{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/20133"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/20133","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Noise trader sentiment and the behavior of futures prices","abstract":"In this research, the noise trader sentiment model of De Long, Schleifer, Summers, and Waldmann is modified and applied to futures markets. The theoretical model predicts that overly optimistic (pessimistic) noise traders result in futures prices that are greater (less) than fundamental value. More specifically, the model indicates that futures prices contain a systematic bias proportional to the average level of sentiment, and market returns are predictable with the level of noise trader sentiment.","abstract_html":"In this research, the noise trader sentiment model of De Long, Schleifer, Summers, and Waldmann is modified and applied to futures markets. The theoretical model predicts that overly optimistic (pessimistic) noise traders result in futures prices that are greater (less) than fundamental value. More specifically, the model indicates that futures prices contain a systematic bias proportional to the average level of sentiment, and market returns are predictable with the level of noise trader sentiment.","abstract_has_math":false,"creators":["Sanders, Dwight Robert"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Agricultural Economics","degree_department":null,"school":null,"contributors":["Leuthold, Raymond M."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-05-07T12:29:52Z","date_published":"2011-05-07T12:29:52Z","updated_at":"2026-07-22T22:25:15Z","subjects":["Economics, Agricultural","Economics, Finance"],"languages":["eng"],"rights":["Copyright 1995 Sanders, Dwight Robert"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["AAI9624479","(UMI)AAI9624479"],"render_values":[{"text":"AAI9624479","href":null,"code":true},{"text":"(UMI)AAI9624479","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/20133","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Leuthold, Raymond M."]},{"key":"dc:creator","label":"Author","values":["Sanders, Dwight Robert"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2011-05-07T12:29:52Z","10000-01-01","1995"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Agricultural Economics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["Economics, Agricultural","Economics, Finance"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 1995 Sanders, Dwight Robert"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["AAI9624479","(UMI)AAI9624479","http://hdl.handle.net/2142/20133"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In this research, the noise trader sentiment model of De Long, Schleifer, Summers, and Waldmann is modified and applied to futures markets. The theoretical model predicts that overly optimistic (pessimistic) noise traders result in futures prices that are greater (less) than fundamental value. More specifically, the model indicates that futures prices contain a systematic bias proportional to the average level of sentiment, and market returns are predictable with the level of noise trader sentiment.","Couched within Muth's rational forecasting framework, the model's predictions are tested on data from twenty-eight futures markets. Noise trader sentiment is proxied with commercial market sentiment indices. These data allow the formation of well-defined alternative hypotheses and, hence, more powerful statistical tests than found in previous research. The analysis reveals that the behavior of the commercial sentiment indices is consistent with noise trader sentiment as a theoretical construct. Fama-MacBeth cross-sectional regressions indicate that noise traders do not create a systematic bias in futures prices. Return predictability is evaluated with the Cumby-Modest market timing test and Granger causality tests. Sentiment is useful for predicting returns in only a few isolated cases. Overall, there is little evidence that noise trader sentiment impacts future prices.","This research documents the existence of noise trader sentiment in futures markets and provides a comprehensive test for its effect. The empirical evidence provides only weak support for theoretical noise trader models. Importantly, the research establishes a unique data set for examining noise trader effects, thereby laying a groundwork for future research.","Made available in DSpace on 2011-05-07T12:29:52Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9624479.pdf: 7319681 bytes, checksum: 20c4bb320f97ae5edd1bbe48945c35d5 (MD5) Previous issue date: 1995","Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:41:47Z Item is restricted indefinitely.","Restriction data tranferred 2014-07-01T11:18:07-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission","ETDs are only available to UIUC Users without author permission","U of I Only"]},{"key":"dc:title","label":"Title","values":["Noise trader sentiment and the behavior of futures prices"]}]}],"canonical_facts":{"dc:contributor":["Leuthold, Raymond M."],"dc:creator":["Sanders, Dwight Robert"],"dc:date":["2011-05-07T12:29:52Z","10000-01-01","1995"],"dc:description":["In this research, the noise trader sentiment model of De Long, Schleifer, Summers, and Waldmann is modified and applied to futures markets. The theoretical model predicts that overly optimistic (pessimistic) noise traders result in futures prices that are greater (less) than fundamental value. More specifically, the model indicates that futures prices contain a systematic bias proportional to the average level of sentiment, and market returns are predictable with the level of noise trader sentiment.","Couched within Muth's rational forecasting framework, the model's predictions are tested on data from twenty-eight futures markets. Noise trader sentiment is proxied with commercial market sentiment indices. These data allow the formation of well-defined alternative hypotheses and, hence, more powerful statistical tests than found in previous research. The analysis reveals that the behavior of the commercial sentiment indices is consistent with noise trader sentiment as a theoretical construct. Fama-MacBeth cross-sectional regressions indicate that noise traders do not create a systematic bias in futures prices. Return predictability is evaluated with the Cumby-Modest market timing test and Granger causality tests. Sentiment is useful for predicting returns in only a few isolated cases. Overall, there is little evidence that noise trader sentiment impacts future prices.","This research documents the existence of noise trader sentiment in futures markets and provides a comprehensive test for its effect. The empirical evidence provides only weak support for theoretical noise trader models. Importantly, the research establishes a unique data set for examining noise trader effects, thereby laying a groundwork for future research.","Made available in DSpace on 2011-05-07T12:29:52Z (GMT). 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