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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

References

Achelis, S. B., & Achelis, S. (2001). Technical analysis from A to Z (pp. 199–200). New York: McGraw-Hill.
Anbalagan, T., & Maheswari, S. U. (2015). Classification and prediction of stock market index based on fuzzy metagraph. Procedia Computer Science, 47, 214–221.
Epstein, L., & Roze, G. D. (2023). Trading for dummies. John Wiley & Sons.
Hayes, A. (2021). Stochastic oscillator: What it is, how it works, how to calculate. Investopedia. URL: https://www.investopedia.com/terms/s/stochasticoscillator.asp (Accessed: October 16, 2024).
Hayes, A. (2022). Financial markets: Role in the economy, importance, types, and examples. Investopedia, 31(5), 34–43.
Jain, D. K., Bhatele, K. R., Garg, D., & Dhiman, G. (Eds.). (2025). A Compendium of Responsible Artificial Intelligence. CRC Press.
Kane, M. J., Price, N., Scotch, M., & Rabinowitz, P. (2014). Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks. BMC Bioinformatics, 15, 1–9.
Mayenberger, D. (2019). How to overcome modelling and model risk management challenges with artificial intelligence and machine learning. Journal of risk management in financial institutions, 12(3), 241-255.
Rane, N., Choudhary, S., & Rane, J. (2023). Explainable Artificial Intelligence (XAI) approaches for transparency and accountability in financial decision-making. Available at SSRN 4640316.
Yang, H., Chan, L., & King, I. (2002, August). Support vector machine regression for volatile stock market prediction. In International conference on intelligent data engineering and automated learning (pp. 391–396). Berlin, Heidelberg: Springer Berlin Heidelberg.

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