<?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>S0186-10422024000400111</article-id>
<article-id pub-id-type="doi">10.22201/fca.24488410e.2024.5191</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Machine learning portfolios for US stock prices: Directional forecasting before and during the COVID-19 pandemic]]></article-title>
<article-title xml:lang="es"><![CDATA[Portafolios de machine learning para precios accionarios estadounidenses: pronósticos direccionales antes y durante la pandemia del COVID-19]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Reyes Santiago]]></surname>
<given-names><![CDATA[Adán]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[González Maíz Jiménez]]></surname>
<given-names><![CDATA[Jaime]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Tecnológico de Monterrey  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de las Américas  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<volume>69</volume>
<numero>4</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0186-10422024000400111&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0186-10422024000400111&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0186-10422024000400111&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract In this study, we evaluate the performance of five Machine Learning (ML) portfolios-logistic regression, random forest, decision tree, gradient boosting, and adaptive boosting-against that of the Dow Industrial Average -passive approach. We consider an active portfolio management approach, employing out-of- sample backtesting to simulate the strategy performance as a categorical approach. We employ as predictors the opening price, the highest price, the lowest price, the closing price, the Williams %R and a the 13-week T-bills. During the whole period, before COVID-19, and during the pandemic, in all cases, at least one ML portfolio beats the index. These results suggest that overall, investors obtain positive outcomes if they use ML portfolios instead of investing passively in the index, obtaining the most benefits in times of greater uncertainty, such as the peak of the pandemic.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen En este estudio, evaluamos el rendimiento de cinco portafolios de Machine Learning (ML) (regresión logística, bosque aleatorio, árbol de decisión, aumento de gradiente y aumento adaptativo) en comparación con un enfoque pasivo del Dow Industrial Average. Consideramos un enfoque de gestión de portafolios activo, empleando pruebas de backtesting fuera de la muestra para simular el desempeño de la estrategia como un enfoque categórico. Empleamos como predictores el precio de apertura, el precio más alto, el precio más bajo, el precio de cierre, el %R de Williams y las T-bills a 13 semanas. Durante todo los escenarios considerados -incluido el periodo del COVID-19-, al menos un portafolio de ML fue superior al índice. Estos resultados sugieren que, en general, los inversores pueden obtener resultados positivos si utilizan carteras de ML, obteniendo los mayores beneficios en momentos de mayor incertidumbre, como el pico de la pandemia.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[COVID-19]]></kwd>
<kwd lng="en"><![CDATA[backtesting]]></kwd>
<kwd lng="es"><![CDATA[machine learning]]></kwd>
<kwd lng="es"><![CDATA[COVID-19]]></kwd>
<kwd lng="es"><![CDATA[backtesting]]></kwd>
</kwd-group>
</article-meta>
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