Examinando por Autor "Senyo, P.K."

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    Predicting cryptocurrency prices during economic uncertainty with explainable Artificial Intelligence
    (Routledge, 2026) González Cortés, Daniel Alejandro ; Nandy, Monomita; Suman Lodh,; Senyo, P.K.; Wu, Jian; Onieva Caracuel, Enrique
    Predicting cryptocurrency prices is challenging due to their high volatility. This challenge is more pronounced during economic uncertainty, such as the 2008 financial crisis and the COVID-19 pandemic. While machine learning models can help in the prediction of cryptocurrency prices, their underlying conditions influencing the outcomes are sometimes unknown, and there is a lack of consensus on appropriate techniques to use for technical prediction and circumstances under which they may be suitable. In this paper, we apply an existing explainable artificial intelligence (XAI) framework, specifically SHAP, to identify suitable analytical techniques and the optimized set of parameters for technical trading prediction based on the two most valuable cryptocurrencies, Bitcoin and Ethereum. Rather than developing a new model, our contribution lies in systematically applying XAI techniques to uncover variable importance and model behavior in volatile market conditions. The results show that our explainable AI model is capable of efficiently forecasting closing, high, and low prices from previous days during economic uncertainties. Through our model and findings, we contribute critical insights to research and practice, especially in overcoming the challenges of the “black box” nature of machine learning models. Moreover, practitioners such as investors and regulators can utilize our model to efficiently capture changes in different cryptocurrencies’ price trends toward improved decision-making during economic uncertainty.