{"id":{"repo_id":"njit","oai_identifier":"oai:digitalcommons.njit.edu:theses-1282"},"canonical_url":"https://search.dev.ndltd.org/etd/njit/oai:digitalcommons.njit.edu:theses-1282","repository":{"repo_id":"njit","name":"NJIT","base_url":"https://digitalcommons.njit.edu/do/oai/"},"display":{"title":"Uusing the KDJ as a trading strategy on biotech companies","abstract":"Mean Reversion is the most commonly used model in quantitative trading. This model is associated with several factors, like ma5 and ma10 line. These factors are the most significant in stock markets. However, the disadvantages of this model are lag and inaccuracy. In this research, we get the historical and current stock data by web crawler, analyze the quantitative data and build a new model involved with the KDJ. Taking biotech companies marketed in the United States and B-share marketed in China as the research subjects, the result shows increased profits compared with the Mean Reversion model. It also shows that as long as we clearly understand the relationship between the turnover and fluctuation of share price, we can find the trading signals more accurately and generate more profit.","abstract_html":"Mean Reversion is the most commonly used model in quantitative trading. This model is associated with several factors, like ma5 and ma10 line. These factors are the most significant in stock markets. However, the disadvantages of this model are lag and inaccuracy. In this research, we get the historical and current stock data by web crawler, analyze the quantitative data and build a new model involved with the KDJ. Taking biotech companies marketed in the United States and B-share marketed in China as the research subjects, the result shows increased profits compared with the Mean Reversion model. It also shows that as long as we clearly understand the relationship between the turnover and fluctuation of share price, we can find the trading signals more accurately and generate more profit.","abstract_has_math":false,"creators":["Zha, Shijie"],"institution":null,"degree_name":"Master of Science in Bioinformatics - (M.S.)","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Zhi Wei","Usman W. Roshan","Jason T. L. Wang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-05-31T07:00:00Z","date_published":"2016-05-31T07:00:00Z","updated_at":"2026-07-24T03:22:34Z","subjects":["Trading strategies","Quantitative trading","Biotech companies","Bioinformatics","Computer Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.njit.edu/theses/283","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zhi Wei","Usman W. Roshan","Jason T. L. 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This model is associated with several factors, like ma5 and ma10 line. These factors are the most significant in stock markets. However, the disadvantages of this model are lag and inaccuracy. In this research, we get the historical and current stock data by web crawler, analyze the quantitative data and build a new model involved with the KDJ. Taking biotech companies marketed in the United States and B-share marketed in China as the research subjects, the result shows increased profits compared with the Mean Reversion model. It also shows that as long as we clearly understand the relationship between the turnover and fluctuation of share price, we can find the trading signals more accurately and generate more profit."]},{"key":"dc:title","label":"Title","values":["Uusing the KDJ as a trading strategy on biotech companies"]}]}],"canonical_facts":{"dc:contributor":["Zhi Wei","Usman W. Roshan","Jason T. L. Wang"],"dc:creator":["Zha, Shijie"],"dc:description.abstract":["Mean Reversion is the most commonly used model in quantitative trading. This model is associated with several factors, like ma5 and ma10 line. These factors are the most significant in stock markets. However, the disadvantages of this model are lag and inaccuracy. In this research, we get the historical and current stock data by web crawler, analyze the quantitative data and build a new model involved with the KDJ. Taking biotech companies marketed in the United States and B-share marketed in China as the research subjects, the result shows increased profits compared with the Mean Reversion model. It also shows that as long as we clearly understand the relationship between the turnover and fluctuation of share price, we can find the trading signals more accurately and generate more profit."],"dc:identifier":["https://digitalcommons.njit.edu/theses/283"],"dc:subject":["Trading strategies","Quantitative trading","Biotech companies","Bioinformatics","Computer Sciences"],"dc:title":["Uusing the KDJ as a trading strategy on biotech companies"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["Master of Science in Bioinformatics - (M.S.)"]},"updated_at":"2026-07-24T03:22:34Z"}