[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122235-en":3,"doc-seo-122235-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122235,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Predicting the Trends of the Egyptian Stock Market Using Machine Learning and Deep Learning Methods","Stock price movement forecasting remains a central concern for researchers and investors because financial markets change continuously under persistent macroeconomic conditions. Accurate trend prediction supports portfolio optimization by highlighting equities likely to rise while reducing exposure to those expected to fall. This study forecasts stock price movements for selected real-estate companies on the Egyptian Stock Exchange from 2013–2022 using machine learning (RF, AdaBoost, SVM, KNN) and deep learning (ANN, RNN, LSTM). The results compare predictive accuracy to reduce uncertainty in stock-market forecasting. Adaptive Boosting delivers the highest accuracy at 99.5%, and LSTM achieves the lowest error rate, with RNN following. Overall, data-driven modeling helps mitigate risk and improve investment decisions.","Computational Journal of Mathematical and Statistical Sciences 4(1), 186–221  \nDOI:10.21608/cjmss.2024.320645.1077  \n[https:](https://cjmss.journals.ekb.eg/)[//](https://cjmss.journals.ekb.eg/)[cjmss.journals.ekb.eg](https://cjmss.journals.ekb.eg/)[/](https://cjmss.journals.ekb.eg/)  \nResearch article  \nPredicting the Trends ofthe Egyptian Stock Market Using Machine Learning and Deep Learning Methods  \nHeba Elsegai 1 * , Hanem S. H. M. El-Metwally2 Hisham M. Almongy 1  \n1 Department of Applied Statistics, Faculty of Commerce, Mansoura University, Mansoura City 35516, Egypt  \n2 Department of Basic Science-Higher Institute of Administrative Sciences, El-Menzala, Egypt  \n* Correspondence: [dr.heba.elsegai@mans.edu.eg](dr.heba.elsegai@mans.edu.eg)  \nAbstract: The prediction of stock price movements has remained a significant area of interest for researchers and investors, driven by the dynamic nature of financial markets and persistent economic fluctuations. The ability to forecast price trends enables investors to optimize their portfolios by identifying stocks likely to appreciate in value while avoiding those predicted to decline, thus maximizing returns and minimizing losses. This study focuses on forecasting the stock price movements of selected companies in the real estate sector listed on the Egyptian Stock Exchange over the period 2013– 2022. It employs a range of machine learning algorithms, including Random Forest (RF), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), and K-Nearest Neighbours (KNN), as well as deep learning architectures such as Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks. The study aims to evaluate and compare the performance of these methods in terms of predictive accuracy, with the ultimate goal of reducing the uncertainty associated with stock market forecasting. By applying these computational techniques, the research seeks to uncover patterns and insights within large datasets, providing actionable intelligence for investors and traders. The comparative analysis reveals that Adaptive Boosting achieves the highest accuracy among the machine learning algorithms, with a precision rate of 99.5% . Among deep learning models, LSTM exhibits superior predictive capability, yielding the lowest error rate, followed by RNN with an error rate of 0.3 . These findings demonstrate the efficacy of advanced machine learning and deep learning models in stock price prediction, offering robust tools for enhancing decisionmaking processes in financial markets. The results highlight the potential of integrating data-driven methodologies to mitigate risks and improve investment outcomes.  \nKeywords: Stock market data analysis, Machine learning models, Deep learning models, Model assessment metrics, Prediction accuracy  \nMathematics Subject Classification: 68T07; 91G15; 62P05  \nReceived: 10 October 2024; Revised: 20 November 2024; Accepted: 25 November 2024; Online: 11 December 2024 .  Copyright: © 2025 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license.  \n1. Introduction  \nThe application of machine learning in forecasting is a developing field that leverages artificial intelligence and data analysis to predict future events. This form of machine learning enables systems and programs to learn and adapt based on the data they gather and assess. It can significantly impact sectors like e-commerce, marketing, weather forecasting, healthcare, and finance. Recent advancements in machine learning technology have enhanced the accuracy of predictions and improved decision-making. However, forecasting future stock performance presents numerous scientific challenges due to the vast amount of trading data stored in stock databases. Thus, an overview is presented to highlight the significance of employing machine learning methods in predicting stock performance,","cbCaihFZmLvYxaXQ","https://ap.wps.com/l/cbCaihFZmLvYxaXQ","pdf",1141986,1,36,"English","en",105,"# Introduction\n## Related Work\n## Big Data and Machine Learning Approaches\n## RNN and LSTM for Multi-Source Prediction\n# Problem Focus and Forecast Planning","[{\"question\":\"Which companies and time period are used in the study’s stock trend forecasting?\",\"answer\":\"The study forecasts selected real-estate sector companies listed on the Egyptian Stock Exchange over the period from 2013 to 2022.\"},{\"question\":\"What machine learning and deep learning models are compared for prediction?\",\"answer\":\"Machine learning models include Random Forest, AdaBoost, Support Vector Machine, and K-Nearest Neighbours. Deep learning models include Artificial Neural Networks, Recurrent Neural Networks, and Long Short-Term Memory networks.\"},{\"question\":\"Which methods achieve the best predictive performance?\",\"answer\":\"Adaptive Boosting achieves the highest accuracy among machine learning methods at 99.5%. Among deep learning models, LSTM shows the lowest error rate, and RNN comes next with an error rate reported as 0.3.\"}]","Predicting the Trends of the Egyptian Stock Market Using Machine Learning and Deep Learning Methods | PDF",1785809561,91,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-the-trends-of-the-egyptian-stock-market-using-machine-learning-and-deep-learning-methods","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-the-trends-of-the-egyptian-stock-market-using-machine-learning-and-deep-learning-methods/122235/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which companies and time period are used in the study’s stock trend forecasting?","Question",{"text":75,"@type":76},"The study forecasts selected real-estate sector companies listed on the Egyptian Stock Exchange over the period from 2013 to 2022.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning and deep learning models are compared for prediction?",{"text":80,"@type":76},"Machine learning models include Random Forest, AdaBoost, Support Vector Machine, and K-Nearest Neighbours. Deep learning models include Artificial Neural Networks, Recurrent Neural Networks, and Long Short-Term Memory networks.",{"name":82,"@type":73,"acceptedAnswer":83},"Which methods achieve the best predictive performance?",{"text":84,"@type":76},"Adaptive Boosting achieves the highest accuracy among machine learning methods at 99.5%. Among deep learning models, LSTM shows the lowest error rate, and RNN comes next with an error rate reported as 0.3.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]