PRINCIPAL COMPONENTS AND CANONICAL CORRELATION ANALYSES AS COMPLEMENTARY TOOLS. APPLICATION TO THE PROCESSING OF FINANCIAL INFORMATION

Ana García-Gallego () and María-Jesús Mures-Quintana ()
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María-Jesús Mures-Quintana: Universidad de León, Spain

ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, 2016, vol. 50, issue 4, 249-266

Abstract: The processing of huge amounts of data (big data) whose generation has been fostered by advances in the information and communications technologies involves a high cost of timing and resources, which can be simplified by the application of data reduction statistical methods, such as Principal Components Analysis (PCA). In this paper a PCA is applied in order to prove its usefulness in reducing financial information expressed as ratios. The achieved results, in terms of variable selection, are next justified by the application of a Canonical Correlation Analysis (CCA). The use of both methods shows they are complementary, since the ratios correlated to the extracted factors in PCA are also important in defining the canonical variates in CCA, showing the relationship between them.

Keywords: principal components analysis; canonical correlation analysis; financial information; ratios; big data. (search for similar items in EconPapers)
JEL-codes: O30 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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