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    <journal-meta>
      <journal-id journal-id-type="nlm-ta">Rea Press</journal-id>
      <journal-id journal-id-type="publisher-id">null</journal-id>
      <journal-title>Rea Press</journal-title><issn pub-type="ppub">3042-0210</issn><issn pub-type="epub">3042-0210</issn><publisher>
      	<publisher-name>Rea Press</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">https://doi.org/10.22105/aaa.v3i1.91</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
        <subj-group><subject>Systematic risk, Structural risk management, Institutional and policy factors, Interpretive Structural Modeling–Multiplication applied to classification, Iranian capital market.</subject></subj-group>
      </article-categories>
      <title-group>
        <article-title>The Application of Artificial Intelligence and Big Data–Based Predictive Models in Accounting</article-title><subtitle>The Application of Artificial Intelligence and Big Data–Based Predictive Models in Accounting</subtitle></title-group>
      <contrib-group><contrib contrib-type="author">
	<name name-style="western">
	<surname>Dodangeh</surname>
		<given-names>Parisa  </given-names>
	</name>
	<aff> Departmant of Accounting, University of Zanjan, Zanjan, Iran.</aff>
	</contrib></contrib-group>		
      <pub-date pub-type="ppub">
        <month>03</month>
        <year>2026</year>
      </pub-date>
      <pub-date pub-type="epub">
        <day>22</day>
        <month>03</month>
        <year>2026</year>
      </pub-date>
      <volume>3</volume>
      <issue>1</issue>
      <permissions>
        <copyright-statement>© 2026 Rea Press</copyright-statement>
        <copyright-year>2026</copyright-year>
        <license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.5/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p></license>
      </permissions>
      <related-article related-article-type="companion" vol="2" page="e235" id="RA1" ext-link-type="pmc">
			<article-title>The Application of Artificial Intelligence and Big Data–Based Predictive Models in Accounting</article-title>
      </related-article>
	  <abstract abstract-type="toc">
		<p>
			Systematic risk is one of the fundamental challenges in financial markets, and conventional approaches to addressing it have primarily focused on quantitative measurement and control instruments. However, experiences from financial crises indicate that reliance solely on these approaches, without considering institutional structures and policy frameworks, cannot achieve sustainable management of systematic risk. This study adopts a structural approach to analyze the factors influencing systematic risk management in the Iranian capital market, examining the roles of institutional, policy, and instrument-based factors within a causal framework. The research methodology integrates qualitative thematic analysis with Interpretive Structural Modeling (ISM) and Multiplication Applied to Classification (MICMAC) analysis. Data were derived from the structural judgments of experts in capital markets, financial policy, and risk management, and the final indicators were analyzed within a hierarchical structure. Findings indicate that institutional and policy factors occupy the foundational levels of the causal structure and act as key drivers shaping the behavior of the entire system, whereas financial instruments primarily occupy intermediate and outcome levels. These results suggest that systematic risk in the Iranian capital market is structural and endogenous in nature, and its effective management requires attention to institutional drivers and policy patterns. By providing a structural analytical framework, this study contributes to the literature on systematic risk management in emerging markets and offers a foundation for forward-looking policy design.
		</p>
		</abstract>
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