Predicting Stock Price Crash Risk Using Ant Colony Optimization Technique

نوع المستند : المقالة الأصلية

المؤلفون

1 تجارة الازهر بنات اسيوط

2 تجارة الازهر بنات القاهرة

3 تجارة الازهر بنين

10.21608/jsfc.2025.432108

المستخلص

Abstract
            The study[1] aimed to assess the impact of disclosure of research and development (R&D) activities on predicting the stock price crash risk (SPCR) using Ant Colony Optimization (ACO). Our study was based on the quarterly data of Mina Pharm for Pharmaceuticals and Chemical Industries, a pharmaceutical and healthcare sector company operating on the Egyptian Stock Exchange. We found that disclosure of Corporate R&D activities can effectively reduce the SPCR, particularly with a company with product diversification, cost reduction, and information transparency constraints. We propose a novel method for accurately predicting the SPCR through ACO. It also contributes to continuous improvement until the optimal solution is reached, supports sustainable competitive advantage, and adds to the existing body of literature.
 
 
[1] This paper is based on dissertation titled” A proposed approach for predicting the Stock price by using the ant colony algorithm to support sustainable competitive advantage- an applied study” members of committee was: Prof. Laila Abd El - Hamid Lotfy Prof. Emad Saad Mohamed El-Saygh (Co-Chair .Dr. Aliaa Abdel- Latif Ahmed Abed(Co-Chair)- A thesis from researcher Doaa Mohamed Sayed Ahmed- Assistant Lecturer to obtain a PhD in Accounting.
 

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