[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119270-en":3,"doc-seo-119270-105":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},119270,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","An Online Stock Market Recommender System Using Machine Learning - Master of Science Thesis","Investing in the stock market poses major obstacles for novice investors due to the overwhelming volume of data and the difficulty of filtering reliable information. In Kenya, these challenges often lead to irrational decisions and poor outcomes, especially when access to well-organised company information is limited. This research develops an online stock market recommender system using machine learning to support informed, personalised decisions based on historical patterns and data-driven insights. A mixed-methods sequential approach was used, with OOAD for system design and DSDM as the development framework.","Electronic Thesesand Dissertations  \n2023  \nAn Online stock market recommender system using machine learning.  \nMuoki, Sharleen Mwikali  \nSchool of Computing and Engineering Sciences Strathmore University  \nRecommendedCitation  \nMuoki, S. M. (2023) . An Online stock market recommender system using machine learning [Strathmore University] . [http://hdl.handle.net/11071/13534](http://hdl.handle.net/11071/13534)  \nFollow this andadditional works at:  [http://hdl.handle.net/11071/13534](http://hdl.handle.net/11071/13534)  \nAn Online Stock Market Recommender System Using Machine Learning  \nBy  \nSharleen Mwikali Muoki  \n090923  \nMaster of Science in Information Technology  \n2023  \nAn Online Stock Market Recommender System Using Machine Learning  \nBy  \nSharleen Mwikali Muoki  \n090923  \nSubmitted in Partial Fulfilment of the Requirements for the Degree of Master of Science in Information Technology at Strathmore University.  \nSchool of Computing and Engineering Sciences,  \nStrathmore University,  \nNairobi, Kenya.  \nJuly, 2023  \nDeclaration and Approval  \nDeclaration  \nI hereby affirm that this work has not been previously submitted for the award of a degree by this or any other university. To the best of my knowledge and belief, this thesis contains no material that has been previously published or written by another person, except where due reference is made in the thesis itself.  \n© No part of this thesis may be reproduced without the explicit permission of both the author and Strathmore University.  \nStudent’s Name: Sharleen Mwikali Muoki  \nSign:    \nDate:  \nApproval  \nThe thesis submitted by the candidate, Name of Candidate (in black colour), has been thoroughly reviewed and approved for examination by the following individuals:  \nDr. Esther Khakata,  \nSchool of Computing & Engineering Sciences, Strathmore University.  \nDr. Julius Butime,  \nDean, School of Computing & Engineering Sciences, Strathmore University.  \nDr. Bernard Shibwabo, Director of Graduate Studies, Strathmore University.  \nAbstract  \nInvesting in the stock market presented significant challenges for novice investors, primarily due to the overwhelming volume of available data. Consequently, novice investors often made irrational decisions and experienced unfavourable investment outcomes, particularly in the context of Kenya. This project aimed to address this issue by developing an innovative online stock market recommender system that utilised machine learning techniques. By leveraging these techniques, the system aimed to facilitate informed decision-making based on reliable data. Novice investors frequently encountered difficulties in accessing sufficient and well-organised information about the companies they intended to invest in, resulting in suboptimal returns. Traditional research methods often failed to adequately address the complexities and vastness of the stock market data. However, incorporating machine learning into the investment process held promise for analysing historical data, identifying patterns, and providing valuable insights to support informed decision-making. To comprehensively achieve the research objectives, a mixed-methods approach was employed, which integrated both quantitative and qualitative data collection and analysis in a sequential design. The Object-Oriented Analysis and Design (OOAD) technique was systematically and logically adopted to develop the software system. Additionally, the Dynamic System Development Methodology (DSDM) served as a guiding framework to address the identified problem and facilitate the development of the online stock market recommender system. This research project identified the information challenges faced by individual investors in the stock market, highlighting issues such as limited access to critical information, lack of necessary skills, and reliance on inaccurate media reports. Moreover, the study identified specific factors that significantly influenced stock market investments, including earni","cbCaieIARC9DAcS0","https://ap.wps.com/l/cbCaieIARC9DAcS0","pdf",11691339,1,142,"English","en",105,"# Declaration and Approval\n# Abstract\n# List of Figures\n# List of Tables\n# Abbreviations / Acronyms","[{\"question\":\"What problem does the online stock market recommender system address?\",\"answer\":\"The system targets the information overload and limited access to reliable, well-organised company data that causes novice investors to make irrational decisions and achieve poor results.\"},{\"question\":\"How does the system support investment decisions?\",\"answer\":\"It uses machine learning to analyse historical data, detect patterns, and produce personalised investment recommendations, alongside comprehensive investment performance reports.\"},{\"question\":\"Which research and development approaches were used in the study?\",\"answer\":\"The study applied a mixed-methods sequential design, using OOAD for analysis and design and DSDM as the guiding development methodology.\"}]","An Online Stock Market Recommender System Using Machine Learning - 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