<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1665-5346</journal-id>
<journal-title><![CDATA[Revista mexicana de economía y finanzas]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. mex. econ. finanz]]></abbrev-journal-title>
<issn>1665-5346</issn>
<publisher>
<publisher-name><![CDATA[Instituto Mexicano de Ejecutivos de Finanzas A.C.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1665-53462021000100009</article-id>
<article-id pub-id-type="doi">10.21919/remef.v16i1.452</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Estadística multivariada aplicada a la clasificación de empresas que cotizan en la Bolsa Mexicana de Valores]]></article-title>
<article-title xml:lang="en"><![CDATA[Multivariate statistics applied to the classification of companies listed on the Mexican Stock Exchange]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rosa Flores]]></surname>
<given-names><![CDATA[Carlos Cristian De la]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ordóñez Parada]]></surname>
<given-names><![CDATA[Ana Isabel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cabrera Ramos]]></surname>
<given-names><![CDATA[Cristina]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Berroterán Martínez]]></surname>
<given-names><![CDATA[Viviana]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma de Chihuahua  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2021</year>
</pub-date>
<volume>16</volume>
<numero>1</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1665-53462021000100009&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1665-53462021000100009&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1665-53462021000100009&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El objetivo fue evidenciar la eficacia de la estadística multivariada para compactar, analizar y clasificar información obtenida de indicadores de desempeño financiero. Se aplicó un análisis de componentes principales (ACP), justificado por la prueba de medida Kaiser-Meyer-Olkin (KMO) y la prueba de esfericidad de Barlett, a 14 razones financieras de cada una de las 21 empresas seleccionadas por medio de un muestreo probabilístico, que cotizaron en la Bolsa Mexicana de Valores durante el año 2017, para finalmente aplicar un análisis cluster jerárquico y otro no jerárquico. En los resultados se obtuvieron 3 componentes principales capaces de resumir la variabilidad total en un 76% con lo que se logró hacer una clasificación de menor a mayor nivel de liquidez, rentabilidad y actividad, además de formar clusters de empresas en relación a la semejanza de su desempeño financiero. Sugerimos replicar esta investigación en empresas que cotizan en otros mercados, tales como el NYSE o el NASDAQ. Se concluye que la estadística multivariada es capaz de generar información financiera más compacta, optimizando la toma de decisiones por parte de inversionistas.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The objective was to demonstrate the effectiveness of multivariate statistics to compact, analyze and classify information obtained from financial performance indicators. A principal component analysis (PCA), justified by the Kaiser-Meyer-Olkin (KMO) measurement test and the Barlett sphericity test, was applied to 14 financial reasons of each of the 21 companies selected by sampling probabilistic, which were listed on the Mexican Stock Exchange during 2017, to finally apply a hierarchical and a non-hierarchical cluster analysis. In the results, 3 main components were obtained, capable of summarizing the total variability in 76%, which allowed a classification of lower to higher level of liquidity, profitability and activity, in addition to forming clusters of companies in relation to the similarity of Your financial performance We suggest replicating this research in companies that are listed in other markets, such as the NYSE or the NASDAQ. It is concluded that multivariate statistics is capable of generating more compact financial information, optimizing decision making by investors.]]></p></abstract>
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<kwd lng="es"><![CDATA[Razones financieras]]></kwd>
<kwd lng="es"><![CDATA[estadística multivariada]]></kwd>
<kwd lng="es"><![CDATA[análisis de componentes principales]]></kwd>
<kwd lng="es"><![CDATA[análisis cluster]]></kwd>
<kwd lng="en"><![CDATA[Financial reasons]]></kwd>
<kwd lng="en"><![CDATA[multivariate statistics]]></kwd>
<kwd lng="en"><![CDATA[principal component analysis]]></kwd>
<kwd lng="en"><![CDATA[cluster analysis]]></kwd>
</kwd-group>
</article-meta>
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