<?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>S1665-53462021000400008</article-id>
<article-id pub-id-type="doi">10.21919/remef.v16i4.505</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Autómata Evolutivo (AE) para el mercado accionario usando martingalas y un algoritmo genético]]></article-title>
<article-title xml:lang="en"><![CDATA[Evolutionary Automaton (AE) for the Stock Market Using Martingales and a Genetic Algorithm]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gómez Vilchis]]></surname>
<given-names><![CDATA[Jaime Alberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hernández Álvarez]]></surname>
<given-names><![CDATA[Federico]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Román de la Sancha]]></surname>
<given-names><![CDATA[Luis Ignacio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional Autónoma de México  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2021</year>
</pub-date>
<volume>16</volume>
<numero>4</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1665-53462021000400008&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1665-53462021000400008&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1665-53462021000400008&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El objetivo de este trabajo es desarrollar un Autómata Evolutivo (AE) que opera con base a un modelo de martingalas con el que se definen estrategias de inversión, las cuales utilizan información inmediata histórica, límites de ganancia, pérdida y tiempos de permanencia; brinda señales de compra, venta o mantener la posición del activo basadas en la combinación óptima de medias móviles seleccionadas mediante un algoritmo genético. Se probó para dos índices accionarios antes, durante y después de la crisis subprime, mostrando que, cuando los mercados estaban en fase alcista, la estrategia comprar-mantener (BH) generó un rendimiento superior al AE; en contraste, para el periodo de crisis observado, el AE logró un rendimiento mayor; finalmente, en todo el periodo de prueba, el rendimiento del AE fue superior. El AE tiene la restricción que solo puede ser usado por una acción/índice por periodo, aunque esto puede ser solventado al ciclarlo por el número de instrumentos del portafolio. La autenticidad del trabajo radica en la combinación de modelos que se complementan para generar un sistema que ayuda en la toma de decisiones.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The aim of this paper is to develop an Evolutionary Automata (EA) that operates considering a martingale model, which defines investment strategies using immediate historical information, profit or lost limits and stopping time. The EA offers signals to buy, sell or hold a determinate stock, these signals are based on the optimal combination of simple moving average through a genetic algorithm. The EA was tested with two stock indices, before, during and after the subprime crisis. Finding that when the stock markets were in bull market, the Buy-Hold (BH) strategy presented superior performance than the EA; in contrast, during the crisis period, the performance of the EA was better than BH, finally, for all of the test period, the performance of the EA was superior. The EA have the restriction that can be used by one stock/index for period, though this can be solved by cycling the number of instruments of the portfolio. The authenticity of this research is the combination of the above models that match between them in order to generate a system that helps investor decisions.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Autómata evolutivo]]></kwd>
<kwd lng="es"><![CDATA[martingalas]]></kwd>
<kwd lng="es"><![CDATA[tiempos de paro]]></kwd>
<kwd lng="es"><![CDATA[medias móviles]]></kwd>
<kwd lng="es"><![CDATA[algoritmo genético]]></kwd>
<kwd lng="en"><![CDATA[Evolutionary Automata]]></kwd>
<kwd lng="en"><![CDATA[martingales]]></kwd>
<kwd lng="en"><![CDATA[stopping time]]></kwd>
<kwd lng="en"><![CDATA[moving average]]></kwd>
<kwd lng="en"><![CDATA[genetic algorithm]]></kwd>
</kwd-group>
</article-meta>
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