order Book Messages with Machine Learning Methods | PDF",1785808793,212,{"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},"max-turunen-modeling-indirect-market-impact-of-limit-order-book-messages-with-machine-learning-methods","",{"@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/max-turunen-modeling-indirect-market-impact-of-limit-order-book-messages-with-machine-learning-methods/122095/",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 goal of the proposed methodology?","Question",{"text":75,"@type":76},"To evaluate how effectively machine learning can measure the indirect market impact of limit order book messages.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which LOB message types are analyzed in the study?",{"text":80,"@type":76},"Order submissions, cancellations, and executions are examined for their individual and collective impact on market behavior.",{"name":82,"@type":73,"acceptedAnswer":83},"Do LOB messages improve price prediction accuracy overall?",{"text":84,"@type":76},"On average, incorporating LOB messages does not significantly improve prediction accuracy, though some models show improved metrics while others perform worse.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]