{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129687"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129687","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Time series forecasting of stock price changes using large language models: A foundation for financial decision-making","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","abstract_has_math":false,"creators":["Go, Eun"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Banerjee, Arindam"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-04-15","date_published":"2025-04-15","updated_at":"2026-07-22T22:25:05Z","subjects":["Time series forecasting","Large language models"],"languages":["en","eng"],"rights":["Copyright 2025 Eun Go"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129687","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Banerjee, Arindam"]},{"key":"dc:creator","label":"Author","values":["Go, Eun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-04-15","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"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 Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Time series forecasting","Large language models"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Eun Go"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129687"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","The student, Eun Go, accepted the attached license on 2025-04-14 at 20:36.","The student, Eun Go, submitted this Thesis for approval on 2025-04-14 at 20:47.","This Thesis was approved for publication on 2025-04-15 at 05:41.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21768 on 2025-10-19 at 19:52:52","Forecasting stock price changes is a fundamental task in financial decision-making, yet it remains highly challenging due to the noisy, non-stationary nature of financial time series. This thesis investigates the use of a foundation model—Chronos, a large language model (LLM)-based forecaster—for multi-horizon stock price change prediction. Unlike traditional models that focus on absolute price forecasting, we formulate the task around predicting future price differences, which are more actionable in trading and portfolio optimization contexts. We evaluate Chronos under multiple configurations, comparing zero-shot and fine-tuned settings across several input formats, including raw prices, daily differences, and horizon-based changes. Extensive experiments on U.S. stock data demonstrate that fine-tuning Chronos significantly improves predictive performance, especially at longer horizons. Among the formats, daily price differences yield the most stable and interpretable results. An ablation study on context length further reveals trade-offs between longer historical windows and increased noise. While no control policy is implemented in this work, we propose a future integration of Chronos into a model predictive control (MPC) framework for multi-period financial planning. This thesis concludes that LLM-based forecasters like Chronos are promising tools for time series prediction in finance, especially when paired with domain-specific fine-tuning and careful input design."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Time series forecasting of stock price changes using large language models: A foundation for financial decision-making"]}]}],"canonical_facts":{"dc:contributor":["Banerjee, Arindam"],"dc:creator":["Go, Eun"],"dc:date":["2025-04-15","2025-05"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","The student, Eun Go, accepted the attached license on 2025-04-14 at 20:36.","The student, Eun Go, submitted this Thesis for approval on 2025-04-14 at 20:47.","This Thesis was approved for publication on 2025-04-15 at 05:41.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21768 on 2025-10-19 at 19:52:52","Forecasting stock price changes is a fundamental task in financial decision-making, yet it remains highly challenging due to the noisy, non-stationary nature of financial time series. This thesis investigates the use of a foundation model—Chronos, a large language model (LLM)-based forecaster—for multi-horizon stock price change prediction. Unlike traditional models that focus on absolute price forecasting, we formulate the task around predicting future price differences, which are more actionable in trading and portfolio optimization contexts. We evaluate Chronos under multiple configurations, comparing zero-shot and fine-tuned settings across several input formats, including raw prices, daily differences, and horizon-based changes. Extensive experiments on U.S. stock data demonstrate that fine-tuning Chronos significantly improves predictive performance, especially at longer horizons. Among the formats, daily price differences yield the most stable and interpretable results. An ablation study on context length further reveals trade-offs between longer historical windows and increased noise. While no control policy is implemented in this work, we propose a future integration of Chronos into a model predictive control (MPC) framework for multi-period financial planning. This thesis concludes that LLM-based forecasters like Chronos are promising tools for time series prediction in finance, especially when paired with domain-specific fine-tuning and careful input design."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129687"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Eun Go"],"dc:subject":["Time series forecasting","Large language models"],"dc:title":["Time series forecasting of stock price changes using large language models: A foundation for financial decision-making"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}