An Online stock market recommender system using machine learning

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dc.contributor.authorMuoki, S. M.
dc.date.accessioned2023-10-12T09:44:06Z
dc.date.available2023-10-12T09:44:06Z
dc.date.issued2023
dc.descriptionFull- text thesis
dc.description.abstractInvesting 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 earnings per share, firm fundamentals, market trends, and news sentiment. To overcome these challenges, the project focused on developing an advanced online stock market recommender system that effectively leveraged machine learning techniques to provide personalised investment recommendations. The system underwent rigorous testing, demonstrating superior performance by offering accurate recommendations and comprehensive investment performance reports. Keywords: Investing; Stock market; Novice investors; Poor investment outcomes; Online stock market recommender system; Machine learning techniques; Informed decision-making; Information challenges; Mixed-methods approach; Quantitative and qualitative data; Object-Oriented Analysis and Design (OOAD); Dynamic System Development Methodology (DSDM); Earnings per share (EPS); Market trends; News sentiment; Investment performance reports
dc.identifier.citationMuoki, S. M. (2023). An Online stock market recommender system using machine learning [Strathmore University]. http://hdl.handle.net/11071/13534
dc.identifier.urihttp://hdl.handle.net/11071/13534
dc.language.isoen
dc.publisherStrathmore University
dc.titleAn Online stock market recommender system using machine learning
dc.typeThesis
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