[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118698-en":3,"doc-seo-118698-105":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},118698,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Unlocking the Potential of Machine Learning in Portfolio Selection - A Hybrid Approach with Genetic Optimization","Machine learning for financial market prediction is widely used to extract patterns, yet portfolio selection success depends on optimizing the factors that drive predictive accuracy. This study integrates machine learning with optimization to improve stock selection and forecasting performance. It applies hyper-parameter optimization and three models—XGBoost, LSTM, and Deep RankNet—selected for their ability to model complex financial data and nonlinear relationships. Results indicate a 40% improvement using genetic-based optimization and an average daily return of 0.47% via feature engineering. A scalable framework targets medium and small traders under resource constraints, with adaptability to varying market conditions and objectives.","CARMA 2024  \n6th Int. Conf. on Advanced Research Methods and Analytics Universitat Politcnica de Valncia, Valncia, 2024 DOI: [https://doi.org/10.4995/CARMA2024.2024.17554](https://doi.org/10.4995/CARMA2024.2024.17554)  \nUnlocking the Potential of Machine Learning in Portfolio Selection: A Hybrid Approach with Genetic Optimization  \nChaher Alzaman  \nDepartment of Supply Chain and Business Technology Management, John Molson School of Business, Concordia University, Montreal, Quebec, H3H 0A1, Canada.  \nHow to cite: Alzaman, C. 2024. Contemporary issues in Financial Technology: the role of the Internet. In: 6th International Conference on Advanced Research Methods and Analytics (CARMA 2024) . Valencia, 26-28 June 2024. [https://doi.org/10.4995/CARMA2024.2024.17554](https://doi.org/10.4995/CARMA2024.2024.17554)  \nAbstract  \nIn the field of financial market predictions, machine learning has been widely used to identify patterns and gain valuable insights. However, for success in portfolio selection, it is crucial to optimize factors that impact accuracy. This study focuses on combining machine learning and optimization to enhance stock selection and prediction capabilities, thereby addressing a critical challenge faced by investors and traders. The work starts with hyper-parameter optimization and utilizes three different machine learning algorithms: XGBoost, LSTM, and Deep RankNet. These algorithms were chosen for their proven performance in handling complex financial data and capturing nonlinear relationships. Our findings show a 40% improvement in results through the use of a genetic-based optimization technique, as well as a promising daily average return of 0.47% through a novel feature engineering approach. The study provides a framework for optimizing and learning in financial portfolio selection, with promising results for medium and small-sized traders who often face resource constraints in developing sophisticated trading strategies. The proposed approach offers a scalable and adaptable solution that can be tailored to different market conditions and investment objectives.  \nKeywords: Artificial Intelligence; Machine Learning; Optimization; Financial Markets; Predictive Analytics.  \n1. Introduction  \nMachine learning has gained widespread attention in the financial market as a tool for predicting stock prices, foreign exchange rates and other market trends. With its ability to analyze large amounts of data, machine learning algorithms can provide more accurate predictions compared to traditional statistical methods. Shah (2007) highlights two main approaches in stock  \nThis work is licensed under a Creative Commons License CC BY-NC-SA 4.0  \nEditorial Universitat Politcnica de Valncia 220  \nprediction: Fundamental Analysis, where analysis is based on a company's financial characteristics (such as past performance, assets, earnings, etc.), and Technical Analysis, where patterns in past stock prices are studied. Despite the efficient market hypothesis (Jensen, 1978) stating that stock prices cannot be predicted and the random-walk hypothesis (Malkiel, 1973) suggesting stock prices only depend on future information and not on history, research by Basaket al. (2019), Chen et al. (2020), and others argue that some elements of stock behavior are predictable.  \nThis work employs LSTM, XGBoost, and Deep RankNet. To set a background, two classes of methods have been prominent in the literature (Machine Learning applications in financial markets): Artificial Neural Networks (ANN) and Ensemble tree-based algorithms. ANN is atthe heart of Deep Learning, which in turn is a subset of Machine Learning (ML) geared toward more complex systems (e.g., big data). LSTM (long short-term memory) is an artificial recurrent neural network tailored to sequential data, such as closing prices of financial assets. The XGBoost (XGB) is an ensemble decision tree-based algorithm that is quite popular in financial market predictions. Basak et al. (2020) an","cbCaic86yT9lBaMP","https://ap.wps.com/l/cbCaic86yT9lBaMP","pdf",552805,1,15,"English","en",105,"# Introduction\n## Literature Review","[{\"question\":\"What problem does the study address in portfolio selection?\",\"answer\":\"It addresses the need to optimize factors affecting prediction accuracy when using machine learning for stock selection and forecasting.\"},{\"question\":\"Which machine learning models are used in the proposed approach?\",\"answer\":\"The study uses XGBoost, LSTM, and Deep RankNet, combined with hyper-parameter optimization.\"},{\"question\":\"How much improvement does genetic-based optimization achieve?\",\"answer\":\"The findings report a 40% improvement in results and a promising average daily return of 0.47% supported by the feature engineering approach.\"}]","Unlocking the Potential of Machine Learning in Portfolio Selection - A Hybrid Approach with Genetic Optimization | PDF",1785684931,38,{"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},"unlocking-the-potential-of-machine-learning-in-portfolio-selection-a-hybrid-approach-with-genetic-optimization","",{"@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/unlocking-the-potential-of-machine-learning-in-portfolio-selection-a-hybrid-approach-with-genetic-optimization/118698/",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","2026-08-02",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},"What problem does the study address in portfolio selection?","Question",{"text":75,"@type":76},"It addresses the need to optimize factors affecting prediction accuracy when using machine learning for stock selection and forecasting.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are used in the proposed approach?",{"text":80,"@type":76},"The study uses XGBoost, LSTM, and Deep RankNet, combined with hyper-parameter optimization.",{"name":82,"@type":73,"acceptedAnswer":83},"How much improvement does genetic-based optimization achieve?",{"text":84,"@type":76},"The findings report a 40% improvement in results and a promising average daily return of 0.47% supported by the feature engineering approach.","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"]