<?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>0186-1042</journal-id>
<journal-title><![CDATA[Contaduría y administración]]></journal-title>
<abbrev-journal-title><![CDATA[Contad. Adm]]></abbrev-journal-title>
<issn>0186-1042</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Facultad de Contaduría y Administración]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0186-10422019000300013</article-id>
<article-id pub-id-type="doi">10.22201/fca.24488410e.2018.1710</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[The Google trends effect on the behavior of the exchange rate Mexican peso - US dollar]]></article-title>
<article-title xml:lang="es"><![CDATA[Efecto de las tendencias de Google en el comportamiento del tipo de cambio peso mexicano- dólar estadounidense]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Durán Bustamante]]></surname>
<given-names><![CDATA[Mario]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hernández del Valle]]></surname>
<given-names><![CDATA[Adrián]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ortiz Ramírez]]></surname>
<given-names><![CDATA[Ambrosio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Politécnico Nacional  ]]></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>64</volume>
<numero>2</numero>
<fpage>0</fpage>
<lpage>0</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0186-10422019000300013&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0186-10422019000300013&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0186-10422019000300013&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract We show the advantage of using Google search engine trends to forecast the volatility of the short-term (weekly) exchange rate between the Mexican peso and United States dollar. We perform a comparison of models in the literature that have used Google Trends to examine explanatory variables. Some of the models are based on time series, whereas others are based on the similarity function, which captures the cognitive form of human reasoning. For example, an investor who needs to know the value that a variable will take in the future will take into account relevant, known, and available information, and weigh it to calculate the forecast. We conclude that taking into account the Google Trends variable helps explains partially the behaviour of volatility; and it is necessary to incorporate more aggregation levels. Moreover, to the best of our knowledge, literature on the subject of using Google Trends to explain relevant economic variables is relatively scarce.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen En este trabajo se muestra la ventaja de usar tendencias del motor de búsqueda de Google para pronosticar la volatilidad de corto plazo (semanal) del tipo de cambio peso mexicano - dólar estadounidense. Se realizó un comparativo de modelos que en la literatura ya han incorporado las tendencias de Google como variable explicativa. Algunos de estos modelos utilizan series de tiempo, mientras que otros utilizan la función de similitud, que captura la forma cognitiva del razonamiento humano. Por ejemplo, un inversor que desea conocer el valor que tomará una variable en el futuro tomará en consideración información relevante, conocida y disponible, y la ponderará para calcular su pronóstico. Se concluye que el tomar en consideración la variable de tendencia de Google no es suficiente para explicar el comportamiento volátil semanal del tipo de cambio y que es necesario incorporar más niveles de agregación. Además, de que de acuerdo con nuestro saber existe escasa literatura que utilice las tendencias de Google para explicar variables económicas relevantes.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[C01]]></kwd>
<kwd lng="en"><![CDATA[C13]]></kwd>
<kwd lng="en"><![CDATA[F31]]></kwd>
<kwd lng="en"><![CDATA[Google trends]]></kwd>
<kwd lng="en"><![CDATA[Empirical similarity]]></kwd>
<kwd lng="en"><![CDATA[Aggregation levels]]></kwd>
<kwd lng="en"><![CDATA[Volatility, Exchange rate]]></kwd>
<kwd lng="es"><![CDATA[C01]]></kwd>
<kwd lng="es"><![CDATA[C13]]></kwd>
<kwd lng="es"><![CDATA[F31]]></kwd>
<kwd lng="es"><![CDATA[Tendencias de Google]]></kwd>
<kwd lng="es"><![CDATA[Similitud empírica]]></kwd>
<kwd lng="es"><![CDATA[Niveles de agregación]]></kwd>
<kwd lng="es"><![CDATA[Volatilidad]]></kwd>
<kwd lng="es"><![CDATA[Tipo de cambio]]></kwd>
</kwd-group>
</article-meta>
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