{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105959"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105959","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Deep learning models for high-frequency financial data","abstract":"The limit order book of a financial instrument represents its supply and demand at each point in time. The limit order book data can be used to predict the future price of the financial instrument. We develop deep learning models to capture the high dimensional data distributions (on R^d) of the limit order data. These models exploit the underlying structure of this complex data. We develop a uniform data grid model for limit order book data to achieve state-of-the-art accuracy for predicting price changes in a stock. We also develop a novel way to use non-uniform events from the limit order book data to train a non-uniform grid data model. This model substantially and consistently outperforms our uniform data grid model. Both the models have been trained and tested over a wide range of periods spanning multiple years for many stocks. The out-of-sample predictions are stable across time for both the models as shown by tests for multiple stocks. Given the huge size of the dataset we use a cluster of CPUs and GPUs to perform our experiments.","abstract_html":"The limit order book of a financial instrument represents its supply and demand at each point in time. The limit order book data can be used to predict the future price of the financial instrument. We develop deep learning models to capture the high dimensional data distributions (on R^d) of the limit order data. These models exploit the underlying structure of this complex data. We develop a uniform data grid model for limit order book data to achieve state-of-the-art accuracy for predicting price changes in a stock. We also develop a novel way to use non-uniform events from the limit order book data to train a non-uniform grid data model. This model substantially and consistently outperforms our uniform data grid model. Both the models have been trained and tested over a wide range of periods spanning multiple years for many stocks. The out-of-sample predictions are stable across time for both the models as shown by tests for multiple stocks. Given the huge size of the dataset we use a cluster of CPUs and GPUs to perform our experiments.","abstract_has_math":false,"creators":["Abhinav, -"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Peng, Jian","Sirignano, Justin"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-11-26T20:59:50Z","date_published":"2019-11-26T20:59:50Z","updated_at":"2026-07-22T22:24:45Z","subjects":["Deep learning","finance","limit order book","high frequency data","time series","lstm","non uniform time series","state of the art"],"languages":["en"],"rights":["Copyright 2019 Abhinav Kohar"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105959","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Peng, Jian","Sirignano, Justin"]},{"key":"dc:creator","label":"Author","values":["Abhinav, -"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-11-26T20:59:50Z","2021-11-27T10:15:34Z","2019-07-18","2019-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Deep learning","finance","limit order book","high frequency data","time series","lstm","non uniform time series","state of the art"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Abhinav Kohar"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105959"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The limit order book of a financial instrument represents its supply and demand at each point in time. The limit order book data can be used to predict the future price of the financial instrument. We develop deep learning models to capture the high dimensional data distributions (on R^d) of the limit order data. These models exploit the underlying structure of this complex data. We develop a uniform data grid model for limit order book data to achieve state-of-the-art accuracy for predicting price changes in a stock. We also develop a novel way to use non-uniform events from the limit order book data to train a non-uniform grid data model. This model substantially and consistently outperforms our uniform data grid model. Both the models have been trained and tested over a wide range of periods spanning multiple years for many stocks. The out-of-sample predictions are stable across time for both the models as shown by tests for multiple stocks. Given the huge size of the dataset we use a cluster of CPUs and GPUs to perform our experiments.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-08-01","The student, - Abhinav, accepted the attached license on 2019-07-17 at 15:37.","The student, - Abhinav, submitted this Thesis for approval on 2019-07-17 at 15:46.","This Thesis was approved for publication on 2019-07-18 at 13:17.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14365 on 2019-11-26 at 14:04:23","Made available in DSpace on 2019-11-26T20:59:50Z (GMT). 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The limit order book data can be used to predict the future price of the financial instrument. We develop deep learning models to capture the high dimensional data distributions (on R^d) of the limit order data. These models exploit the underlying structure of this complex data. We develop a uniform data grid model for limit order book data to achieve state-of-the-art accuracy for predicting price changes in a stock. We also develop a novel way to use non-uniform events from the limit order book data to train a non-uniform grid data model. This model substantially and consistently outperforms our uniform data grid model. Both the models have been trained and tested over a wide range of periods spanning multiple years for many stocks. The out-of-sample predictions are stable across time for both the models as shown by tests for multiple stocks. Given the huge size of the dataset we use a cluster of CPUs and GPUs to perform our experiments.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-08-01","The student, - Abhinav, accepted the attached license on 2019-07-17 at 15:37.","The student, - Abhinav, submitted this Thesis for approval on 2019-07-17 at 15:46.","This Thesis was approved for publication on 2019-07-18 at 13:17.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14365 on 2019-11-26 at 14:04:23","Made available in DSpace on 2019-11-26T20:59:50Z (GMT). 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