{"id":{"repo_id":"umn","oai_identifier":"oai:conservancy.umn.edu:11299/188792"},"canonical_url":"https://search.dev.ndltd.org/etd/umn/oai:conservancy.umn.edu:11299/188792","repository":{"repo_id":"umn","name":"University of Minnesota","base_url":"https://conservancy.umn.edu/server/oai/request"},"display":{"title":"Applying Supervised Machine Learning Techniques to Municipal Bond Trading","abstract":"In this paper we will examine how artificial intelligence or machine learning can be used to make better municipal bond trading decisions. The paper will examine a variety of classification models trained in a supervised environment. The paper will discuss: i. How to prepare data for machine learning analysis ii. The basic mathematical concepts of each model iii. The results of each model and how to interpret them iv. How to fine‐tune parameters for optimal performance","abstract_html":"In this paper we will examine how artificial intelligence or machine learning can be used to make better municipal bond trading decisions. The paper will examine a variety of classification models trained in a supervised environment. The paper will discuss: i. How to prepare data for machine learning analysis ii. The basic mathematical concepts of each model iii. The results of each model and how to interpret them iv. How to fine‐tune parameters for optimal performance","abstract_has_math":false,"creators":["Jacobus, Roland"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-05","date_published":"2017-05","updated_at":"2026-07-24T05:20:07Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11299/188792","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Jacobus, Roland"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2017-07-18T14:11:59Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2017-07-18T14:11:59Z"]},{"key":"dc:date.issued","label":"Date","values":["2017-05"]},{"key":"dc:type","label":"Dc Type","values":["Thesis or Dissertation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11299/188792"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["University of Minnesota M.S. thesis. May 2017. Major: Applied and Computational Mathematics. Advisor: Fadil Santosa. 1 computer file (PDF); x, 55 pages."]},{"key":"dc:description.abstract","label":"Abstract","values":["In this paper we will examine how artificial intelligence or machine learning can be used to make better municipal bond trading decisions. The paper will examine a variety of classification models trained in a supervised environment. The paper will discuss: i. How to prepare data for machine learning analysis ii. The basic mathematical concepts of each model iii. The results of each model and how to interpret them iv. How to fine‐tune parameters for optimal performance"]},{"key":"dc:title","label":"Title","values":["Applying Supervised Machine Learning Techniques to Municipal Bond Trading"]}]}],"canonical_facts":{"dc:creator":["Jacobus, Roland"],"dc:date.accessioned":["2017-07-18T14:11:59Z"],"dc:date.available":["2017-07-18T14:11:59Z"],"dc:date.issued":["2017-05"],"dc:description":["University of Minnesota M.S. thesis. May 2017. Major: Applied and Computational Mathematics. Advisor: Fadil Santosa. 1 computer file (PDF); x, 55 pages."],"dc:description.abstract":["In this paper we will examine how artificial intelligence or machine learning can be used to make better municipal bond trading decisions. The paper will examine a variety of classification models trained in a supervised environment. The paper will discuss: i. How to prepare data for machine learning analysis ii. The basic mathematical concepts of each model iii. The results of each model and how to interpret them iv. How to fine‐tune parameters for optimal performance"],"dc:identifier.uri":["http://hdl.handle.net/11299/188792"],"dc:language.iso":["en"],"dc:title":["Applying Supervised Machine Learning Techniques to Municipal Bond Trading"],"dc:type":["Thesis or Dissertation"]},"updated_at":"2026-07-24T05:20:07Z"}