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<Article>
<Journal>
				<PublisherName>Iranian Financial Engineering Association(IFEA)</PublisherName>
				<JournalTitle>International Journal of Finance &amp; Managerial Accounting</JournalTitle>
				<Issn>2588-4379</Issn>
				<Volume>13</Volume>
				<Issue>49</Issue>
				<PubDate PubStatus="epublish">
					<Year>2026</Year>
					<Month>03</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Explaining Optimal Portfolio Management and Adverse Risk Management Using Econometric Systems</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>205</FirstPage>
			<LastPage>218</LastPage>
			<ELocationID EIdType="pii">24362</ELocationID>
			
<ELocationID EIdType="doi">10.22034/ijfma.2026.78861.2323</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>Shahram</FirstName>
					<LastName>Izadi</LastName>
<Affiliation>Department of Financial Management, SR.C, Islamic Azad University, Tehran, Iran</Affiliation>

</Author>
<Author>
					<FirstName>Ali</FirstName>
					<LastName>Esmaeilzadeh Maghri</LastName>
<Affiliation>Department of Accounting, CT.C, Islamic Azad University, Tehran, Iran</Affiliation>

</Author>
<Author>
					<FirstName>Farzaneh</FirstName>
					<LastName>Haji Hassani</LastName>
<Affiliation>Department of Economics, SR.C, Islamic Azad University, Tehran, Iran</Affiliation>

</Author>
<Author>
					<FirstName>Shadi</FirstName>
					<LastName>Shahverdian</LastName>
<Affiliation>Department of Financial Management, QC.C, Islamic Azad University, Tehran, Iran</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2025</Year>
					<Month>10</Month>
					<Day>06</Day>
				</PubDate>
			</History>
		<Abstract>The purpose of this research is to explain the management of optimal portfolio optimization and adverse risk management using econometric systems. The tool for collecting financial information is the data of top companies listed on the stock exchange, which derive their value from a base asset. Obviously, to enter the market of top companies, an investor needs to predict the future trend of particle swarm optimization to hedge their adverse risk. For this purpose, the present research has proceeded to select a suitable equation for modeling the economy of portfolio optimization and adverse risk management. Portfolio optimization in adverse risk management is shown for the years 2016-2021. In building the models, 65% of the data were used for training, 15% for validation, and 20% for fuzzy testing. The fuzzy model technique had better performance in predicting adverse risk, and the model that simultaneously uses scenario number one and ANFIS provides a more accurate prediction. This is because intelligent techniques provide a better estimate of the future return of adverse risk stocks (Value at Risk and Ultimate Expected Shortfall) compared to the historical average return.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">financial markets</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Market risk</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">investment decisions</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Downside risks</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Price fluctuations</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">http://www.ijfma.ir/article_24362_a95fc8c14636fe3e40866d5ca57698ec.pdf</ArchiveCopySource>
</Article>
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