[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120623-en":3,"doc-seo-120623-105":30,"detail-sidebar-cat-0-en-105":90},{"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},120623,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","CONSTRUCTION OF STOCK PORTFOLIOS BY MACHINE LEARNING METHODS - Master Thesis Defense","The study reviews the theoretical foundations and practical performance of Long Short-Term Memory Networks (LSTM) and Random Forest methods for stock market prediction. It first explores the mathematics behind these algorithms, including gradient descent, automatic differentiation, and an introduction to recurrent neural networks. It then builds rule-based portfolio strategies using technical or fundamental indicators, predicting returns and ranking stocks to form diversified portfolios with annual rebalancing. Portfolio results are benchmarked against market indices such as S&P 500 using CAGR, assessing whether machine learning can support financial decision-making and motivate future research.","The College of Graduate Studies and the College of Science Cordially Invite You to a  \nMaster Thesis Defense  \nEntitled  \nCONSTRUCTION OF STOCK PORTFOLIOS BY  \nMACHINE LEARNING METHODS  \nby  \nAlfan Gehad Hasan Abulehia  \nStudent ID: 201735442  \nFaculty Advisor  \nDr. Ho Hon Leung, Department of Mathematics  \nCollege of Science  \nDate & Venue  \n4:30 pm-6:00 pm  \nThursday, 24 April 2025  \nF3-132  \nAbstract  \nWe review the theoretical foundations and empirical workings of Long Short-Term Memory Networks (LSTM) and Random Forest for stock market prediction. To be precise, our work initiates with a deep-dive exploration in the mathematics of these algorithms which covers topics like gradient descent, automatic differentiation, and a bit on recurrent neural networks as well.  \nAn application creates stock portfolios through LSTM and Random Forest rules- based strategies, using either technical or fundamental indicators as input variables. The process involves predicting returns and ranking stocks accordingly to build portfolios with a certain level of diversification and annual rebalancing over a defined back-testing period. The paper then benchmarks the performance of such portfolios against market benchmarks like the S&P 500, with CAGR as the key metric to be calculated.  \nThe results of our study measure the ability of Machine Learning based strategies to outperform the traditional market indices and, in a broader sense, to provide more information on whether or not Machine Learning has the potential to aid in financial decisionmaking. Such results will, thus, have possible extensions for future research.  \nKeywords: Long Short-Term Memory Networks (LSTM), Random Forest, gradient descent, recurrent neural networks, diversification, portfolio.  \nتتشرف كلية الدراسات العليا و كلية العلوم بدعوتكم لحضور  \nمناقشة أطروحة رسالة الماجستير  \nالعنوان  \nبناء محافظ الأسهم باستخدام طرق التعلم الآلي  \nللطالب   \nألفان جهاد حسن أبولحية  \nالرقم الجامعي201735442 :  \nالمشرف  \nد. هو هون ليونغ، قسم الرياضيات  \nكلية العلوم  \nالمكان والزمان  \n4:30-6:00  \nالخميس، 24 ابريل 2025  \nF3-132  \nالملخص  \nنستعرض الأسس النظرية والآليات العملية لكل من شبكات الذاكرة طويلة وقصيرة المدى ) LSTM ( وغابة القرار ( Randon Forest ) في توقعات سوق الأسهم. على وجه التحديد، يبدأ عملنا باستكشاف عميق في الرياضيات الخاصة بهذه الخوارزميات،متناولًا موضوعات مثل الًنحدار التدريجي، التفاضل التلقائي، ونظرة على الشبكات العصبية المتكررة.يتم إنشاء محافظ الأسهم من خلال استراتيجيات قائمة على قواعد LSTM وRandom Forest ، باستخدام المؤشرات الفنيةأو الأساسية كمتغيرات إدخال. تتضمن العملية التنبؤ بالعوائد وتصنيف الأسهم وفقاا لذلك، لبناء محافظ تتسم بمستوى معين منالتنويع وإعادة التوازن السنوية خلال فترة اختبار محددة. بعد ذلك، يتم قياس أداء هذه المحافظ مقارنةابالمؤشرات السوقية مثل S&P 500، حيث يتم احتساب معدل النمو السنوي المركب )CAGR( كمقياس أساسي.تقيس نتائج دراستنا قدرة استراتيجيات التعلم الآلي على التفوق على المؤشرات التقليدية للسوق، وفي سياق أوسع، تقييم ما إذاكان التعلم الآلي يمكن أن يساعد في اتخاذ القرارات المالية. وبالتالي، يمكن أن تمتد هذه النتائج لتفتح آفاقاا للبحث المستقبلي.  \nكلمات البحث الرئيسية: شبكات الذاكرة طويلة وقصيرة المدى، وغابة القرار العشوائية، الًنحدار التدريجي، الشبكات العصبيةالمتكررة، التنويع، محافظ.","cbCaie19xxlzn6Ca","https://ap.wps.com/l/cbCaie19xxlzn6Ca","pdf",877383,1,2,"English","en",105,"# Abstract\n## LSTM and Random Forest foundations\n## Portfolio construction with technical/fundamental indicators\n## Return prediction, ranking, rebalancing\n## Benchmarking vs S&P 500 using CAGR\n## Performance implications and future research","[{\"question\":\"What machine learning models are used for stock market prediction?\",\"answer\":\"The thesis evaluates Long Short-Term Memory Networks (LSTM) and Random Forest for predicting stock market behavior.\"},{\"question\":\"How are stock portfolios constructed in the proposed approach?\",\"answer\":\"Portfolios are built using rule-based strategies driven by LSTM and Random Forest, with either technical or fundamental indicators as input variables.\"},{\"question\":\"What metric is used to benchmark portfolio performance against market benchmarks?\",\"answer\":\"Performance is benchmarked against indices such as S\\u0026P 500, with CAGR used as the key metric.\"}]","CONSTRUCTION OF STOCK PORTFOLIOS BY MACHINE LEARNING METHODS - Master Thesis Defense | PDF",1785730951,5,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":28},"construction-of-stock-portfolios-by-machine-learning-methods-master-thesis-defense","",{"@graph":36,"@context":84},[37,53,67],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":21},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/construction-of-stock-portfolios-by-machine-learning-methods-master-thesis-defense/120623/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What machine learning models are used for stock market prediction?","Question",{"text":74,"@type":75},"The thesis evaluates Long Short-Term Memory Networks (LSTM) and Random Forest for predicting stock market behavior.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How are stock portfolios constructed in the proposed approach?",{"text":79,"@type":75},"Portfolios are built using rule-based strategies driven by LSTM and Random Forest, with either technical or fundamental indicators as input variables.",{"name":81,"@type":72,"acceptedAnswer":82},"What metric is used to benchmark portfolio performance against market benchmarks?",{"text":83,"@type":75},"Performance is benchmarked against indices such as S&P 500, with CAGR used as the key metric.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,108,113,118,121,126,129,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":105,"show_sort_weight":106,"slug":107},"Comic",60,"comic",{"id":109,"doc_module":4,"doc_module_name":46,"category_name":110,"show_sort_weight":111,"slug":112},6,"Technology",50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":119,"slug":120},30,"research-report",{"id":122,"doc_module":4,"doc_module_name":46,"category_name":123,"show_sort_weight":124,"slug":125},9,"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":29,"slug":136},19,"General","general"]