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  <doi_batch_id>aspg-38-2433-1792226216</doi_batch_id>
  <timestamp>20261017083656</timestamp>
  <depositor>
   <depositor_name>American Scientific Publishing Group</depositor_name>
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  <journal>
   <journal_metadata language="en">
    <full_title>Journal of Sustainable Development and Green Technology</full_title>
    <abbrev_title>JSDGT</abbrev_title>
    <issn media_type="electronic">2836-5399</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2024</year>
    </publication_date>
    <journal_volume>
     <volume>4</volume>
    </journal_volume>
    <issue>1</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Enhancing Business Sustainability through Stock Analysis: A Machine Learning Approach</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Noura</given_name>
      <surname>Metawa</surname>
      <affiliations>
       <institution>
        <institution_name>University of Sharjah, United Arab Emirates</institution_name>
       </institution>
       <institution>
        <institution_name>Faculty of Commerce, Mansoura University, Egypt</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Saad</given_name>
      <surname>Metawa</surname>
      <affiliations>
       <institution>
        <institution_name>Faculty of Commerce, Mansoura University, Egypt</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>This study explores the integration of machine learning methodologies in stock analysis to enhance the understanding of the relationship between sustainable business practices and financial performance. Against the backdrop of a shifting investment landscape that emphasizes responsible and informed decision-making, our research addresses the need for innovative approaches in evaluating stocks within a sustainability framework. Leveraging a combination of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and linear regression, we systematically analyze a dataset comprising sustainability metrics and stock performance. The DBSCAN clustering identifies distinct groups of stocks based on sustainability profiles, offering novel insights into market segmentation. Concurrently, linear regression models quantitatively reveal the impact of sustainability metrics on stock outcomes. The results affirm the significance of sustainability considerations in investment decisions, presenting a compelling case for the adoption of machine learning techniques in responsible investing strategies.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2024</year>
    </publication_date>
    <pages>
     <first_page>17</first_page>
     <last_page>21</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">2433</item_number>
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     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
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     <doi>10.54216/JSDGT.040104</doi>
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