{"id":{"repo_id":"brock","oai_identifier":"oai:brocku.scholaris.ca:10464/9792"},"canonical_url":"https://search.dev.ndltd.org/etd/brock/oai:brocku.scholaris.ca:10464/9792","repository":{"repo_id":"brock","name":"Brock University","base_url":"https://brocku.scholaris.ca/server/oai/request"},"display":{"title":"The Application of the Genetic Algorithm in Promoting Stock Trading Performances","abstract":"This thesis joins the debate on utilizing the Genetic Algorithm (GA) to discover profitable trading strategies by providing an out-of-sample test of GA-based trading strategies on the CSI 300 index. Our results suggest that, with trading costs taken into consideration, GA-based trading rules consistently beat the buy-and-hold strategy in daily trading of CSI 300 index. Besides, we open up the black box of the evolution process of the GA by testing the statistical significance of the GA-based profitable trading strategies through the Fama-MacBeth regressions. In addition, this study connects the literature on the regime switching with studies on the GA-based trading strategies to construct one regime-switching Genetic Algorithm (RSGA) model and makes a comparison between the GA-based and the RSGA-based trading strategies. The empirical results show that trading strategies generated from the RSGA model consistently outperform those obtained from the GA model.","abstract_html":"This thesis joins the debate on utilizing the Genetic Algorithm (GA) to discover profitable trading strategies by providing an out-of-sample test of GA-based trading strategies on the CSI 300 index. Our results suggest that, with trading costs taken into consideration, GA-based trading rules consistently beat the buy-and-hold strategy in daily trading of CSI 300 index. Besides, we open up the black box of the evolution process of the GA by testing the statistical significance of the GA-based profitable trading strategies through the Fama-MacBeth regressions. In addition, this study connects the literature on the regime switching with studies on the GA-based trading strategies to construct one regime-switching Genetic Algorithm (RSGA) model and makes a comparison between the GA-based and the RSGA-based trading strategies. The empirical results show that trading strategies generated from the RSGA model consistently outperform those obtained from the GA model.","abstract_has_math":false,"creators":["Wang, Yanqi"],"institution":"Brock University","degree_name":"M.Sc. Management","degree_level":"Masters","degree_discipline":"Faculty of Business","degree_department":"Faculty of Business Programs","school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-08-16T20:20:20Z","date_published":"2016-08-16T20:20:20Z","updated_at":"2026-07-24T01:23:12Z","subjects":["GA","trading strategy","black box","regime-switching GA","outperformance"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10464/9792","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.department","label":"Department","values":["Faculty of Business Programs"]},{"key":"dc:creator","label":"Author","values":["Wang, Yanqi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2016-08-16T20:20:20Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2016-08-16T20:20:20Z"]},{"key":"dc:date.issued","label":"Date","values":["2016-08-16T20:20:20Z"]},{"key":"dc:type","label":"Dc Type","values":["Electronic Thesis or Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Faculty of Business"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.Sc. 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Our results suggest that, with trading costs taken into consideration, GA-based trading rules consistently beat the buy-and-hold strategy in daily trading of CSI 300 index. Besides, we open up the black box of the evolution process of the GA by testing the statistical significance of the GA-based profitable trading strategies through the Fama-MacBeth regressions. In addition, this study connects the literature on the regime switching with studies on the GA-based trading strategies to construct one regime-switching Genetic Algorithm (RSGA) model and makes a comparison between the GA-based and the RSGA-based trading strategies. The empirical results show that trading strategies generated from the RSGA model consistently outperform those obtained from the GA model."]},{"key":"dc:title","label":"Title","values":["The Application of the Genetic Algorithm in Promoting Stock Trading Performances"]}]}],"canonical_facts":{"dc:contributor.department":["Faculty of Business Programs"],"dc:creator":["Wang, Yanqi"],"dc:date.accessioned":["2016-08-16T20:20:20Z"],"dc:date.available":["2016-08-16T20:20:20Z"],"dc:date.issued":["2016-08-16T20:20:20Z"],"dc:description.abstract":["This thesis joins the debate on utilizing the Genetic Algorithm (GA) to discover profitable trading strategies by providing an out-of-sample test of GA-based trading strategies on the CSI 300 index. Our results suggest that, with trading costs taken into consideration, GA-based trading rules consistently beat the buy-and-hold strategy in daily trading of CSI 300 index. Besides, we open up the black box of the evolution process of the GA by testing the statistical significance of the GA-based profitable trading strategies through the Fama-MacBeth regressions. In addition, this study connects the literature on the regime switching with studies on the GA-based trading strategies to construct one regime-switching Genetic Algorithm (RSGA) model and makes a comparison between the GA-based and the RSGA-based trading strategies. The empirical results show that trading strategies generated from the RSGA model consistently outperform those obtained from the GA model."],"dc:identifier.uri":["http://hdl.handle.net/10464/9792"],"dc:language.iso":["eng"],"dc:subject":["GA","trading strategy","black box","regime-switching GA","outperformance"],"dc:title":["The Application of the Genetic Algorithm in Promoting Stock Trading Performances"],"dc:type":["Electronic Thesis or Dissertation"],"thesis:degree_discipline":["Faculty of Business"],"thesis:degree_level":["Masters"],"thesis:degree_name":["M.Sc. Management"],"thesis:institution_name":["Brock University"]},"updated_at":"2026-07-24T01:23:12Z"}