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Description / Abstract
Submitted to the Department of Mathematics and Computer Science in partial fulfillment of the requirements for the degree of Bachelor of Arts Computer Science
Predictive analysis within the stock market has been a goal of many different banks and large organizations as well as individual traders, as there are substantial monetary gains to be had. The objective of this project is to ask the question: Can one use patterns developed within the stock market to predict the behavior and achieve positive financial margins? When beginning this research endeavor, learning the current patterns for trading algorithms is necessary. Traders often use technical analysis to predict future stocks moves. With this information and a sample database of around nineteen years of stock data, this hypothesis was tested on the Golden and Death Cross. Using specialized SQL queries these patterns were investigated through a series of tables and extensively explored to provide relevant data needed to achieve a prediction method for the trading algorithm. The results demonstrated that the opposite hypothesis, buying on the Death Cross and selling on the Golden Cross, occurred when employing these patterns implemented by this approach.
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This material is used by permission of the rightsholder in accordance with the terms of the relevant content release. As a user, you have permission to use this content and download a copy for non-commercial use only. Downloading multiple quantities of this resource is expressly forbidden.
KeywordsStock market, SQL, Data analytics
Degree AwardedBachelor of Arts
Awarding InstitutionNorthwest Nazarene University
Committee MembersAudra Butkus, Barry Myers
Supervisor or ChairKevin McCarthy
Peer Reviewed?No
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NNU Computer Science Theses
74 Resources
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- Physical Sciences and Mathematics
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