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  <Article>
    <Journal>
      <PublisherName>ijesm</PublisherName>
      <JournalTitle>International Journal of Engineering, Science and</JournalTitle>
      <PISSN>I</PISSN>
      <EISSN>S</EISSN>
      <Volume-Issue>volume 15,issue 1</Volume-Issue>
      <PartNumber/>
      <IssueTopic>Multidisciplinary</IssueTopic>
      <IssueLanguage>English</IssueLanguage>
      <Season>January 2026</Season>
      <SpecialIssue>N</SpecialIssue>
      <SupplementaryIssue>N</SupplementaryIssue>
      <IssueOA>Y</IssueOA>
      <PubDate>
        <Year>-0001</Year>
        <Month>11</Month>
        <Day>30</Day>
      </PubDate>
      <ArticleType>Engineering, Science and Mathematics</ArticleType>
      <ArticleTitle>Risk assessment in financial portfolios using fuzzy logic</ArticleTitle>
      <SubTitle/>
      <ArticleLanguage>English</ArticleLanguage>
      <ArticleOA>Y</ArticleOA>
      <FirstPage>28</FirstPage>
      <LastPage>42</LastPage>
      <AuthorList>
        <Author>
          <FirstName>Sanjay</FirstName>
          <LastName>Kumar</LastName>
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
        </Author>
      </AuthorList>
      <DOI/>
      <Abstract>This study examines the application of fuzzy logic as an advanced analytical framework for assessing risk in financial portfolios under conditions of uncertainty and imprecision. Conventional portfolio risk measures, such as variance and beta, rely heavily on precise numerical data and often fail to capture the ambiguity and subjectivity inherent in real-world financial markets. Fuzzy logic, grounded in fuzzy set theory and linguistic variables, enables the modelling of qualitative and quantitative risk factors in a more flexible and realistic manner. The paper develops a conceptual fuzzy inference system for evaluating portfolio risk by integrating key variables such as market volatility, liquidity, asset correlation, and return stability. By transforming these inputs into fuzzy membership functions and rule-based evaluations, the model provides nuanced risk classifications that better reflect investor perceptions and market dynamics. The findings indicate that fuzzy-based risk assessment offers a more comprehensive and adaptive approach for portfolio decision-making.</Abstract>
      <AbstractLanguage>English</AbstractLanguage>
      <Keywords/>
      <URLs>
        <Abstract>https://ijesm.co.in/ubijournal-v1copy/journals/abstract.php?article_id=16100&amp;title=Risk assessment in financial portfolios using fuzzy logic</Abstract>
      </URLs>
      <References>
        <ReferencesarticleTitle>References</ReferencesarticleTitle>
        <ReferencesfirstPage>16</ReferencesfirstPage>
        <ReferenceslastPage>19</ReferenceslastPage>
        <References/>
      </References>
    </Journal>
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