<?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>2448-718X</journal-id>
<journal-title><![CDATA[El trimestre económico]]></journal-title>
<abbrev-journal-title><![CDATA[El trimestre econ]]></abbrev-journal-title>
<issn>2448-718X</issn>
<publisher>
<publisher-name><![CDATA[Fondo de Cultura Económica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2448-718X2005000400765</article-id>
<article-id pub-id-type="doi">10.20430/ete.v72i288.561</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Regularidades no lineales en índices accionarios. Una aproximación con redes neuronales]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Johnson]]></surname>
<given-names><![CDATA[Christian A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Padilla]]></surname>
<given-names><![CDATA[Miguel A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Adolfo Ibáñez Escuela de Negocios ]]></institution>
<addr-line><![CDATA[Santiago ]]></addr-line>
<country>Chile</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Escuela Superior Politécnica del Litoral Instituto de Ciencias Humanísticas y Económicas ]]></institution>
<addr-line><![CDATA[Guayaquil ]]></addr-line>
<country>Ecuador</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2005</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2005</year>
</pub-date>
<volume>72</volume>
<numero>288</numero>
<fpage>765</fpage>
<lpage>821</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2448-718X2005000400765&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2448-718X2005000400765&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2448-718X2005000400765&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Las redes neuronales artificiales (RNA) se han convertido en un importante instrumento para modelar y predecir los rendimientos accionarios. Debido a que son modelos que incorporan variables no lineales (característica de la mayoría de las series económicas y financieras) funcionan mejor que los modelos estadísticos tradicionales, como las regresiones lineales o modelos Box-Jenkins. Este estudio intenta encontrar regularidades en los índices accionarios de 27 países mediante un acercamiento de redes neuronales artificiales y su contraste con modelos lineales rezagados, y aporta evidencia a la discusión actual respecto a la teoría de los mercados eficientes. Asimismo se realizan predicciones extramuestrales dinámicas sustentadas también con una prueba no paramétrica, que confirma excelentes resultados de las redes neuronales en contraste con los modelos autorregresivos tradicionales.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The artificial neural networks (ANN) have turned into an important tool to shape and to predict the stock returns. Due to the fact that those models incorporate nonlinear variables (characteristic of the majority of the economic and financial series) they work better than the statistical traditional models such as linear regressions or Box-Jenkins' model. This study brings the attempt of finding regularities in the stock indexes of 27 countries by means an approximation of artificial neural networks and their contrast with linear regressive models finding evidence that reaches to the current discussion on the "Efficient Market Theory". Likewise dynamics out of sample predictions are realized sustained also by a nonparametric test confirming excellent results of the neural networks in contrast with the traditional autoregressive models.]]></p></abstract>
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<kwd lng="es"><![CDATA[redes neuronales artificiales]]></kwd>
<kwd lng="es"><![CDATA[metodología y aplicaciones]]></kwd>
<kwd lng="es"><![CDATA[mercados accionarios]]></kwd>
</kwd-group>
</article-meta>
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