\"There are many different strategies to predict the stock market. When selecting a strategy to predict the stock market, that strategy must be robust and be able to handle unexpected events. This paper analyzes algorithms that are based on human psychology instead of just looking for patterns in the data. It also attempts to find optimal parameters for the algorithms and see if their performance will persist in the future and with trading costs. Finally, this paper looks at algorithms that are able to combine the signals of other algorithms and see how well they perform with and without trading costs\"--Leaf iv.
","abstract_html":"<p>"There are many different strategies to predict the stock market. When selecting a strategy to predict the stock market, that strategy must be robust and be able to handle unexpected events. This paper analyzes algorithms that are based on human psychology instead of just looking for patterns in the data. It also attempts to find optimal parameters for the algorithms and see if their performance will persist in the future and with trading costs. Finally, this paper looks at algorithms that are able to combine the signals of other algorithms and see how well they perform with and without trading costs"--Leaf iv.</p>","abstract_has_math":false,"creators":["Klinger, Nicholas P."],"institution":null,"degree_name":"Master of Science (MS) in Computer Science","degree_level":"Thesis: EWU Only","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-01-01T08:00:00Z","date_published":"2016-01-01T08:00:00Z","updated_at":"2026-07-24T02:13:29Z","subjects":["Stocks--Prices--Mathematical models","Stock price forecasting","Stock exchanges","Computer algorithms","Computer Sciences","Finance and Financial Management","Theory and Algorithms"],"languages":[],"rights":["Access perpetually restricted to EWU users with an active EWU NetID"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://dc.ewu.edu/theses/400","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Klinger, Nicholas P."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis: EWU Only"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS) in Computer Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Stocks--Prices--Mathematical models","Stock price forecasting","Stock exchanges","Computer algorithms","Computer Sciences","Finance and Financial Management","Theory and Algorithms"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["Access perpetually restricted to EWU users with an active EWU NetID"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://dc.ewu.edu/theses/400"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["\"There are many different strategies to predict the stock market. When selecting a strategy to predict the stock market, that strategy must be robust and be able to handle unexpected events. This paper analyzes algorithms that are based on human psychology instead of just looking for patterns in the data. It also attempts to find optimal parameters for the algorithms and see if their performance will persist in the future and with trading costs. Finally, this paper looks at algorithms that are able to combine the signals of other algorithms and see how well they perform with and without trading costs\"--Leaf iv.
"]},{"key":"dc:title","label":"Title","values":["Analysis of algorithms to create profitable trades in the stock market"]}]}],"canonical_facts":{"dc:creator":["Klinger, Nicholas P."],"dc:description.abstract":["\"There are many different strategies to predict the stock market. When selecting a strategy to predict the stock market, that strategy must be robust and be able to handle unexpected events. This paper analyzes algorithms that are based on human psychology instead of just looking for patterns in the data. It also attempts to find optimal parameters for the algorithms and see if their performance will persist in the future and with trading costs. Finally, this paper looks at algorithms that are able to combine the signals of other algorithms and see how well they perform with and without trading costs\"--Leaf iv.
"],"dc:identifier":["https://dc.ewu.edu/theses/400"],"dc:rights":["Access perpetually restricted to EWU users with an active EWU NetID"],"dc:subject":["Stocks--Prices--Mathematical models","Stock price forecasting","Stock exchanges","Computer algorithms","Computer Sciences","Finance and Financial Management","Theory and Algorithms"],"dc:title":["Analysis of algorithms to create profitable trades in the stock market"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis: EWU Only"],"thesis:degree_name":["Master of Science (MS) in Computer Science"]},"updated_at":"2026-07-24T02:13:29Z"}