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International Journal of Advanced Artificial Intelligence Research

Open Access Peer Review International
Open Access

EXPLAINABLE MACHINE LEARNING FOR FINANCIAL ANALYSIS

DOI https://doi.org/10.55640/ijaair-v02i07-02
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Olabayoji Oluwatofunmi Oladepo. 1 , Opeyemi Eebru Alao 1 ,
4 Department of Computer Science, Swansea University, Singleton Park, Sketty, Swansea, SA2 8PP, Wales, United Kingdom
4 Department of Computer Science, Swansea University, Singleton Park, Sketty, Swansea, SA2 8PP, Wales, United Kingdom

Abstract

Technical analysis plays a vital role in the dynamic world of stock trading by helping traders identify patterns and trends in market prices. However, interpreting these patterns can be complicated, especially for novice traders, due to the complexity of charts and indicators involved. This research introduces a user-friendly tool aimed at making technical analysis more accessible. It provides a streamlined interface for analysing and forecasting stock trends, catering to users of varying expertise. Most existing trading platforms either lack predictive features or operate as opaque 'black-box' systems, which can deter beginner users. To address this, the research developed a solution that simplifies financial analysis and enhances clarity. This tool stands out by reducing reliance on complex visuals, improving user understanding of financial indicators, and explaining how they influence market predictions. A unique feature of the tool is its use of Explainable Artificial Intelligence (XAI), which provides transparency and builds user trust. Feedback from user testing was positive, with participants highlighting the tool’s clarity and interactivity as valuable for making informed investment decisions.

Zenodo DOI:- https://doi.org/10.5281/zenodo.16566789

Keywords

Explainable AI, stock, support vector machine (SVM), random forest, rule builder

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