<?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>2409-8752</journal-id>
<journal-title><![CDATA[Revista Científica de la UCSA]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. ciente. UCSA]]></abbrev-journal-title>
<issn>2409-8752</issn>
<publisher>
<publisher-name><![CDATA[Universidad del Cono Sur de las Américas]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2409-87522019000100049</article-id>
<article-id pub-id-type="doi">10.18004/ucsa/2409-8752/2019.006(01)049-064</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Redes neuronales para predecir el comportamiento del conjunto de activos financieros más líquidos del mercado de valores peruano]]></article-title>
<article-title xml:lang="en"><![CDATA[Neural networks to predict the behavior of the most liquid financial asset set of the peruvian securities market]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bellido]]></surname>
<given-names><![CDATA[B]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Schwarz]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Lima  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Peru</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2019</year>
</pub-date>
<volume>6</volume>
<numero>1</numero>
<fpage>49</fpage>
<lpage>64</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.iics.una.py/scielo.php?script=sci_arttext&amp;pid=S2409-87522019000100049&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.iics.una.py/scielo.php?script=sci_abstract&amp;pid=S2409-87522019000100049&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.iics.una.py/scielo.php?script=sci_pdf&amp;pid=S2409-87522019000100049&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN La presente investigación tiene como propósito identificar una herramienta de inteligencia artificial basada en redes neuronales para predecir el comportamiento de rendimiento y riesgo del conjunto de activos financieros basados en acciones que reflejen con mayor exactitud el movimiento bursátil del mercado de valores peruano. La investigación identificó inicialmente el activo financiero más apropiado para estimar los valores de rendimiento y riesgo de la cartera de acciones 50% más liquida del mercado peruano en el período 2010-2016. A partir del activo seleccionado se utilizó la técnica de redes neuronales artificiales con un perceptrón multicapa con regresión configurado con 3 capas (21,85,2) usando una función de activación logística con un optimizador LBFGS a una taza de aprendizaje de 0.01 para establecer los patrones financieros, operacionales, comerciales o de gobierno corporativo que puedan explicar y/o predecir el comportamiento del mismo en el mercado. La investigación concluye que la capacidad de generación de caja y la velocidad con la que se rotan los activos, así como la velocidad con la que se desembolsa el Capex constituyen los principales factores que influencian en la determinación de las mejores combinaciones de rendimiento y riesgo para el grupo de activos financieros considerados como materia de estudio, independiente del sector de mercado en el cual se opera. La investigación encontró una red neuronal capaz de aproximar la predicción de rendimiento y riesgo con un 76.93% de eficacia para el conjunto de activos seleccionados en el periodo de estudio. La investigación aporta un reconocimiento de patrones diferenciados en aspectos financieros, operacionales, comerciales y de gobierno corporativo con un especial énfasis en la capacidad gerencial que los genera cuya influencia se refleja en el desempeño del conjunto de activos estudiados por medio de la técnica de redes neuronales generando una herramienta predictiva para estimar su comportamiento bursátil.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT The purpose of this research is to identify an artificial intelligence tool based on neural networks to predict the behavior of performance and risk of the set of financial assets based on actions that more accurately reflect the stock market movement of the Peruvian stock market. The research initially identified the most appropriate financial asset to estimate the performance and risk values &#8203;&#8203;of the 50% most liquid share portfolio in the Peruvian market in the 2010-2016 period. From the selected asset, the technique of artificial neural networks with a multilayer perceptron with regression configured with 3 layers (21,85,2) was used, using a logistic activation function with an LBFGS optimizer at a learning rate of 0.01 to establish the financial, operational, commercial or corporate governance patterns that can explain and / or predict the behavior of the same in the market. The research concludes that the cash generation capacity and the speed with which the assets are rotated, as well as the speed with which the Capex is disbursed, constitute the main factors that influence the determination of the best combinations of performance and risk for the group of financial assets considered as a subject of study, independent of the market sector in which it operates. The research found a neural network able to approximate the prediction of performance and risk with a 76.93% efficiency for the set of assets selected in the study period. The research provides a recognition of differentiated patterns in financial, operational, commercial and corporate governance aspects with a special emphasis on the managerial capacity that generates them whose influence is reflected in the performance of the set of assets studied through the technique of neural networks generating a predictive tool to estimate its stock market behavior.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Activo financiero]]></kwd>
<kwd lng="es"><![CDATA[Fondo cotizado]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales]]></kwd>
<kwd lng="es"><![CDATA[rendimiento bursátil]]></kwd>
<kwd lng="es"><![CDATA[riesgo bursátil]]></kwd>
<kwd lng="en"><![CDATA[Financial assets]]></kwd>
<kwd lng="en"><![CDATA[exchange traded fund]]></kwd>
<kwd lng="en"><![CDATA[neural networks]]></kwd>
<kwd lng="en"><![CDATA[stock performance]]></kwd>
<kwd lng="en"><![CDATA[stock market risk]]></kwd>
</kwd-group>
</article-meta>
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