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dc.contributor.advisorAlhajj, Reda
dc.contributor.authorAghakhani, Sara
dc.date.accessioned2017-05-01T17:47:58Z
dc.date.available2017-05-01T17:47:58Z
dc.date.issued2017
dc.date.submitted2017en
dc.identifier.citationAghakhani, S. (2017). Effective Data Analysis Framework for Financial Variable Selection and Missing Data Discovery (Unpublished doctoral thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/25787en_US
dc.identifier.urihttp://hdl.handle.net/11023/3790
dc.description.abstractQuantitative evaluation of financial variables plays a foundational role in financial price modeling, economic prediction, risk evaluation, portfolio management, etc. However, the problem suffers from high dimensionality. Thus, financial variables should be selected in a way to reduce the dimensionality of the financial model and make the model more efficient. In addition, it is quite common for financial datasets to contain missing data due to a variety of limitations. Consequently, in practical situations, it is difficult to choose the best subset of financial variables due to the existence of missing values. The two problems are interrelated. Therefore, the central idea in this research is to develop and examine new techniques for financial variable selection based on estimating the missing values, while accounting for all the longitudinal and latitudinal information. This research proposes a novel methodology to minimize the problem associated with missing data and find the best subset of financial variables that could be used for effective analysis. There are two major steps; the first step concentrates on estimating missing data using Bayesian updating and Kriging algorithms. The second step is to find the best subset of financial variables. In this step a novel feature subset selection is proposed (LmRMR) which ranks the financial variables and the best subset of variables is chosen by employing statistical techniques through Super Secondary Target Correlation (SSTC) measurement. Some tests have been done to demonstrate the applicability and effectiveness of the ideas presented in this research. In particular, the potential application of the proposed methods in stock market trading model and stock price forecasting are studied. The experimental studies are conducted on Dow Jones Industrial Average financial variables.en_US
dc.language.isoeng
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectEconomics--Finance
dc.subjectComputer Science
dc.subject.otherMissing Data Analysis
dc.subject.otherFinancial Variable Selection
dc.subject.otherBayesian Updating
dc.subject.otherSuper Secondary Data
dc.subject.otherKriging
dc.subject.otherStock Price Prediction
dc.subject.otherAPT
dc.subject.otherStock Market Trading Model
dc.subject.othermRMR
dc.subject.otherLikelihood
dc.subject.otherDow Jones Industrial Average
dc.subject.otherFIM
dc.subject.otherNMAR
dc.subject.otherLikelihood Minimum Redundancy Maximum Relevance
dc.subject.otherLmRMR
dc.subject.otherFinincial Factors
dc.titleEffective Data Analysis Framework for Financial Variable Selection and Missing Data Discovery
dc.typedoctoral thesis
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgaryen
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/25787
thesis.degree.nameDoctor of Philosophy
thesis.degree.namePhD
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Calgary
atmire.migration.oldid5505
dc.contributor.committeememberRokne, Jon
dc.contributor.committeememberChang, Philip
dc.contributor.committeememberKhoshgoftaar, Taghi
dc.contributor.committeememberMoshirpour, Mohammad
dc.publisher.placeCalgaryen


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