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  <front>
    <journal-meta>
      <journal-id journal-id-type="issn">2583-7214</journal-id>
      <journal-title-group>
        <journal-title>TMP Universal Journal of Research and Review Archives</journal-title>
      </journal-title-group>
      <issn pub-type="epub">2583-7214</issn>
      <publisher>
        <publisher-name>Ms Ashvi Jain</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.69557/ujrra.v4i2s.194</article-id>
      <title-group>
        <article-title>OPTIMIZATION OF INVESTMENT PORTFOLIOS USING THE ARTIFICIAL BEE COLONY ALGORITHM: EVIDENCE FROM ISTANBUL STOCK MARKETS</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Ganji</surname>
            <given-names>Farshad</given-names>
          </name>
          <xref ref-type="aff" rid="aff-1"/>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Erdoğdu</surname>
            <given-names>Aylin</given-names>
          </name>
          <xref ref-type="aff" rid="aff-2"/>
        </contrib>
      </contrib-group>
      <aff id="aff-1">Institute of Social Sciences, Istanbul Arel University</aff>
      <aff id="aff-2">Department of Finance and Banking, Faculty of Economics and Administrative Sciences, Istanbul Arel University</aff>
      <pub-date publication-format="electronic" date-type="pub" iso-8601-date="2025-06-16">
        <day>16</day>
        <month>06</month>
        <year>2025</year>
      </pub-date>
      <volume>4</volume>
      <issue>2s</issue>
      <permissions>
        <copyright-year>2025</copyright-year>
        <license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0">
          <license-p>
            This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License which permits unrestricted use, sharing, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
            <uri xlink:href="https://creativecommons.org/licenses/by/4.0">https://creativecommons.org/licenses/by/4.0</uri>
          </license-p>
        </license>
      </permissions>
      <self-uri xlink:href="https://tmp.twistingmemoirs.com/index.php/ujrra/article/view/194"/>
      <abstract>
        <p>In the realm of financial markets, accurate forecasting of stock prices remains a challenging yet essential endeavor for investors, analysts, and policymakers alike. This study delves into the application of advanced computational techniques, specifically the Artificial Bee Colony (ABC) algorithm, for enhancing forecasting accuracy in the context of stock price clustering on the Borsa Istanbul (BIST) exchange. The BIST 50 index and five prominent companies selected from the BIST 50 such as Akbank (AKBNK), BIM Birleşik Mağazalar (BIMAS), Turkish Airlines (THYAO), Ford Otosan (FROTO), and Aselsan (ASELS) are examined across three distinct time intervals: fifteen days, two months, and two years, spanning from January 1, 2015, to February 1, 2024. Data for this analysis is sourced from the Investing.com platform.The study employs a multifaceted approach, encompassing moving averages, Relative Strength Index (RSI) values, and linear regression analysis, to discern short-term, medium-term, and long-term trends in stock prices. Moving averages provide insights into trend directions and market sentiment, while RSI values indicate momentum and potential overbought or oversold conditions. Linear regression analysis offers predictive capabilities, aiding in the identification of future price movements based on historical data.The findings reveal nuanced patterns and trends in stock prices across different time intervals. Short-term analysis exposes immediate market fluctuations, medium-term analysis identifies momentum over intermediate horizons, and long-term analysis unveils overarching trends. The integration of the ABC algorithm with machine learning techniques further enhances forecasting accuracy, offering a promising avenue for investors and market participants to make informed decisions.In conclusion, this study contributes to computational finance by demonstrating the efficacy of the ABC algorithm in stock price clustering on the Borsa Istanbul exchange. The insights hold implications for portfolio diversification, risk management, and investment strategies, offering valuable guidance to stakeholders navigating the dynamic landscape of financial markets.</p>
      </abstract>
    </article-meta>
  </front>
</article>