Stock trend prediction using regression analysis–a data mining approach

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dc.contributor.authorAbdulsalam, S. O.
dc.contributor.authorAdewole, K. S.
dc.contributor.authorJimoh, R. G.
dc.date.accessioned2017-11-23T16:39:35Z
dc.date.available2017-11-23T16:39:35Z
dc.date.issued2011
dc.description.abstractOrganizations have been collecting data for decades, building massive data warehouses in which to store the data. Even though this data is available, very few of these organizations have been able to realize the actual value stored in it. The question these organizations are asking is how to extract meaningful data and uncover patterns and relationship from their databases. This paper presents a study of regression analysis for use in stock price prediction. Data were obtained from the daily official list of the prices of all shares traded on the stock exchange published by the Nigerian Stock Exchange using banking sector of Nigerian economy with three banks namely:- First Bank of Nigeria Plc, Zenith Bank Plc, and Skye Bank Plc to build a database. A data mining software tool was used to uncover patterns and relationships and also to extract values of variables from the database to predict the future values of other variables through the use of time series data that employed moving average method. The tools were found capable technique to describe the trends of stock market prices and predict the future stock market prices of three banks sampled.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/48
dc.language.isoenen_US
dc.publisherARPN Journal of Systems and Softwareen_US
dc.subjectData warehouses; regression analysis; stock price; data mining; moving averageen_US
dc.titleStock trend prediction using regression analysis–a data mining approachen_US
dc.typeArticleen_US

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