<?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>2683-2690</journal-id>
<journal-title><![CDATA[The Anáhuac journal]]></journal-title>
<abbrev-journal-title><![CDATA[The Anáhuac j.]]></abbrev-journal-title>
<issn>2683-2690</issn>
<publisher>
<publisher-name><![CDATA[Universidad Anáhuac del Sur S.C., Facultad de Economía y Negocios]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2683-26902019000100013</article-id>
<article-id pub-id-type="doi">10.36105/theanahuacjour.2019v19n1.01</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Análisis y proyección de los rendimientos accionarios de Pfizer, en el período 2018-2020, mediante redes neuronales diferenciales]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Aja Kindelan]]></surname>
<given-names><![CDATA[Alfonso]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Mata Mata]]></surname>
<given-names><![CDATA[Leovardo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Beltrán Godoy]]></surname>
<given-names><![CDATA[Jaime Humberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,aff1  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Anáhuac México  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Anáhuac México  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2019</year>
</pub-date>
<volume>19</volume>
<numero>1</numero>
<fpage>13</fpage>
<lpage>34</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2683-26902019000100013&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2683-26902019000100013&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2683-26902019000100013&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen En este trabajo se utiliza una red neuronal diferencial (DNN, por sus siglas en inglés) para proyectar los rendimientos accionarios de Pfizer en el período 2018-2020. El modelo emplea datos trimestrales, al cierre del período, del precio de la acción de la empresa (P), ventas netas (VN), activos totales (AT) y cuentas por cobrar (CC). Los resultados señalan una bondad de ajuste superior de las DNN frente a los métodos convencionales, pues el error en el pronóstico out sample es inferior al 5 %. Este hallazgo contribuye con evidencia empírica para afirmar que las DNN ofrecen mayor robustez predictiva de los rendimientos accionarios de Pfizer.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract In this paper, a differential neural network (DNN) is used to project Pfizer&#8217;s stock returns in the 2018-2020 period. The model uses quarterly data, at the end of the period, the price of the company&#8217;s stock (P), net sales (NS), total assets (TA) and accounts receivable (AR). The results are compared with the classic regression models and there is evidence of the superior goodness of fit of the DNN, compared to conventional methods, since the error in out sample forecast is less than 5 %.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[redes neuronales]]></kwd>
<kwd lng="es"><![CDATA[pronóstico]]></kwd>
<kwd lng="es"><![CDATA[rendimientos accionarios]]></kwd>
<kwd lng="en"><![CDATA[neural networks]]></kwd>
<kwd lng="en"><![CDATA[forecast]]></kwd>
<kwd lng="en"><![CDATA[stock returns]]></kwd>
<kwd lng="es"><![CDATA[C45]]></kwd>
<kwd lng="es"><![CDATA[C51]]></kwd>
<kwd lng="en"><![CDATA[C45]]></kwd>
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