<?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>S1665-53462018000400603</article-id>
<article-id pub-id-type="doi">10.21919/remef.v13i4.342</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Predicción del IPC mexicano combinando modelos econométricos e inteligencia artificial]]></article-title>
<article-title xml:lang="en"><![CDATA[Mexican IPC Prediction Combining Econometric Models and Artificial Intelligence]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[León Anaya]]></surname>
<given-names><![CDATA[Luis Manuel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Landassuri Moreno]]></surname>
<given-names><![CDATA[Víctor Manuel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Orozco Aguirre]]></surname>
<given-names><![CDATA[Héctor Rafael]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Quintana López]]></surname>
<given-names><![CDATA[Maricela]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma del Estado de México  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2018</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2018</year>
</pub-date>
<volume>13</volume>
<numero>4</numero>
<fpage>603</fpage>
<lpage>629</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1665-53462018000400603&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1665-53462018000400603&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1665-53462018000400603&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El objetivo de este trabajo es descomponer los factores de comportamiento del Índice de Precios y Cotizaciones (IPC) mexicano para ser pronosticado mediante modelos econométricos y redes neuronales artificiales evolutivas. La metodología empleada consiste en reducir la complejidad de análisis y eliminar el ruido en los datos del IPC mediante la descomposición empírica en modos (DEM), combinando las funciones de modo intrínseco (FMIs) resultantes con las variantes de los modelos autorregresivo integrado de promedio móvil (ARIMA) y autorregresivo con heterocedasticidad condicional (ARCH), y el algoritmo de selección de características de programación evolutiva de redes (FS-EPNet) para pronosticar su comportamiento. La configuración experimental y resultados se presentan y analizan mediante tres fases de predicción del IPC. Las limitaciones son que el IPC mexicano no es estacionario, implicando que algunas FMIs tampoco lo sean. La originalidad consiste en la combinación de la DEM con el algoritmo FS-EPNet para analizar la evolución del mercado bursátil mexicano a través de su IPC, con lo cual se demuestra y concluye que genera una mejor predicción que la obtenida a partir de los datos originales.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The purpose of this paper is to decompose the behavioral factors of the Mexican Price and Quotation Index (IPC for its acronym in Spanish) to be forecast using econometric models and evolutionary artificial neural networks. The methodology used consists on reducing the analysis complexity and eliminating the noise in the IPC data through empirical mode decomposition (EMD), combining the intrinsic mode functions (IMFs) resulting with the variants of the autoregressive integrated mobile average (ARIMA) and autoregressive conditional heteroskedasticity (ARCH) models, as well as the algorithm for selection of characteristics of evolutionary network programing (FS-EPNet) to forecast its behavior. The experimental configuration and results are shown and are analyzed using three prediction phases of the IPC. The limitations are that the Mexican IPC is not stationary, which implies that some IMFs are also not stationary. The originality of this consists on the combination of DEM with the FS-EPNet algorithm to analyze the evolution of the Mexican Stock Exchange through its IPC, which is used to show and conclude that it generates a better prediction than that obtained from the original data.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[C22]]></kwd>
<kwd lng="es"><![CDATA[C45]]></kwd>
<kwd lng="es"><![CDATA[C53]]></kwd>
<kwd lng="es"><![CDATA[Pronóstico]]></kwd>
<kwd lng="es"><![CDATA[Índice Bursátil]]></kwd>
<kwd lng="es"><![CDATA[Series de Tiempo]]></kwd>
<kwd lng="es"><![CDATA[Descomposición Empírica en Modos]]></kwd>
<kwd lng="es"><![CDATA[Redes Neuronales Artificiales Evolutivas]]></kwd>
<kwd lng="en"><![CDATA[C22]]></kwd>
<kwd lng="en"><![CDATA[C45]]></kwd>
<kwd lng="en"><![CDATA[C53]]></kwd>
<kwd lng="en"><![CDATA[Forecast]]></kwd>
<kwd lng="en"><![CDATA[Stock Index]]></kwd>
<kwd lng="en"><![CDATA[Time Series]]></kwd>
<kwd lng="en"><![CDATA[Empirical Mode Decomposition]]></kwd>
<kwd lng="en"><![CDATA[Evolutio-nary Artificial Neural Networks]]></kwd>
</kwd-group>
</article-meta>
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